Schema 39 documentation

Generated from: scenario_39.xsd.

Key:

  abc           required (one)
[ def ]         optional (zero or one)
( ghi )*        any number (zero or more)
( jkl )+        at least one
( mno ){2,inf}  two or more occurrences

Scenario

→ scenario

<scenario
    schemaVersion=int
  [ analysisNo=int ]
    name=string
  [ wuID=int ]
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:om="http://openmalaria.org/schema/scenario_39"
    xsi:schemaLocation="http://openmalaria.org/schema/scenario_39 scenario_39.xsd"
  >
IN ANY ORDER:
|   <demography ... /> 
|   <monitoring ... /> 
|   <interventions ... /> 
|   <healthSystem ... /> 
|   <entomology ... /> 
| [ <parasiteGenetics ... /> ]
| [ <pharmacology ... /> ]
| [ <diagnostics ... /> ]
|   <model ... /> 
</scenario>

demography
monitoring
interventions
healthSystem
entomology
parasiteGenetics
pharmacology
diagnostics
model

Attributes

Version of the xml schema

schemaVersion=int

Version of xml schema. If not equal to the current version an error is thrown. Use SchemaTranslator to update xml files.

Reference number of the analysis

analysisNo=int

Units: Number Min: 1 Max: 100000000

Unique identifier of scenario

Work unit identifier

wuID=int

Units: Number

Work unit ID. Obselete and no longer required.

Human age distribution

→ scenario → demography

<demography
    name=string
    popSize=int
    maximumAgeYrs=double
  [ growthRate=double ]
  >
IN THIS ORDER:
|   <ageGroup ... /> 
</demography>

ageGroup

Attributes

Population size

popSize=int

Units: Count Min: 1 Max: 100000

Population size

Maximum age of simulated humans

maximumAgeYrs=double

Units: Years Min: 0 Max: 100

Maximum age of simulated humans in years

Growth rate of human population

growthRate=double

Units: Number Min: 0 Max: 0

Growth rate of human population. (we should be able to implement this with non-zero values)

Age groups

→ scenario → demography → ageGroup

<ageGroup
    lowerbound=double
  >
IN THIS ORDER:
| ( <group ... /> )+
</ageGroup>

group

Documentation (element)

list of age groups included in demography

Documentation (type)

list of age groups included in demography or surveys

Attributes

Lower bound of age group

lowerbound=double

Units: Years Min: 0 Max: 100

Lower bound of age group

group

→ scenario → demography → ageGroup → group

<group
    poppercent=double
    upperbound=double
  />

Attributes

Percentage in age group

poppercent=double

Units: Percentage Min: 0 Max: 100

Percentage of human population in age group

Upper bound of age group

upperbound=double

Units: Years Min: 0 Max: 100

Upper bound of age group

Measures to be reported

→ scenario → monitoring

<monitoring
    name=string
  [ startDate=string ]
  >
IN THIS ORDER:
| [ <continuous ... /> ]
|   <SurveyOptions ... /> 
|   <surveys ... /> 
|   <ageGroup ... /> 
| [ <cohorts ... /> ]
</monitoring>

continuous
SurveyOptions
surveys
ageGroup
cohorts

Attributes

Start of monitoring

startDate=string

An optional date for the start of monitoring. If given, dates may be used to specify when other events (surveys, intervention deployments) occur; alternately times relative to the start of the intervention period may be used to specify event times. Setting this to 1st January of some year might simplify usage of dates, and putting the start a couple of years before the start of intervention deployment (along with some extra surveys) may be useful to check transmission stabilises to the expected pre-intervention levels. As an example, if this date is set to 2000-01-01, then the following event times are equivalent (assuming 1t=5d): 15t, 75d, 0.2y, 2000-03-16. Must be in the form YYYY-MM-DD, e.g. 2003-01-01.

continuous

→ scenario → monitoring → continuous

<continuous
    period=string
  [ duringInit=boolean ]
  >
IN THIS ORDER:
| ( <option ... /> )*
</continuous>

option

Attributes

Delay between reports

period=string

Units: User defined (default: steps)

Delay between reports; typically one time step but can be greater. Can be specified in steps (e.g. 1t) or days (e.g. 5d).

During initialization

duringInit=boolean

Units: Days Min: 1 Max: unbounded

Also output during initialization. By default this is disabled (only intervention-period data is output). This should not be used for predictions, but can be useful for model validation. In this mode, 'simulation time' is output as the first column (in addition to 'timestep'), since 'timestep' is dis- continuous across the start of the intervention period.

option

→ scenario → monitoring → continuous → option

<option
    name=string
  [ value=boolean ] DEFAULT VALUE true
  />

Attributes

Option name

name=string

Name of an option (monitoring measure or model option).

Indicator of whether option is required

value=boolean

Default value: true

Option on/off switch (true/false). Specifying value="true" is the same as not specifying a value; specifying value="false" explicitly turns the option off. If an option is not mentioned at all, it is left at its default value (normally off, but in a few cases, such as some bug-fix options, on).

Name of quantity

→ scenario → monitoring → SurveyOptions

<SurveyOptions
  [ onlyNewEpisode=boolean ] DEFAULT VALUE false
  >
IN THIS ORDER:
| ( <option ... /> )*
</SurveyOptions>

option

Documentation (element)

List of all active survey options. See model/mon/OutputMeasures.h for a list of supported outputs. Should also be on the wiki.

Attributes

Report only for new cases

onlyNewEpisode=boolean

Default value: false

If set, some statistics exclude humans who have been treated in the recent past (precisely, when the time of last treatment was before the current step and no more than health-system-memory days/steps ago). This is a rough replacement for the REPORT_ONLY_AT_RISK option, with one difference: the maximum age of treatment for REPORT_ONLY_AT_RISK was fixed at 20 days. Affected measures include (as of version 35): nHost (0), nInfect(1), nExpectd (2), nPatent (3), sumLogPyrogenThres (4), sumlogDens (5), totalInfs (6), totalPatentInf (8), sumPyrogenThresh (10), nSubPopRemovalFirstEvent (62), sumAge (68), nInfectByGenotype (69), nPatentByGenotype (70), logDensByGenotype (71), nHostDrugConcNonZero (72), sumLogDrugConcNonZero (73).

option

→ scenario → monitoring → SurveyOptions → option

<option
    name=string
  [ value=boolean ] DEFAULT VALUE true
  [ outputNumber=int ]
  [ byAge=boolean ]
  [ byCohort=boolean ]
  [ bySpecies=boolean ]
  [ byGenotype=boolean ]
  [ byDrugType=boolean ]
  />

Attributes

Option name

name=string

Name of an option (monitoring measure or model option).

Indicator of whether option is required

value=boolean

Default value: true

Number identifying measure in output

outputNumber=int

Number identifying this monitoring measure in the output file (3rd column). Normally this is determined from the measure, but it can be set manually, e.g. for when the same measure is recorded twice (to accumulate across different categories).

Report by age category

byAge=boolean

If true, the measure is reported for each age category. If false, values are summed across all age categories and only the sum reported. If not specified, separate categories will be reported if the measure supports this.

Report by cohort

byCohort=boolean

If true, the measure is reported for each cohort separately. If false, values are summed across all cohorts and only the sum reported. If not specified, separate categories will be reported if the measure supports this.

Report by mosquito species

bySpecies=boolean

If true, the measure is reported for each mosquito species separately. If false, values are summed across all species and only the sum reported. If not specified, separate categories will be reported if the measure supports this.

Report by parasite genotype

byGenotype=boolean

If true, the measure is reported for each parasite genotype separately. If false, values are summed across all genotypes and only the sum reported. If not specified, separate categories will be reported if the measure supports this.

Report by drug type

byDrugType=boolean

If true, the measure is reported for each drug type separately. If false, values are summed across all drug types and only the sum reported. If not specified, separate categories will be reported if the measure supports this.

Survey times (time steps)

→ scenario → monitoring → surveys

<surveys
  [ detectionLimit=double ]
  [ diagnostic=string ]
  >
IN THIS ORDER:
| ( <surveyTime ... /> )+
</surveys>

surveyTime

Documentation (element)

List of survey times

Attributes

Detection limit for parasitaemia

detectionLimit=double

Units: parasites/microlitre Min: 0

Deprecated: limit above which a human's infection is reported as patent. Alternative: do not specify this; instead specify "diagnostic".

Name of monitoring diagnostic

diagnostic=string

Name of a parameterised diagnostic to use in surveys (see scenario/diagnostics).

Survey time

→ scenario → monitoring → surveys → surveyTime

<surveyTime
  [ repeatStep=string ]
  [ repeatEnd=string ]
  [ reported=boolean ] DEFAULT VALUE true
  >
    string
</surveyTime>

Documentation (element)

Units: User defined (defaults to steps) Min: 0

Time of a survey. A report will be made for those measures enabled under SurveyOptions. Reported data is either from the moment the survey is done (immediate data) or is collected over the time since the previous survey, or in some cases over a fixed time span (usually one year).

Times can be specified in time steps, starting from 0, or as a date (see monitoring/startDate), or in days (e.g. 15d) or years (e.g. 1y). Relative times mean the time since the start of the intervention period, and must be non-negative (zero is valid, but some measures, e.g. nUncomp, will be zero).

The simulation ends immediately after the last survey is taken.

Attributes

Step of repetition

repeatStep=string

Units: User defined

See repeatEnd's documentation.

End of repetition (exclusive)

repeatEnd=string

Units: User defined

Either both repeatStep and repeatEnd should be present or neither. If present, the survey is repeated every repeatStep timesteps (i.e. if t0 is the initial time and x is repeatStep, surveys are done at times t0, t0+x, t0+2*x, ...), ending before repeatEnd (final repetition is the one before repeatEnd). Note that repeatEnd may be specified as a date but repeatStep must be a duration (days, steps or years).

reported

reported=boolean

Default value: true

For normal surveys, reporting=true. If set false, quantities are measured but not reported. The reason for doing this is to update conditions set on reportable measures. Multiple surveys may be given here for the same date, e.g. if using "repeatStep" for both reporting and non-reporting surveys. These are combined such that a maximum of one survey is carried out per time-step, and the survey is reported if any of the listed surveys for this date is configured as "reporting". Note that adding non-reporting surveys will not affect value output by reported surveys, with the exception that generated psuedo-random numbers may be altered (specifically, when any stochastic diagnostics are used in surveys).

Age groups

→ scenario → monitoring → ageGroup

<ageGroup
    lowerbound=double
  >
IN THIS ORDER:
| ( <group ... /> )+
</ageGroup>

group

Documentation (element)

List of age groups included in demography or surveys

Attributes

lower bound of age group

lowerbound=double

Units: Years Min: 0 Max: 100

Lower bound of age group

group

→ scenario → monitoring → ageGroup → group

<group
    upperbound=double
  />

Attributes

upper bound of age group

upperbound=double

Units: Years Min: 0 Max: 100

Upper bound of age group

Cohorts

→ scenario → monitoring → cohorts

<cohorts>
IN THIS ORDER:
| ( <subPop ... /> )+
</cohorts>

subPop

Documentation (element)

Allows the configuration of multiple cohorts (output segregated according to membership within specific sub-populations).

If this element is omitted, monitoring surveys cover the entire simulated human population.

It does not affect the "continuous" outputs (these never take cohorts into account).

Sub-population

→ scenario → monitoring → cohorts → subPop

<subPop
    id=string
    number=integer
  />

Documentation (element)

Consider a certain sup-population a cohort, and segregate outputs according to membership. Where multiple sub-populations are listed, segregate output according to all combinations of membership: e.g. if sub-populations A and B are listed, there will be outputs for "member of A and B", "member of A but not B", "B but not A" and "not a member of A or B". Listing n sub-populations implies 2^n sets of outputs (each is further segregated by age groups, survey times and enabled output measures, which could lead to excessive program memory usage and output file size).

To identify outputs, each sub-population has a power of two number as identifier (see "number" attribute). Each of the 2^n output sets is identified by a number: the output set is the output from humans who are members in some set of sub-populations (S1, S2, ...) and not members in some others (T1, T2, ...); the number identifying the set is the sum of the numbers identifying the sets S1, S2, etc.

In the output file, the output set is identified by multiplying this number by 1000 then adding it to the age group column.

Attributes

Sub-population identifier

id=string

Textual identifier for the sub-population (i.e. for an intervention component, since sub-populations are defined as the hosts an intervention component is deployed to).

Sub-population number

number=integer

Units: dimensionless Min: 1 Max: 2097152

Number identifying a sub-population; used to define identifiers of output sets. This number must be a power of 2 (i.e. 1, 2, 4, 8, ...). See documentation of subPop element.

Preventative interventions

→ scenario → interventions

<interventions
    name=string
  >
IN ANY ORDER:
| [ <changeHS ... /> ]
| [ <changeEIR ... /> ]
| [ <importedInfections ... /> ]
| [ <insertR_0Case ... /> ]
| [ <uninfectVectors ... /> ]
| [ <vectorPop ... /> ]
| [ <vectorTrap ... /> ]
| [ <human ... /> ]
</interventions>

changeHS
changeEIR
importedInfections
insertR_0Case
uninfectVectors
vectorPop
vectorTrap
human

Documentation (element)

List of interventions. Generally these are either point-time distributions of something to some subset of the population, or continuous-time distribution targetting individuals when they reach a certain age.

Attributes

Name of intervention set

name=string

Name of set of interventions

Change health system

→ scenario → interventions → changeHS

<changeHS
  [ name=string ]
  >
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</changeHS>

timedDeployment

Documentation (element)

Changes to the health system

Attributes

Name of intervention

name=string

Name of intervention

timedDeployment

→ scenario → interventions → changeHS → timedDeployment

<timedDeployment
    time=string
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <EventScheduler ... /> 
| |   <ImmediateOutcomes ... /> 
| |   <DecisionTree5Day ... /> 
|   <CFR ... /> 
|   <pSequelaeInpatient ... /> 
</timedDeployment>

EventScheduler
ImmediateOutcomes
DecisionTree5Day
CFR
pSequelaeInpatient

Documentation (type)

A complete replacement health system. Replaces all previous properties. (Health system can be replaced multiple times if necessary.)

Documentation (base type)

Description of case management system, used to specify the initial model or a replacement (an intervention). Encompasses case management data and some other data required to derive case outcomes.

Contains a sub-element describing the particular health-system in use. Health system data is here defined as data used to decide on a treatment strategy, given a case requiring treatment.

Attributes

Time

time=string

Units: User defined (defauls to steps) Min: 0

Time at which this replacement occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

EventScheduler

→ scenario → healthSystem → EventScheduler

<EventScheduler>
IN THIS ORDER:
|   <uncomplicated ... /> 
|   <complicated ... /> 
|   <ClinicalOutcomes ... /> 
| [ <NonMalariaFevers ... /> ]
</EventScheduler>

uncomplicated
complicated
ClinicalOutcomes
NonMalariaFevers

uncomplicated

→ scenario → healthSystem → EventScheduler → uncomplicated

<uncomplicated
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</uncomplicated>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

multiple

→ scenario → interventions → human → component → decisionTree → multiple

<multiple
  [ name=string ]
  >
IN THIS ORDER:
| ( <caseType ... /> )*
| ( <diagnostic ... /> )*
| ( <random ... /> )*
| ( <age ... /> )*
| ( <treatPKPD ... /> )*
| [ <treatSimple ... /> ]
| ( <deploy ... /> )*
</multiple>

caseType
diagnostic
random
age
treatPKPD
treatSimple
deploy

Documentation (type)

A special node allowing multiple sub-trees to be evaluated.

This is different from an ordinary decision tree node in that:

a) multiple types of child can occur simultaneously (e.g. multiple types of treatment or treatment plus a 'random' sub-tree)

b) the 'noTreatment' and 'treatFailure' nodes are not allowed

Attributes

Name

name=string

An optional piece of documentation attached to this node.

caseType

→ scenario → interventions → human → component → decisionTree → multiple → caseType

<caseType
  [ name=string ]
  >
IN ANY ORDER:
|   <firstLine ... /> 
|   <secondLine ... /> 
</caseType>

firstLine
secondLine

Documentation (type)

A switch which choses a branch deterministically, based on whether the patient was treated recently (second line) or not (first line).

For uncomplicated cases only.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

firstLine

→ scenario → interventions → human → component → decisionTree → multiple → caseType → firstLine

<firstLine
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</firstLine>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

caseType

→ scenario → interventions → human → component → decisionTree → caseType

<caseType
  [ name=string ]
  >
IN ANY ORDER:
|   <firstLine ... /> 
|   <secondLine ... /> 
</caseType>

firstLine
secondLine

Documentation (type)

A switch which choses a branch deterministically, based on whether the patient was treated recently (second line) or not (first line).

For uncomplicated cases only.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

secondLine

→ scenario → interventions → human → component → decisionTree → multiple → caseType → secondLine

<secondLine
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</secondLine>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

diagnostic

→ scenario → interventions → human → component → decisionTree → diagnostic

<diagnostic
    diagnostic=string
  [ name=string ]
  >
IN ANY ORDER:
|   <positive ... /> 
|   <negative ... /> 
</diagnostic>

positive
negative

Documentation (type)

A switch which choses a branch deterministically, based on the outcome of some type of diagnostic.

Attributes

Name of diagnostic

diagnostic=string

Should match the name of some parameterised diagnostic (see scenario/diagnostics).

Name

name=string

An optional piece of documentation attached to this node.

positive

→ scenario → interventions → human → component → decisionTree → multiple → diagnostic → positive

<positive
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</positive>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

random

→ scenario → interventions → human → component → decisionTree → random

<random
  [ name=string ]
  >
IN THIS ORDER:
| ( <outcome ... /> )+
</random>

outcome

Documentation (type)

A switch which choses a branch randomly.

Each branch must be listed with a probability; the sum of all these probabilities must equal 1.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

outcome

→ scenario → interventions → human → component → decisionTree → multiple → random → outcome

<outcome
  [ name=string ]
    p=double
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</outcome>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (base type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

Probability

p=double

Units: None Min: 0 Max: 1

Probability of selecting this outcome. The sum of probabilities across all outcomes must be 1.

age

→ scenario → interventions → human → component → decisionTree → age

<age
  [ name=string ]
  >
IN THIS ORDER:
| ( <age ... /> )+
</age>

Documentation (type)

A switch which choses a branch deterministically, based on the patient's age (in years).

Categories must uniquely cover all ages from birth, with no upper bound. Categories must be listed in order of age, increasing; the first must have lower bound 0. Upper bounds are equal to the lower bound of the next category, (but are exclusive where lower bounds are inclusive); the last category has no upper bound.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

Age range

→ scenario → interventions → human → component → decisionTree → multiple → age → age

<age
  [ name=string ]
    lb=double
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</age>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (element)

Describes a branch, selected for patients of a certain age.

Documentation (base type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

Lower bound (inclusive)

lb=double

Min: 0

noTreatment

→ scenario → interventions → human → component → decisionTree → noTreatment

<noTreatment
  [ name=string ]
  />

Documentation (type)

An end node doing nothing. This exists to explicitly state that no treatment happens and to prevent trees from accidentally being left incomplete.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

treatFailure

→ scenario → interventions → human → component → decisionTree → treatFailure

<treatFailure
  [ name=string ]
  />

Documentation (type)

An end node which reports treatment but does not change parasitalogical status. This allows correct labelling of second-line cases.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

treatPKPD

→ scenario → interventions → human → component → decisionTree → treatPKPD

<treatPKPD
    schedule=string
    dosage=string
  [ delay_h=double ] DEFAULT VALUE 0
  />

Documentation (type)

A command to administer drugs according to a given schedule and dosage table, optionally with a delay.

Attributes

Name of treatment schedule

schedule=string

The name of a schedule to use for treatment.

Name of dosage table

dosage=string

The name of a dosage table to use for treatment.

Delay (hours)

delay_h=double

Default value: 0

Optionally, this can be given to delay the start of treatment by a given number of hours. If not specified, treatment is not delayed. If a delay is given, all medications within the treatment schedule used are delayed by this number of hours.

treatSimple

→ scenario → interventions → human → component → decisionTree → treatSimple

<treatSimple
    durationLiver=string
    durationBlood=string
  />

Documentation (type)

Simple treatment model, targetting liver- and/or blood-stage infections. This is all-or-nothing treatment which, when deploymed, completely clears all infections of the targetted stages. This makes it unsuitable for modeling resistance, but suitable for use with simple infection models.

Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.

Attributes

Length of liver-stage effect

durationLiver=string

Units: User defined

Controls action on liver-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all liver-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).

Length of blood-stage effect

durationBlood=string

Units: User defined

Controls action on blood-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all blood-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).

deploy

→ scenario → interventions → human → component → decisionTree → deploy

<deploy
    component=string
  />

Documentation (type)

Deploy one or more intervention components.

Attributes

Component identifier

component=string

The identifier (short name) of a component.

negative

→ scenario → interventions → human → component → decisionTree → multiple → diagnostic → negative

<negative
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</negative>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

diagnostic

→ scenario → interventions → human → component → decisionTree → multiple → diagnostic

<diagnostic
    diagnostic=string
  [ name=string ]
  >
IN ANY ORDER:
|   <positive ... /> 
|   <negative ... /> 
</diagnostic>

positive
negative

Documentation (type)

A switch which choses a branch deterministically, based on the outcome of some type of diagnostic.

Attributes

Name of diagnostic

diagnostic=string

Should match the name of some parameterised diagnostic (see scenario/diagnostics).

Name

name=string

An optional piece of documentation attached to this node.

random

→ scenario → interventions → human → component → decisionTree → multiple → random

<random
  [ name=string ]
  >
IN THIS ORDER:
| ( <outcome ... /> )+
</random>

outcome

Documentation (type)

A switch which choses a branch randomly.

Each branch must be listed with a probability; the sum of all these probabilities must equal 1.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

age

→ scenario → interventions → human → component → decisionTree → multiple → age

<age
  [ name=string ]
  >
IN THIS ORDER:
| ( <age ... /> )+
</age>

Documentation (type)

A switch which choses a branch deterministically, based on the patient's age (in years).

Attributes

Name

name=string

An optional piece of documentation attached to this node.

treatPKPD

→ scenario → interventions → human → component → decisionTree → multiple → treatPKPD

<treatPKPD
    schedule=string
    dosage=string
  [ delay_h=double ] DEFAULT VALUE 0
  />

Documentation (type)

A command to administer drugs according to a given schedule and dosage table, optionally with a delay.

Attributes

Name of treatment schedule

schedule=string

The name of a schedule to use for treatment.

Name of dosage table

dosage=string

The name of a dosage table to use for treatment.

Delay (hours)

delay_h=double

Default value: 0

treatSimple

→ scenario → interventions → human → component → decisionTree → multiple → treatSimple

<treatSimple
    durationLiver=string
    durationBlood=string
  />

Documentation (type)

Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.

Attributes

Length of liver-stage effect

durationLiver=string

Units: User defined

Length of blood-stage effect

durationBlood=string

Units: User defined

deploy

→ scenario → interventions → human → component → decisionTree → multiple → deploy

<deploy
    component=string
  />

Documentation (type)

Deploy one or more intervention components.

Attributes

Component identifier

component=string

The identifier (short name) of a component.

complicated

→ scenario → healthSystem → EventScheduler → complicated

<complicated
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</complicated>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

ClinicalOutcomes

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes

<ClinicalOutcomes>
IN THIS ORDER:
|   <maxUCSeekingMemory ... /> 
|   <uncomplicatedCaseDuration ... /> 
|   <complicatedCaseDuration ... /> 
|   <complicatedRiskDuration ... /> 
| ( <dailyPrImmUCTS ... /> )+
</ClinicalOutcomes>

maxUCSeekingMemory
uncomplicatedCaseDuration
complicatedCaseDuration
complicatedRiskDuration
dailyPrImmUCTS

Documentation (type)

Description of base parameters of the clinical model.

Max UC treatment-seeking memory

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → maxUCSeekingMemory

<maxUCSeekingMemory>
    int
</maxUCSeekingMemory>

Documentation (element)

Units: Days Min: 0 Max: unbounded

Maximum number of timesteps (including first day of case) that an individual with an uncomplicated case of malaria will remember he/she was sick before resetting.

Uncomplicated case duration

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → uncomplicatedCaseDuration

<uncomplicatedCaseDuration>
    int
</uncomplicatedCaseDuration>

Documentation (element)

Units: Days Min: 1 Max: unbounded

Fixed length of an uncomplicated case of malarial or non-malarial sickness (from treatment seeking until return to life-as-usual). Usually 3.

Complicated case duration

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedCaseDuration

<complicatedCaseDuration>
    int
</complicatedCaseDuration>

Documentation (element)

Units: Days Min: 1 Max: unbounded

Fixed length of a complicated or severe case of malaria (from treatment seeking until return to life-as-usual).

Complicated risk duration

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedRiskDuration

<complicatedRiskDuration>
    int
</complicatedRiskDuration>

Documentation (element)

Units: Days Min: 1 Max: unbounded

Number of days for which humans are at risk of death during a severe or complicated case of malaria. Cannot be greater than the duration of a complicated case or less than 1 day.

Daily probability of immediate treatment seeking for uncomplicated cases

→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → dailyPrImmUCTS

<dailyPrImmUCTS>
    double
</dailyPrImmUCTS>

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

It is sometimes desirable to model delays to treatment-seeking in uncomplicated cases. While treatment of drugs can be delayed within case management trees to provide a similar effect, this doesn't delay any of the decisions, including diagnostics using the current parasite density.

Instead a list of dailyPrImmUCTS elements can be used, describing successive daily probabilities of treatment (sum must be 1). For example, with a list of two elements with values 0.8 and 0.2, for 80% of UC cases the decision tree is evaluated immediately, and for 20% of cases evaluation is delayed by one day.

For no delay, use one element with a value of 1.

NonMalariaFevers

→ scenario → healthSystem → EventScheduler → NonMalariaFevers

<NonMalariaFevers>
IN THIS ORDER:
|   <prTreatment ... /> 
|   <effectNegativeTest ... /> 
|   <effectPositiveTest ... /> 
|   <effectNeed ... /> 
|   <effectInformal ... /> 
|   <CFR ... /> 
|   <TreatmentEfficacy ... /> 
</NonMalariaFevers>

prTreatment
effectNegativeTest
effectPositiveTest
effectNeed
effectInformal
CFR
TreatmentEfficacy

Documentation (type)

Description of non-malaria fever health-system modelling (treatment, outcomes and costing). Incidence is described by the model->clinical->NonMalariaFevers element. Non-malaria fevers are only modelled if the NON_MALARIA_FEVERS option is used.

As further explanation of the parameters below, we first take: β₀ = logit(P₀) - β₃·P(need), and then calculate the probability of antibiotic administration, P(AB), dependent on treatment seeking location. No seeking: P(AB) = 0 Informal sector: logit(P(AB)) = β₀ + β₄ Health facility: logit(P(AB)) = β₀ + β₁·I(neg) + β₂·I(pos) + β₃·I(need) (where I(X) is 1 when event X is true and 0 otherwise, logit(p)=log(p/(1-p)), event "need" is the event that death may occur without treatment, events "neg" and "pos" are the events that a malaria parasite diagnositic was used and indicated no parasites and parasites respectively).

P(treatment|no diagnostic)

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → prTreatment

<prTreatment>
    double
</prTreatment>

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability of a non-malaria fever being treated with an antibiotic given that no malaria diagnostic was used but independent of need. Symbol: P₀.

Effect of a negative test

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNegativeTest

<effectNegativeTest>
    double
</effectNegativeTest>

Documentation (element)

The effect of a negative malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₁).

Effect of a positive test

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectPositiveTest

<effectPositiveTest>
    double
</effectPositiveTest>

Documentation (element)

The effect of a positive malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₂).

Effect of need

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNeed

<effectNeed>
    double
</effectNeed>

Documentation (element)

The effect of needing antibiotic treatment on the odds ratio of receiving antibiotics. Symbol: exp(β₃).

Effect of informal provider

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectInformal

<effectInformal>
    double
</effectInformal>

Documentation (element)

The effect of seeking treatment from an informal provider (i.e. a provider untrained in NMF diagnosis) on the odds ratio of receiving antibiotics. Symbol: exp(β₄)

Case fatality rate

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → CFR

<CFR
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</CFR>

group

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Base case fatality rate for non-malaria fevers (probability of death from a fever requiring antibiotic treatment given that no antibiotic treatment is received, per age-group).

Attributes

interpolation

interpolation=("none" or "linear")

Interpolation algorithm. Normally it is desirable for age-based values to be continuous w.r.t. age. By default linear interpolation is used. With all algorithms except "none", the age groups are converted to a set of points centred within each age range. Extra points are added at each end (zero and infinity) to keep value constant at both ends of the function. A zero-length age group may be used as a kind of barrier to adjust the distribution; e.g. with age group boundaries at 15, 20 and 25 years, a (linear) spline would be drawn between ages 17.5 and 22.5, whereas with boundaries at 15, 20 and 20 years, a spline would be drawn between ages 17.5 and 20 years (may be desired if individuals are assumed to reach adult size at 20). Algorithms:

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

age group

→ scenario → healthSystem → CFR → group

<group
    value=double
    lowerbound=double
  />

Documentation (element)

A series of values according to age groups, each specified with a lower-bound and a value. The first lower-bound specified must be zero; a final upper-bound of infinity is added to complete the last age group. At least one age group is required. Normally these are interpolated by a continuous function (see interpolation attribute).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Lower bound

lowerbound=double

Units: Years Min: 0 Max: 100

Lower bound of age group

Treatment efficacy

→ scenario → healthSystem → EventScheduler → NonMalariaFevers → TreatmentEfficacy

<TreatmentEfficacy>
    double
</TreatmentEfficacy>

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that treatment would prevent a death (i.e. CFR is multiplied by one minus this when treatment occurs).

ImmediateOutcomes

→ scenario → healthSystem → ImmediateOutcomes

<ImmediateOutcomes
  [ name=string ]
  [ useDiagnosticUC=boolean ] DEFAULT VALUE false
  >
IN ANY ORDER:
|   <drugRegimen ... /> 
|   <initialACR ... /> 
|   <compliance ... /> 
|   <nonCompliersEffective ... /> 
|   <treatmentActions ... /> 
|   <pSeekOfficialCareUncomplicated1 ... /> 
|   <pSelfTreatUncomplicated ... /> 
|   <pSeekOfficialCareUncomplicated2 ... /> 
|   <pSeekOfficialCareSevere ... /> 
| [ <liverStageDrug ... /> ]
</ImmediateOutcomes>

drugRegimen
initialACR
compliance
nonCompliersEffective
treatmentActions
pSeekOfficialCareUncomplicated1
pSelfTreatUncomplicated
pSeekOfficialCareUncomplicated2
pSeekOfficialCareSevere
liverStageDrug

Documentation (type)

Description of "immediate outcomes" health system: Tediosi et al case management model (Case management as described in AJTMH 75 (suppl 2) pp90-103).

Attributes

Name of case management parameterisation

name=string

Name of health system

useDiagnosticUC

useDiagnosticUC=boolean

Default value: false

Description of drug regimen

→ scenario → healthSystem → ImmediateOutcomes → drugRegimen

<drugRegimen
    firstLine=string
    secondLine=string
    inpatient=string
  />

Documentation (element)

Description of drug regimen.

Attributes

First line drug

firstLine=string

Units: Drug code

Code for first line drug

Second line drug

secondLine=string

Units: Drug code

Code for second line drug

Drug use for treating inpatients

inpatient=string

Units: Drug code

Code for drug used for treating inpatients

Initial cure rate

→ scenario → healthSystem → ImmediateOutcomes → initialACR

<initialACR>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
|   <selfTreatment ... /> 
</initialACR>

CQ
SP
AQ
SPAQ
ACT
QN
selfTreatment

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Initial cure rate

Chloroquine

→ scenario → healthSystem → ImmediateOutcomes → initialACR → CQ

<CQ
    value=double
  />

Documentation (element)

Chloroquine

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Sulphadoxine-pyrimethamine

→ scenario → healthSystem → ImmediateOutcomes → initialACR → SP

<SP
    value=double
  />

Documentation (element)

Sulphadoxine-pyrimethamine

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Amodiaquine

→ scenario → healthSystem → ImmediateOutcomes → initialACR → AQ

<AQ
    value=double
  />

Documentation (element)

Amodiaquine

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Sulphadoxine-pyrimethamine/Amodiaquine

→ scenario → healthSystem → ImmediateOutcomes → initialACR → SPAQ

<SPAQ
    value=double
  />

Documentation (element)

Sulphadoxine-pyrimethamine/Amodiaquine

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Artemisinine based combination therapy

→ scenario → healthSystem → ImmediateOutcomes → initialACR → ACT

<ACT
    value=double
  />

Documentation (element)

Artemisinine combination therapy

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Quinine

→ scenario → healthSystem → ImmediateOutcomes → initialACR → QN

<QN
    value=double
  />

Documentation (element)

Quinine

Attributes

Input parameter value

value=double

A double-precision floating-point value.

selfTreatment

→ scenario → healthSystem → ImmediateOutcomes → initialACR → selfTreatment

<selfTreatment
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0 Max: 1name:P(self-treat)

Probability of self-treatment

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Adherence to treatment

→ scenario → healthSystem → ImmediateOutcomes → compliance

<compliance>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
|   <selfTreatment ... /> 
</compliance>

CQ
SP
AQ
SPAQ
ACT
QN
selfTreatment

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Adherence to treatment

Effectiveness of treatment in non-adherent patients

→ scenario → healthSystem → ImmediateOutcomes → nonCompliersEffective

<nonCompliersEffective>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
|   <selfTreatment ... /> 
</nonCompliersEffective>

CQ
SP
AQ
SPAQ
ACT
QN
selfTreatment

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Effectiveness of treatment for non-compliant patients

treatmentActions

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions

<treatmentActions>
IN ANY ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
</treatmentActions>

CQ
SP
AQ
SPAQ
ACT
QN

CQ

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ

<CQ
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</CQ>

deploy
clearInfections

Documentation (type)

Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

deploy

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ → deploy

<deploy
  [ maxAge=double ]
  [ minAge=double ] DEFAULT VALUE 0
  [ p=double ] DEFAULT VALUE 1
  >
IN THIS ORDER:
| ( <component ... /> )+
</deploy>

component

Attributes

Maximum age of eligible humans

maxAge=double

Units: Years Min: 0

Maximum age of eligible humans (defaults to no limit). Input is rounded to the nearest time step.

Minimum age of eligible humans

minAge=double

Units: Years Min: 0

Default value: 0

Minimum age of eligible humans (defaults to 0). Input is rounded to the nearest time step.

Probability of delivery to eligible humans

p=double

Units: dimensionless Min: 0 Max: 1

Default value: 1

Probability of this list of components being deployed, given that other constraints are met.

component

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ → deploy → component

<component
    id=string
  />

Documentation (type)

The list of components deployed to eligible humans.

Attributes

Identifier

id=string

The identifier (short name) of a component.

Prophylactic treatment

→ scenario → healthSystem → DecisionTree5Day → treatmentSevere → clearInfections

<clearInfections
    timesteps=string
    stage=("liver" or "blood" or "both")
  />

Documentation (element)

This clears infections according to several options: it can clear all blood stage infections, all liver stage infections or both, and it can act on multiple timesteps. To have a probability of no action add another treatment option (which does nothing) and set the probabilities of selection appropriately.

This allows immediate (legacy) or delayed action, a prophylactic period, and selection of which stages are targeted. It is a simple model but appropriate enough for use with the five day timestep when assuming no resistance and that drug failure is mainly caused by bad drugs or compliance.

The old treatment action for the five-day timestep model is essentially this, with immediateAction (timesteps=-1) and stage=both, except for the IPT model's SP action, which was more like with timesteps>1 and stage=blood.

Attributes

Length of effect

timesteps=string

Units: User defined (defaults to steps)

The number of timesteps during which this action remains in effect (e.g. 2 means clear infections during the next two timestep updates). Full clearance of the targeted stages occurs during this time. A special value of -1 means act immediately (retrospectively); this the old behaviour. A value of 1 means act on the next timestep only. Both of these can be thought of as a model for short-acting effective drug treatment; the main differences are that the latter means parasite densities will remain high from the point of view of surveys and diagnostics (i.e. mass screen and treat) used before the next timestep and that the latter will also remove infections starting the next timestep. Arguably the latter is a better model, but the differences are perhaps small, excepting where immediate treatment of fevers (i.e. through the health system) can hide high parasite densities from reporting and mass-screen-and-treat diagnostics. For use by interventions, the latter model has nicer behaviour in that the order of deployment of multiple interventions deployed at the same time does not matter, and that the former model retrospectively treats infections which may already have caused fever, thus may have a lower health impact than it should. It is recommended to use the new model (value 1, or greater than 1 if prophylactic effect is desired) unless wanting to emulate the old behaviour. Values of 0 or less than -1 are not allowed. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).

Target stage

stage=("liver" or "blood" or "both")

Controls whether liver-stage or blood-stage infections are cleared, or both. Infections are considered liver-stage for one 5-day timestep, blood-stage but pre-patent for some number of timesteps (latentp - 1), then start the patent blood stage. If stage is set to "liver", infections are only cleared during their first timestep; if stage is set to "blood", infections are cleared during pre-patent and patent blood stages; if stage is set to "both" all infections are cleared. The old behaviour (oddly considering the drugs it is meant to emulate) is to clear both stages, except for the IPT model of SP action, which cleared only patent blood-stage infections.

SP

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → SP

<SP
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</SP>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

AQ

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → AQ

<AQ
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</AQ>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

SPAQ

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → SPAQ

<SPAQ
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</SPAQ>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

ACT

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → ACT

<ACT
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</ACT>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

QN

→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → QN

<QN
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</QN>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

Probability that a patient with uncomplicated disease seeks official care immediately.

→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated1

<pSeekOfficialCareUncomplicated1
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with newly incident uncomplicated disease seeks official care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a patient with uncomplicated disease will self-treat.

→ scenario → healthSystem → ImmediateOutcomes → pSelfTreatUncomplicated

<pSelfTreatUncomplicated
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with uncomplicated disease without recent history of disease (i.e. first line) will self-treat.

Note that in second line cases there is no probability of self-treatment.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a recurring patient seeks official care

→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated2

<pSeekOfficialCareUncomplicated2
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with recurrence of uncomplicated disease seeks official care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a patient with severe disease obtains appropriate care

→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareSevere

<pSeekOfficialCareSevere
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with severe disease obtains appropriate care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

liverStageDrug

→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug

<liverStageDrug>
IN ANY ORDER:
|   <pHumanCannotReceive ... /> 
| [ <ignoreCannotReceive ... /> ]
| [ <pUseUncomplicated ... /> ]
|   <effectivenessOnUse ... /> 
</liverStageDrug>

pHumanCannotReceive
ignoreCannotReceive
pUseUncomplicated
effectivenessOnUse

Documentation (type)

Parameters for drug treatment which have an effect on the liver-stage of parasites (Primaquine and potentially Tafenoquine); for use with the Vivax model only.

Note: if this section is not listed, the following default values are assumed: pHumanCannotReceive=0, pUseUncomplicated=0, effectivenessOnUse=1.

Probability that human is incompatible with liver-stage drug treatment

→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → pHumanCannotReceive

<pHumanCannotReceive
    value=double
  />

Documentation (element)

Units: Probability Min: 0 Max: 1

Chance that a human is determined to be unable to receive liver-stage drug treatment. Treatment is neither reported or given for such humans.

This is sampled once per human at birth.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Ignore liver-stage drug treatment incompatibility

→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → ignoreCannotReceive

<ignoreCannotReceive
    value=boolean
  />

Documentation (element)

If true, ignore pHumanCannotReceive and consider all humans eligible for treatment; if false (or not specified), do not treat those demed incompatible with liver-stage drug treatment.

The point of this is that pHumanCannotReceive cannot be altered by changeHS interventions, but this property can be.

Attributes

Input parameter value

value=boolean

A boolean value.

Prob use in UC case

→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → pUseUncomplicated

<pUseUncomplicated
    value=double
  />

Documentation (element)

Units: Probability Min: 0 Max: 1

This feature is deprecated; it is suggested to use the "simple treatment" feature configured to clear liver-stage parasites, leaving this option unset or zero.

Chance of liver-stage drug treatment being used for routine treatment of an uncomplicated case.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Effectiveness

→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → effectivenessOnUse

<effectivenessOnUse
    value=double
  />

Documentation (element)

Units: Probability Min: 0 Max: 1

Chance that liver-stage drug treatment is effective.

On application, a random variable is sampled against this probability. If false, the treatment does nothing; if true, the treatment clears all liver stage parasites. Where effectiveness is longer than a single time step (prophylactic effect), this sample still only happens once (thus either no effect or all liver stages cleared over multiple steps).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

DecisionTree5Day

→ scenario → healthSystem → DecisionTree5Day

<DecisionTree5Day
  [ name=string ]
  >
IN ANY ORDER:
|   <pSeekOfficialCareUncomplicated1 ... /> 
|   <pSelfTreatUncomplicated ... /> 
|   <pSeekOfficialCareUncomplicated2 ... /> 
|   <pSeekOfficialCareSevere ... /> 
| [ <liverStageDrug ... /> ]
|   <treeUCOfficial ... /> 
|   <treeUCSelfTreat ... /> 
|   <cureRateSevere ... /> 
|   <treatmentSevere ... /> 
</DecisionTree5Day>

pSeekOfficialCareUncomplicated1
pSelfTreatUncomplicated
pSeekOfficialCareUncomplicated2
pSeekOfficialCareSevere
liverStageDrug
treeUCOfficial
treeUCSelfTreat
cureRateSevere
treatmentSevere

Documentation (type)

Description of the health system using the 5-day timestep with decision tree model: access is configured as in the Tediosi et al case management model (Case management as described in AJTMH 75 (suppl 2) pp90-103) while treatment decisions are configured via decision trees.

Besides greater flexibility, this allows treatment via PK/PD models.

Attributes

Name of case management parameterisation

name=string

Name of health system

Probability that a patient with uncomplicated disease seeks official care immediately.

→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareUncomplicated1

<pSeekOfficialCareUncomplicated1
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with newly incident uncomplicated disease seeks official care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a patient with uncomplicated disease will self-treat.

→ scenario → healthSystem → DecisionTree5Day → pSelfTreatUncomplicated

<pSelfTreatUncomplicated
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with uncomplicated disease without recent history of disease (i.e. first line) will self-treat.

Note that in second line cases there is no probability of self-treatment.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a recurring patient seeks official care

→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareUncomplicated2

<pSeekOfficialCareUncomplicated2
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with recurrence of uncomplicated disease seeks official care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that a patient with severe disease obtains appropriate care

→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareSevere

<pSeekOfficialCareSevere
    value=double
  />

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a patient with severe disease obtains appropriate care

Attributes

Input parameter value

value=double

A double-precision floating-point value.

liverStageDrug

→ scenario → healthSystem → DecisionTree5Day → liverStageDrug

<liverStageDrug>
IN ANY ORDER:
|   <pHumanCannotReceive ... /> 
| [ <ignoreCannotReceive ... /> ]
| [ <pUseUncomplicated ... /> ]
|   <effectivenessOnUse ... /> 
</liverStageDrug>

pHumanCannotReceive
ignoreCannotReceive
pUseUncomplicated
effectivenessOnUse

Documentation (type)

Parameters for drug treatment which have an effect on the liver-stage of parasites (Primaquine and potentially Tafenoquine); for use with the Vivax model only.

Note: if this section is not listed, the following default values are assumed: pHumanCannotReceive=0, pUseUncomplicated=0, effectivenessOnUse=1.

treeUCOfficial

→ scenario → healthSystem → DecisionTree5Day → treeUCOfficial

<treeUCOfficial
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</treeUCOfficial>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

treeUCSelfTreat

→ scenario → healthSystem → DecisionTree5Day → treeUCSelfTreat

<treeUCSelfTreat
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</treeUCSelfTreat>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

Cure rate (severe cases)

→ scenario → healthSystem → DecisionTree5Day → cureRateSevere

<cureRateSevere
    value=double
  />

Documentation (element)

Min: 0 Max: 1

The probability of clearing parasites given access to appropriate (hospital) care, for a severe case.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

treatmentSevere

→ scenario → healthSystem → DecisionTree5Day → treatmentSevere

<treatmentSevere
  [ name=string ]
  >
IN THIS ORDER:
| ( <deploy ... /> )*
</treatmentSevere>

deploy
clearInfections

Documentation (type)

Documentation (base type)

Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).

Attributes

Name

name=string

Describes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).

Case fatality rate for inpatients

→ scenario → healthSystem → CFR

<CFR
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</CFR>

group

Documentation (element)

Case fatality rate (probability of an inpatient fatality from a bout of severe malaria, per age-group).

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

Probabilities of sequelae in inpatients

→ scenario → healthSystem → pSequelaeInpatient

<pSequelaeInpatient
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</pSequelaeInpatient>

group

Documentation (element)

Units: Dimensionless

List of age-specific probabilities of sequelae in inpatients, during a severe bout of malaria.

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

Change transmission levels

→ scenario → interventions → changeEIR

<changeEIR
  [ name=string ]
  >
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</changeEIR>

timedDeployment

Documentation (element)

New description of transmission level for models not supporting vector control interventions. Use of this overrides previous transmission levels such that human infectiousness no longer has any feedback effect on transmission. Supplied EIR data must last until end of simulation.

Attributes

Name of intervention

name=string

Name of intervention

timedDeployment

→ scenario → interventions → changeEIR → timedDeployment

<timedDeployment
    eipDuration=int
    time=string
  >
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</timedDeployment>

EIRDaily

Documentation (type)

Replacement transmission levels. Disables feedback of human infectiousness to mosquitoes on further mosquito to human transmission. Must last until end of simulation.

Attributes

Duration of sporogony

eipDuration=int

Units: Days

The duration of sporogony in days

Time

time=string

Units: User defined (defauls to steps) Min: 0

EIRDaily

→ scenario → entomology → nonVector → EIRDaily

<EIRDaily
  [ origin=string ]
  >
    double
</EIRDaily>

Documentation (type)

Units: Infectious bites per adult per day

In the non-vector model, EIR is input as a sequence of daily values. There must be at least one years' worth of entries (365), and if there are more, values are wrapped and averaged (i.e. value for first day of year is taken as the mean of values for days 0, 365+0, 2*365+0, etc.).

Attributes

Time origin of EIR sequence

origin=string

Imported infections

→ scenario → interventions → importedInfections

<importedInfections
  [ name=string ]
  >
IN THIS ORDER:
|   <timed ... /> 
</importedInfections>

timed

Documentation (element)

Models importation of P. falciparum infections directly into humans from an external source. This is infections, not inoculations or EIR being imported.

Attributes

Name of intervention

name=string

Name of intervention

Rate of importation

→ scenario → interventions → importedInfections → timed

<timed
  [ period=string ] DEFAULT VALUE 0
  >
IN THIS ORDER:
| ( <rate ... /> )+
</timed>

rate

Documentation (element)

Rate of case importation, as a step function. Each value is valid until replaced by the next value.

Attributes

Period of repetition

period=string

Units: User defined (default: steps) Min: 0

Default value: 0

If period is 0 (or effectively infinite), the last specified value remains indefinitely in effect, otherwise the times of all values specified must be less than the period, and values are repeated modulo period (the step at time 'period+2t' has same value as the step at '2t', etc.). Can be specified in steps (e.g. 1t) or days (e.g. 365d).

rate

→ scenario → interventions → importedInfections → timed → rate

<rate
    value=double
    time=string
  />

Documentation (type)

Units: Imported cases per thousand people per year

A time-rate pair.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Time of start

time=string

Units: User defined (defauls to steps) Min: 0

Time at which this importation rate becomes active. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

Insert R_0 case

→ scenario → interventions → insertR_0Case

<insertR_0Case
  [ name=string ]
  >
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</insertR_0Case>

timedDeployment

Documentation (element)

Used to simulate R_0. First, infections should be eliminated, immunity removed, and the population given an effective transmission- blocking vaccine (not done by this intervention). Then this intervention may be used to: pick one human, infect him, administer a fully effective Preerythrocytic vaccine and remove transmission-blocking vaccine effect on this human. Thus only this one human will be a source of infections in an unprotected population, and will not reinfected himself.

Attributes

Name of intervention

name=string

Name of intervention

timedDeployment

→ scenario → interventions → insertR_0Case → timedDeployment

<timedDeployment
    time=string
  />

Attributes

Time

time=string

Units: User defined (defauls to steps) Min: 0

Time at which this intervention occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

Uninfect vectors

→ scenario → interventions → uninfectVectors

<uninfectVectors
  [ name=string ]
  >
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</uninfectVectors>

timedDeployment

Documentation (element)

Units: List of elements

Removes all infections from mosquitoes -- resulting in zero EIR to humans, until such time that mosquitoes are re-infected and become infectious. Only efficacious in dynamic EIR mode (when changeEIR was not used).

Hypothetical, but potentially useful to simulate a setting starting from no infections, but with enough mosquitoes to reach a set equilibrium of exposure.

Attributes

Name of intervention

name=string

Name of intervention

timedDeployment

→ scenario → interventions → uninfectVectors → timedDeployment

<timedDeployment
    time=string
  />

Attributes

Time

time=string

Units: User defined (defauls to steps) Min: 0

Vector population intervention

→ scenario → interventions → vectorPop

<vectorPop>
IN THIS ORDER:
| ( <intervention ... /> )+
</vectorPop>

intervention

Documentation (element)

Units: List of elements

A list of parameterisations of generic vector host-inspecific interventions.

intervention

→ scenario → interventions → vectorPop → intervention

<intervention
    name=string
  >
IN THIS ORDER:
|   <description ... /> 
| [ <timed ... /> ]
</intervention>

description
timed

Documentation (type)

Units: List of elements

An intervention which may have various effects on the vector populations as a whole. (Not host specific.)

Multiple instances of this intervention class are allowed (multiple parameterisations, not just deployments).

Each instance may have multiple deployments. In this case the effects of each instance are independent (effects are combined) but the effects of multiple deployments of a single instance are not independent (only the latest deployment has any effect).

Attributes

Name of intervention

name=string

Name of intervention (e.g. larviciding, sugar bait).

description

→ scenario → interventions → vectorPop → intervention → description

<description>
IN THIS ORDER:
| ( <anopheles ... /> )+
</description>

anopheles

anopheles

→ scenario → interventions → vectorPop → intervention → description → anopheles

<anopheles
    mosquito=string
  >
IN ANY ORDER:
| [ <seekingDeathRateIncrease ... /> ]
| [ <probDeathOvipositing ... /> ]
| [ <emergenceReduction ... /> ]
</anopheles>

seekingDeathRateIncrease
probDeathOvipositing
emergenceReduction

Documentation (type)

Units: dimensionless Min: 0 Max: 1

Descriptions of the effects of vector interventions with per-species effects.

Attributes

Species/subspecies/variant name

mosquito=string

Name of the species/subspecies/variant.

Proportional increase in deaths while host searching

→ scenario → interventions → vectorPop → intervention → description → anopheles → seekingDeathRateIncrease

<seekingDeathRateIncrease
    initial=double
  >
IN THIS ORDER:
|   <decay ... /> 
</seekingDeathRateIncrease>

decay

Documentation (element)

Units: dimensionless

Describe an effect on the increase in the death rate while host seeking (mu_vA) due to this intervention.

Enter the rate increase (i.e. if rate increases to 120% of normal, give 0.2). New death rate while seeking is old × (1 + increase) where increase is this factor given. Must have increas ≥ -1.

Attributes

Initial proportion increase

initial=double

Units: dimensionless Min: -1 Max: inf

decay

→ scenario → interventions → vectorPop → intervention → description → anopheles → seekingDeathRateIncrease → decay

<decay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

Determines which decay function to use. Available decay functions, for age t in years: constant: 1 step: 1 for t less than L, otherwise 0 linear: 1 - t/L for t less than L, otherwise 0 exponential: exp( - t/L * log(2) ) weibull: exp( -(t/L)^k * log(2) ) hill: 1 / (1 + (t/L)^k) smooth-compact: exp( k - k / (1 - (t/L)^2) ) for t less than L, otherwise 0

L

L=string

Units: User-defined (defaults to years) Min: 0

(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

If sigma is non-zero, heterogeneity of decay is introduced via a random variable sampled from the log-normal distribution with mu and sigma as specified. Both mu and sigma default to zero when not specified. The decay rate is multiplied by this variable (effectively, the half-life is divided by it). Note that with m=0, the median of the variable and the median value of L is unchanged, and thus the time at which the median decay amongst the population of decaying objects reaches half (assuming exponential, Weibull or Hill decay) is L. With m=-½σ² (negative half sigma squared) the mean of the variable will be 1 and mean of the half-life L, but the time at which mean decay of the population has reached half may not be L.

σ (sigma)

sigma=double

Min: 0

Default value: 0

Proportion ovipositing mosquitoes killed

→ scenario → interventions → vectorPop → intervention → description → anopheles → probDeathOvipositing

<probDeathOvipositing
    initial=double
  >
IN THIS ORDER:
|   <decay ... /> 
</probDeathOvipositing>

decay

Documentation (element)

Units: dimensionless

Describe an effect of increased mortality while ovipositing due to this intervention. Enter the probability of dying due to this intervention.

Attributes

Initial probability of killing

initial=double

Units: dimensionless Min: 0 Max: 1

decay

→ scenario → interventions → vectorPop → intervention → description → anopheles → probDeathOvipositing → decay

<decay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Proportion of emerging pupa killed

→ scenario → interventions → vectorPop → intervention → description → anopheles → emergenceReduction

<emergenceReduction
    initial=double
  >
IN THIS ORDER:
|   <decay ... /> 
</emergenceReduction>

decay

Documentation (element)

Units: dimensionless

Describe an effect on emergence of pupa into adults: this value is the proportion of emerging pupa which are killed by this intervention.

This can be used as a crude way of modelling larviciding. It ca also be used to increase emergence by giving a negative value. The emergence rate is "old rate" × (1 - factor) where factor is the value given here; thus, for example, using -1 will double emergence.

Attributes

Initial proportion reduction

initial=double

Units: dimensionless Min: -inf Max: 1

decay

→ scenario → interventions → vectorPop → intervention → description → anopheles → emergenceReduction → decay

<decay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Vector population intervention deployment

→ scenario → interventions → vectorPop → intervention → timed

<timed>
IN THIS ORDER:
| ( <deploy ... /> )+
</timed>

deploy

Documentation (element)

List of timed vector population intervention deployment

deploy

→ scenario → interventions → vectorPop → intervention → timed → deploy

<deploy
    time=string
  />

Attributes

Time

time=string

Units: User defined (defauls to steps) Min: 0

Time at which this deployment occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

Baited trap

→ scenario → interventions → vectorTrap

<vectorTrap>
IN THIS ORDER:
| ( <intervention ... /> )+
</vectorTrap>

intervention

Documentation (element)

Traps attract and kill mosquitoes. They are modelled as a non-human-host where the probability of mosquitoes surviving feeding is zero (since otherwise the simulator would assume surviving mosquitoes have had a blood meal), and where this "host" is initially not present.

Model: each type of trap has has an initial availability relative to a human and a decay in availability. Each deployment has a fixed maximum lifespan, after which the traps from that deployment are removed (it is up to the user whether this is after availability is effectively zero or sooner, either coinciding with a redeployment or causing a reduction in overall effectiveness of traps).

intervention

→ scenario → interventions → vectorTrap → intervention

<intervention
  [ name=string ]
  >
IN THIS ORDER:
| ( <description ... /> )+
| [ <timed ... /> ]
</intervention>

description
timed

Documentation (type)

Parameters and deployment of one type of trap. In case multiple types of trap are needed simultaneously, multiple elements can be used. Note that different types of trap do not interact except that all will attract mosquitoes.

Attributes

Descriptive name for type of trap

name=string

Optional name for this type of trap

Description

→ scenario → interventions → vectorTrap → intervention → description

<description
    mosquito=string
  >
IN THIS ORDER:
|   <relativeAvailability ... /> 
|   <decayOfAvailability ... /> 
</description>

relativeAvailability
decayOfAvailability

Documentation (element)

Parameters associated with a vector trap intervention, per mosquito species.

Attributes

Species/subspecies/variant name

mosquito=string

Name of the species/subspecies/variant.

Initial relative availability

→ scenario → interventions → vectorTrap → intervention → description → relativeAvailability

<relativeAvailability
    value=double
  />

Documentation (element)

Units: Proportion Min: 0 Max: inf

Describes the availiability of a trap to a host-seeking mosquito relative to an average unprotected adult.

I.e. if this parameter is 2, then each trap will on average attract twice as many mosquitoes as unprotected adults.

This is the initial availability; it may decay towards zero depending on the configured decay function.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Decay of availability

→ scenario → interventions → vectorTrap → intervention → description → decayOfAvailability

<decayOfAvailability
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Describes how availability decays to zero.

If decay heterogeneity/variance is used, there will be a sample once-per-deployment (i.e. all traps of the same deployment will be affected the same way). There is no support for variances between traps (except in this crude way, between deployments).

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Vector trap intervention deployment

→ scenario → interventions → vectorTrap → intervention → timed

<timed>
IN THIS ORDER:
| ( <deploy ... /> )*
</timed>

deploy

Documentation (element)

List of timed vector trap intervention deployment

deploy

→ scenario → interventions → vectorTrap → intervention → timed → deploy

<deploy
    time=string
    ratioToHumans=double
    lifespan=string
  />

Attributes

Time

time=string

Units: User defined (defauls to steps) Min: 0

Ratio to humans

ratioToHumans=double

Min: 0 Max: inf

The number of traps deployed, by this deployment, per adult human. E.g. if there are currently 100 traps and 1000 humans, then a ratio of 0.1 will increase the number of traps to 200.

Lifespan

lifespan=string

Units: Steps or Days or Years

Life of the trap until replaced or removed, e.g. "73t" or "1y". After this time period, these traps will be removed from the simulation. New deployments do not automatically remove old traps. Existing traps cannot be refurbished in the model. It may make sense to make the end-of-life coincide with a new deployment.

Human-specific interventions

→ scenario → interventions → human

<human>
IN THIS ORDER:
| ( <component ... /> )+
| ( <deployment ... /> )*
</human>

component
deployment

Documentation (element)

Encapsulates all interventions whose effects are specific to the human host: any interventions where target humans may be selected via population-coverage, age limits and sub-population membership.

Component

→ scenario → interventions → human → component

<component
    id=string
  [ name=string ]
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <screen ... /> 
| |   <treatSimple ... /> 
| |   <treatPKPD ... /> 
| |   <decisionTree ... /> 
| |   <PEV ... /> 
| |   <BSV ... /> 
| |   <TBV ... /> 
| |   <ITN ... /> 
| |   <IRS ... /> 
| |   <GVI ... /> 
| | [ <recruitmentOnly ... /> ]
| |   <clearImmunity ... /> 
| [ <subPopRemoval ... /> ]
</component>

screen
treatSimple
treatPKPD
decisionTree
PEV
BSV
TBV
ITN
IRS
GVI
recruitmentOnly
clearImmunity
subPopRemoval

Documentation (element)

A parameterisation of an effect achieved by one component of an intervention. (An intervention is described as the effects of a set of components plus deployments of those components. This describes the components individually, not deployments or which components comprise an intervention.)

Each element describes one component: its effects, decay of the(se) effect(s), and related stuff (e.g. description of indirect decay and of usage levels).

Different interventions can deploy the same component to the same perso. In most cases this will just deploy a fresh instance (e.g. a new bed net will replace the old (nobody uses multiple bed nets), or a new drug dose will act on top of previous doses, or in the case of a vaccine, effect depends on the total number of previous inoculations (including from other interventions).

Where multiple components of the same type (but with different ids) are deployed (whether within a single intervention or by multiple interventions), they act independently (e.g. two bed nets deployed to a single host would act to reduce attractiveness or survival of mosquitoes biting that host twice — this may be useful to simulate some novel vector intervention since the two nets may have separate parameters).

Attributes

Component identifier

id=string

A short name or code identifying the intervention component (used to refer to this component when describing an intervention). Also the id of the sub-population defined as those hosts who have received this intervention and who haven't subsequently been removed from the sub-population.

Name of component

name=string

An informal name/description for the component

screen

→ scenario → interventions → human → component → screen

<screen
    diagnostic=string
  >
IN THIS ORDER:
| ( <positive ... /> )*
| ( <negative ... /> )*
</screen>

positive
negative

Documentation (type)

This can be combined with MDA to achieve mass screen and treat (MSAT) or other types of mass screening intervention.

When deployed to a host, this simulates a test of patent malaria (microscopy, RDT or some such), then triggers deployment of whichever intervention components are configured (deployments for both positive and negative test outcomes can be configured).

The use of the screening itself is reported (if enabled), but not the outcome. Deployment of interventions triggered by the screening may be reported, however.

Attributes

Name of diagnostic

diagnostic=string

Name of a parameterised diagnostic (see scenario/diagnostics).

positive

→ scenario → interventions → human → component → screen → positive

<positive
    id=string
  />

Documentation (type)

The list of components deployed to eligible humans.

Attributes

Identifier

id=string

The identifier (short name) of a component.

negative

→ scenario → interventions → human → component → screen → negative

<negative
    id=string
  />

Documentation (type)

The list of components deployed to eligible humans.

Attributes

Identifier

id=string

The identifier (short name) of a component.

treatSimple

→ scenario → interventions → human → component → treatSimple

<treatSimple
    durationLiver=string
    durationBlood=string
  />

Documentation (type)

Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.

Attributes

Length of liver-stage effect

durationLiver=string

Units: User defined

Length of blood-stage effect

durationBlood=string

Units: User defined

treatPKPD

→ scenario → interventions → human → component → treatPKPD

<treatPKPD
    schedule=string
    dosage=string
  [ delay_h=double ] DEFAULT VALUE 0
  />

Documentation (type)

A command to administer drugs according to a given schedule and dosage table, optionally with a delay.

Attributes

Name of treatment schedule

schedule=string

The name of a schedule to use for treatment.

Name of dosage table

dosage=string

The name of a dosage table to use for treatment.

Delay (hours)

delay_h=double

Default value: 0

decisionTree

→ scenario → interventions → human → component → decisionTree

<decisionTree
  [ name=string ]
  >
EXACTLY ONE OF:
|   <multiple ... /> 
|   <caseType ... /> 
|   <diagnostic ... /> 
|   <random ... /> 
|   <age ... /> 
|   <noTreatment ... /> 
|   <treatFailure ... /> 
| ( <treatPKPD ... /> )+
|   <treatSimple ... /> 
| ( <deploy ... /> )+
</decisionTree>

multiple
caseType
diagnostic
random
age
noTreatment
treatFailure
treatPKPD
treatSimple
deploy

Documentation (type)

Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.

Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.

Attributes

Name

name=string

An optional piece of documentation attached to this node.

Vaccines

→ scenario → interventions → human → component → PEV

<PEV>
IN THIS ORDER:
|   <decay ... /> 
|   <efficacyB ... /> 
| ( <initialEfficacy ... /> )+
</PEV>

decay
efficacyB
initialEfficacy

Documentation (element)

Pre-erythrocytic vaccine (PEV): prevents a proportion of infections from commencing.

Documentation (type)

Description of a vaccine's effect

Decay of effect

→ scenario → interventions → human → component → PEV → decay

<decay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Specification of decay of efficacy. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Variance parameter for vaccine efficacy

→ scenario → interventions → human → component → PEV → efficacyB

<efficacyB
    value=double
  />

Documentation (element)

Units: Positive real Min: 0.001 Max: 1.00E+06

Measure of variation in vaccine efficacy: efficacy is sampled from a beta distribution with efficacyB its beta parameter and its alpha parameter fixed such that the mean is that given by initialEfficacy.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Initial mean efficacy

→ scenario → interventions → human → component → PEV → initialEfficacy

<initialEfficacy
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Mean efficacy values before decay (see efficacyB and decay parameter descriptions for sampling and decay). The i-th value in this list is used for the efficacy of the vaccine after the i-th dose. Where more doses are given than there are values in this list, the last value is repeated.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Vaccines

→ scenario → interventions → human → component → BSV

<BSV>
IN THIS ORDER:
|   <decay ... /> 
|   <efficacyB ... /> 
| ( <initialEfficacy ... /> )+
</BSV>

decay
efficacyB
initialEfficacy

Documentation (element)

Blood-stage vaccine (BSV): acts as a killing factor on blood-stage parasites. Exact action depends on the within host model.

Documentation (type)

Description of a vaccine's effect

Vaccines

→ scenario → interventions → human → component → TBV

<TBV>
IN THIS ORDER:
|   <decay ... /> 
|   <efficacyB ... /> 
| ( <initialEfficacy ... /> )+
</TBV>

decay
efficacyB
initialEfficacy

Documentation (element)

Transmission-blocking vaccine (TBV): one minus this scales the probability of transmission to mosquitoes

Documentation (type)

Description of a vaccine's effect

Bed nets

→ scenario → interventions → human → component → ITN

<ITN>
IN THIS ORDER:
| [ <usage ... /> ]
|   <holeRate ... /> 
|   <ripRate ... /> 
|   <ripFactor ... /> 
|   <initialInsecticide ... /> 
|   <insecticideDecay ... /> 
|   <attritionOfNets ... /> 
| ( <anophelesParams ... /> )+
</ITN>

usage
holeRate
ripRate
ripFactor
initialInsecticide
insecticideDecay
attritionOfNets
anophelesParams

Documentation (element)

Description of bed-net interventions (ITNs, LLINs).

Proportion of time nets are used by humans

→ scenario → interventions → human → component → ITN → usage

<usage
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Usage of nets by humans, from 0 to 1.

At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-usagepropActing) + intervention_factorusage*propActing.

See also "propActing" (proportion of bits for which net acts).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Rate at which holes are made

→ scenario → interventions → human → component → ITN → holeRate

<holeRate
    mean=double
    sigma=double
  />

Documentation (element)

Units: Holes per annum Min: 0

The rate at which new holes are made in nets.

nHoles(t) = nHoles(t-1) + X where X~Pois(R/T) where T is the number of time-steps per year. R is sampled from log-normal: R ~ log N( log(mean)-sigma²/2, sigma² ) and is covariant with ripRate and insecticideDecay. (To be exact, a single Gaussian sample is taken, adjusted for each sigma then exponentiated.)

Documentation (type)

Parameters of a log-normal distribution.

Variates are sampled as: X ~ ln N( log(mean)-sigma²/2, sigma² ).

Equivalent R sample: rlnorm(n, log(m) - s*s/2, s)

Attributes

mean

mean=double

Units: (same as base units)

The mean of the lognormal distribution.

sigma

sigma=double

Sigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.

Rate at which holes are enlarged

→ scenario → interventions → human → component → ITN → ripRate

<ripRate
    mean=double
    sigma=double
  />

Documentation (element)

Units: Rips per existing hole per annum Min: 0

Each existing hole has a probability of being ripped bigger according to a Poisson process with this rate as (only) parameter.

New rips occur in a net at rate X~Pois(h×R/T) where h is the number of existing holes and T the number of time-steps per year. R is sampled from log-normal: R ~ log N( log(mean)-sigma²/2, sigma² ) and is covariant with holeRate and insecticideDecay. (To be exact, a single Gaussian sample is taken, adjusted for the each and sigma then exponentiated.)

Documentation (type)

Parameters of a log-normal distribution.

Variates are sampled as: X ~ ln N( log(mean)-sigma²/2, sigma² ).

Equivalent R sample: rlnorm(n, log(m) - s*s/2, s)

Attributes

mean

mean=double

Units: (same as base units)

The mean of the lognormal distribution.

sigma

sigma=double

Sigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.

Rip factor

→ scenario → interventions → human → component → ITN → ripFactor

<ripFactor
    value=double
  />

Documentation (element)

Units: none Min: 0

This factor expresses how important rips are in increasing the hole.

The hole index of a net is h + F×x where h and x are the total numbers of holes and rips respectively and F is the rip factor.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Initial insecticide

→ scenario → interventions → human → component → ITN → initialInsecticide

<initialInsecticide
    mu=double
    sigma=double
  />

Documentation (element)

Units: mg/m² Min: 0

The insecticide concentration of new nets is Gaussian distributed with mean "mu" and a standard deviation "sigma". The standard deviation should be small relative to the mean to avoid negative initial concentration. Any negative values sampled are set to 0.

Documentation (type)

Parameters of a normal distribution.

Variates are sampled as: X ~ N( mu, sigma² ).

Attributes

mu

mu=double

Units: (same as base units)

The mean of the normal distribution.

sigma

sigma=double

Units: (same as base units)

The standard deviation of variates.

Decay of insecticide

→ scenario → interventions → human → component → ITN → insecticideDecay

<insecticideDecay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Units: none

Decay curve for insecticide content of nets. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions

The distribution of decay rates over nets is covariant with the distribution of ripRate and holeRate over nets. This distribution is generated by taking one sample per net from a Gaussian distribution with mean 0 and standard deviation 1. For each variable, the sample is multiplied by the respective sigma and a constant added such that, once exponentiated, the mean of the variable over nets is 1. The variable is then exponentiated and multiplied by the required mean rate for the respective variable.

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Attrition of nets

→ scenario → interventions → human → component → ITN → attritionOfNets

<attritionOfNets
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Units: dimensionless

Specifies the rate at which nets are disposed of over time. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions

In the current model, nets are disposed of randomly (no correlation with state of decay) such that the chance of each net surviving until age t is the value of this decay function at time t. Equivalently (where a large number of nets are distributed at the same time), the proportion of nets remaining in use should match this decay function over time.

Humans are removed from the intervention component's sub-population on disposal (attrition) of their nets. Currently this event is not reported.

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

anophelesParams

→ scenario → interventions → human → component → ITN → anophelesParams

<anophelesParams
    mosquito=string
  [ propActive=double ] DEFAULT VALUE 1
  >
IN THIS ORDER:
| [ <holeIndexMax ... /> ]
| EXACTLY ONE OF:
| |   <deterrency ... /> 
| |   <twoStageDeterrency ... /> 
| EXACTLY ONE OF:
| |   <preprandialKillingEffect ... /> 
| |   <preprandialKillingEffectLogit ... /> 
| EXACTLY ONE OF:
| |   <postprandialKillingEffect ... /> 
| |   <postprandialKillingEffectLogit ... /> 
| EXACTLY ONE OF:
| | [ <fecundityReduction ... /> ]
| | [ <fecundityReductionLogit ... /> ]
</anophelesParams>

holeIndexMax
deterrency
twoStageDeterrency
preprandialKillingEffect
preprandialKillingEffectLogit
postprandialKillingEffect
postprandialKillingEffectLogit
fecundityReduction
fecundityReductionLogit

Attributes

Mosquito species

mosquito=string

Name of the affected anopheles-mosquito species.

Proportion of bites for which net acts

propActive=double

Units: dimensionless Min: 0 Max: 1

Default value: 1

The proportion of bites, when nets are in use, for which the net has any action whatsoever on the mosquito. At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-usagepropActing) + intervention_factorusage*propActing. See also "usage" (proportion of time nets are used by humans).

maximum of holed surface area that has an effect (comparable to no net)

→ scenario → interventions → human → component → ITN → anophelesParams → holeIndexMax

<holeIndexMax
    value=double
  />

Documentation (element)

Units: in same unit as holeIndex

Used by logit attacking and killing models only, holeIndexMax is a user defined maximum hole index (typically, the total surface area of a net).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Relative attractiveness

→ scenario → interventions → human → component → ITN → anophelesParams → deterrency

<deterrency
    insecticideFactor=double
    insecticideScalingFactor=double
    holeFactor=double
    interactionFactor=double
    holeScalingFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of net on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.

Attractiveness of the human is multiplied by exp(log(H)×h + log(P)×p + log(I)×h×p where H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Hole factor

holeFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Interaction factor

interactionFactor=double

Units: none Max: 1

holeFactor + insecticideFactor + interactionFactor must not be greater than 1, and is expected to be at least 0. A negative value is not necessarily invalid, but allows nets to increase transmission.

Hole scaling factor

holeScalingFactor=double

Units: none Min: 0

Relative attractiveness

→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency

<twoStageDeterrency>
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <entering ... /> 
| |   <enteringLogit ... /> 
| EXACTLY ONE OF:
| |   <attacking ... /> 
| |   <attackingLogit ... /> 
</twoStageDeterrency>

entering
enteringLogit
attacking
attackingLogit

Documentation (element)

Units: dimensionless

Effect of net on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.

This deterrency model multiplies human attractiveness by pEnt×pAtt.

Deterrency: entering

→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → entering

<entering
    insecticideFactor=double
    insecticideScalingFactor=double
  />

Documentation (element)

Units: dimensionless

pEnt represents the relative probability of entering due to ITNs: pEnt = exp(log(P)×p) where P is the insecticide factor and p=1−exp(-insecticideContent×insecticideScalingFactor).

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Deterrency: entering (logit model)

→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → enteringLogit

<enteringLogit
    baseFactor=double
    insecticideFactor=double
  />

Documentation (element)

Units: dimensionless

pEnt represents the relative probability of entering due to insecticide in the hut: pEnt = exp(logit.pEnt) / (exp(logit.pEnt) + 1) logit.pEnt = B + P * p where B is the basefactor (without net); P is insecticide factor, and p = log(insecticideContent+1). Without a net, probability of entering a house is pEnt0 = exp(logit.pEnt0) / (exp(logit.pEnt0) + 1) logit.pEnt0 = B Entering of mosquitoes is adjusted via multiplication by pEnt / pEnt0. To keep this in the range [0,1], we (normally) require that pEnt ≤ pEnt0 and thus P ≤ 0 and give a warning if this is not fulfilled.

Attributes

Base factor

baseFactor=double

Units: none

See parent element documentation

Insecticide factor

insecticideFactor=double

Units: none

See parent element documentation

Deterrency: attacking

→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → attacking

<attacking
    insecticideFactor=double
    insecticideScalingFactor=double
    holeFactor=double
    interactionFactor=double
    holeScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Units: dimensionless

pAtt represents the relative probability of attacking a human after entering a house due to ITNs (i.e. of feeding/dying vs. flying off): pAtt = B + H×h + P×p + I×h×p where B is the base (without net) probability; H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex × holeScalingFactor) and p=1 - exp(-insecticideContent × insecticideScalingFactor).

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Hole factor

holeFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Interaction factor

interactionFactor=double

Units: none Max: 1

holeFactor + insecticideFactor + interactionFactor must not be greater than 1, and is expected to be at least 0. A negative value is not necessarily invalid, but allows nets to increase transmission.

Hole scaling factor

holeScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Deterrency: attacking (logit model)

→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → attackingLogit

<attackingLogit
    baseFactor=double
    insecticideFactor=double
    holeFactor=double
    interactionFactor=double
  />

Documentation (element)

Units: dimensionless

pAtt represents the relative probability of attacking a human after entering a house due to ITNs (i.e. of feeding/dying vs. flying off): pAtt = exp(logit.pAtt) / (exp(logit.pAtt) + 1) logit.pAtt = B + H×min(h, hMax) + P×p + I×min(h, hMax)×p where B is the base factor (without net); H, P and I are the hole, insecticide and interaction factors respectively, and: h = log(holeIndex + 1) p = log(insecticideContent + 1) Without a net, probability of attacking a human after entering a house is pAtt0 = exp(logit.pAtt0) / (exp(logit.pAtt0) + 1) logit.pAtt0 = B + H×hMax where hMax=log(holeIndexMax + 1) and holeIndexMax is a user defined maximum hole index (typically, the total surface area of a net). Attacking of mosquitoes is adjusted via multiplication by pAtt / pAtt0. This may be larger and smaller than 1 (but will not be negative). By definition (through the logit transformation) pAtt0 > 0.

Attributes

Base factor

baseFactor=double

Units: dimensionless

Logit of the probability (e.g. of death, of entry, of attacking) without intervention.

Insecticide factor

insecticideFactor=double

Units: none

Coefficient of log(insecticide content+1) in a generalized linear model with logit link function.

Hole factor

holeFactor=double

Units: none

Coefficient of log(total holed surface area (in cm2) +1) in a generalized linear model with logit link function.

Interaction factor

interactionFactor=double

Units: none

Coefficient of the interaction term of log(total holed surface area (in cm2) +1) with log(insecticide content+1) in a generalized linear model with logit link function.

Pre-prandial killing effect

→ scenario → interventions → human → component → ITN → anophelesParams → preprandialKillingEffect

<preprandialKillingEffect
    insecticideFactor=double
    insecticideScalingFactor=double
    holeFactor=double
    interactionFactor=double
    holeScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of net on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not sleeping under a net. Parameterisations should take into account that mosquitoes do not always bite indoors.

Killing proportion is calculated as K = B + H×h + P×p + I×h×p where B is the base (without net) probability of death, H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).

Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+H ≤ 1, B+P ≤ 1, B+H+P+I ≤ 1, H ≥ 0, P ≥ 0 and H+P+I ≥ 0.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Hole factor

holeFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Interaction factor

interactionFactor=double

Units: none Max: 1

holeFactor + insecticideFactor + interactionFactor must not be greater than 1, and is expected to be at least 0. A negative value is not necessarily invalid, but allows nets to increase transmission.

Hole scaling factor

holeScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Pre-prandial killing effect (logit)

→ scenario → interventions → human → component → ITN → anophelesParams → preprandialKillingEffectLogit

<preprandialKillingEffectLogit
    baseFactor=double
    insecticideFactor=double
    holeFactor=double
    interactionFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of net on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not sleeping under a net.
Killing proportion is calculated as K=exp(logit.K)/(exp(logit.K)+1) logit.K = B + H×min(h,hMax) + P×p + I×min(h,hMax)×p where B is the basefactor (without net), H, P and I are the hole, insecticide and interaction factors respectively, h=log(holeIndex+1) and p=log(insecticideContent+1). Without a net, the killing proportion K0=exp(logit.K0)/(exp(logit.K0)+1) logit.K0 = B + H×hMax where hMax=log(holeIndexMax+1) and holeIndexMax is a user defined maximum hole index (typically, the total surface area of a net). Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−K0). To keep this in the range [0,1], we require that K ≥ K0. We enforce that P ≥ 0 (It would not make sense biologically if P were negative) and P+I*hMax ≥ 0 and H ≤ 0 and holeIndex ≤ holeIndexMax and give a warning if these conditions are not fulfilled.

Attributes

Base factor

baseFactor=double

Units: dimensionless

Logit of the probability (e.g. of death, of entry, of attacking) without intervention.

Insecticide factor

insecticideFactor=double

Units: none

Coefficient of log(insecticide content+1) in a generalized linear model with logit link function.

Hole factor

holeFactor=double

Units: none

Coefficient of log(total holed surface area (in cm2) +1) in a generalized linear model with logit link function.

Interaction factor

interactionFactor=double

Units: none

Coefficient of the interaction term of log(total holed surface area (in cm2) +1) with log(insecticide content+1) in a generalized linear model with logit link function.

Post-prandial killing effect

→ scenario → interventions → human → component → ITN → anophelesParams → postprandialKillingEffect

<postprandialKillingEffect
    insecticideFactor=double
    insecticideScalingFactor=double
    holeFactor=double
    interactionFactor=double
    holeScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of net on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not sleeping under a net. Parameterisations should take into account that mosquitoes do not always bite indoors.

Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+H ≤ 1, B+P ≤ 1, B+H+P+I ≤ 1, H ≥ 0, P ≥ 0 and H+P+I ≥ 0.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Hole factor

holeFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Interaction factor

interactionFactor=double

Units: none Max: 1

holeFactor + insecticideFactor + interactionFactor must not be greater than 1, and is expected to be at least 0. A negative value is not necessarily invalid, but allows nets to increase transmission.

Hole scaling factor

holeScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Post-prandial killing effect (logit)

→ scenario → interventions → human → component → ITN → anophelesParams → postprandialKillingEffectLogit

<postprandialKillingEffectLogit
    baseFactor=double
    insecticideFactor=double
    holeFactor=double
    interactionFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of net on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not sleeping under a net. Killing proportion is calculated as K=exp(logit.K)/(exp(logit.K)+1) logit.K = B + H×min(h,hMax) + P×p + I×min(h,hMax)×p where B is the basefactor (without net), H, P and I are the hole, insecticide and interaction factors respectively, h=log(holeIndex+1) and p=log(insecticideContent+1). Without a net, the killing proportion K0=exp(logit.K0)/(exp(logit.K0)+1) logit.K0 = B + H×hMax where hMax=log(holeIndexMax+1) and holeIndexMax is a user defined maximum hole index (typically, the total surface area of a net). Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−K0). To keep this in the range [0,1], we require that K ≥ K0. We enforce that P ≥ 0 (It would not make sense biologically if P were negative) and P+I*hMax ≥ 0 and H ≤ 0 and holeIndex ≤ holeIndexMax and give a warning if these conditions are not fulfilled.

Attributes

Base factor

baseFactor=double

Units: dimensionless

Logit of the probability (e.g. of death, of entry, of attacking) without intervention.

Insecticide factor

insecticideFactor=double

Units: none

Coefficient of log(insecticide content+1) in a generalized linear model with logit link function.

Hole factor

holeFactor=double

Units: none

Coefficient of log(total holed surface area (in cm2) +1) in a generalized linear model with logit link function.

Interaction factor

interactionFactor=double

Units: none

Coefficient of the interaction term of log(total holed surface area (in cm2) +1) with log(insecticide content+1) in a generalized linear model with logit link function.

Fecundity reduction

→ scenario → interventions → human → component → ITN → anophelesParams → fecundityReduction

<fecundityReduction
    insecticideFactor=double
    insecticideScalingFactor=double
    holeFactor=double
    interactionFactor=double
    holeScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Effect of net on fertility of mosquitoes who survive feeding on a protected human, relative to an unprotected human.

Fertility (number of eggs laid) is multiplied by (1-K) / (1-B), similar to killing effects. This is not allowed to be greater than 1.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Hole factor

holeFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Interaction factor

interactionFactor=double

Units: none Max: 1

holeFactor + insecticideFactor + interactionFactor must not be greater than 1, and is expected to be at least 0. A negative value is not necessarily invalid, but allows nets to increase transmission.

Hole scaling factor

holeScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Fecundity reduction (logit)

→ scenario → interventions → human → component → ITN → anophelesParams → fecundityReductionLogit

<fecundityReductionLogit
    baseFactor=double
    insecticideFactor=double
    holeFactor=double
    interactionFactor=double
  />

Documentation (element)

Effect of net on fertility of mosquitoes who survive feeding on a protected human, relative to an unprotected human.

Fertility (number of eggs laid) is multiplied by (1-K) / (1-K0), similar to killing effects. This is not allowed to be greater than 1.

Attributes

Base factor

baseFactor=double

Units: dimensionless

Logit of the probability (e.g. of death, of entry, of attacking) without intervention.

Insecticide factor

insecticideFactor=double

Units: none

Coefficient of log(insecticide content+1) in a generalized linear model with logit link function.

Hole factor

holeFactor=double

Units: none

Coefficient of log(total holed surface area (in cm2) +1) in a generalized linear model with logit link function.

Interaction factor

interactionFactor=double

Units: none

Coefficient of the interaction term of log(total holed surface area (in cm2) +1) with log(insecticide content+1) in a generalized linear model with logit link function.

Indoor residual spraying

→ scenario → interventions → human → component → IRS

<IRS>
IN THIS ORDER:
| [ <usage ... /> ]
|   <initialInsecticide ... /> 
|   <insecticideDecay ... /> 
| ( <anophelesParams ... /> )+
</IRS>

usage
initialInsecticide
insecticideDecay
anophelesParams

Documentation (element)

Description of indoor residual spraying interventions.

Documentation (type)

Description of effect for the more complex and probably more realistic Briet model: IRS has three effects, whos strength is calculated as a function of surviving insecticide content.

Proportion of Indoor residual spraying (IRS) interventions

→ scenario → interventions → human → component → IRS → usage

<usage
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Usage of Indoor residual spraying (IRS) interventions, from 0 to 1.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Initial insecticide

→ scenario → interventions → human → component → IRS → initialInsecticide

<initialInsecticide
    mu=double
    sigma=double
  />

Documentation (element)

Units: μg/cm² Min: 0

The insecticide concentration of IRS (at time of spraying) is Gaussian distributed with mean "mu" and a standard deviation "sigma". The standard deviation should be small relative to the mean to avoid negative initial concentration. Any negative values sampled are set to 0.

Documentation (type)

Parameters of a normal distribution.

Variates are sampled as: X ~ N( mu, sigma² ).

Attributes

mu

mu=double

Units: (same as base units)

The mean of the normal distribution.

sigma

sigma=double

Units: (same as base units)

The standard deviation of variates.

Decay of insecticide

→ scenario → interventions → human → component → IRS → insecticideDecay

<insecticideDecay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Units: none

Decay curve for insecticide content of IRS. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Per-mosquito species parameters

→ scenario → interventions → human → component → IRS → anophelesParams

<anophelesParams
    mosquito=string
  [ propActive=double ] DEFAULT VALUE 1
  >
IN THIS ORDER:
|   <deterrency ... /> 
|   <preprandialKillingEffect ... /> 
|   <postprandialKillingEffect ... /> 
| [ <fecundityReduction ... /> ]
</anophelesParams>

deterrency
preprandialKillingEffect
postprandialKillingEffect
fecundityReduction

Attributes

Mosquito species

mosquito=string

Name of the affected anopheles-mosquito species.

Proportion of bites for which IRS acts

propActive=double

Units: dimensionless Min: 0 Max: 1

Default value: 1

The proportion of bites for which the IRS has any action whatsoever on the mosquito. At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-propActing) + intervention_factor*propActing.

Relative attractiveness

→ scenario → interventions → human → component → IRS → anophelesParams → deterrency

<deterrency
    insecticideFactor=double
    insecticideScalingFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of IRS on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.

Attractiveness of the human is multiplied by exp(P×log(p)) where P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Pre-prandial killing effect

→ scenario → interventions → human → component → IRS → anophelesParams → preprandialKillingEffect

<preprandialKillingEffect
    insecticideFactor=double
    insecticideScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of IRS on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors. This parameter has been added since some data shows IRS to have a preprandial killing effect.

Killing proportion is calculated as K = B + P×p where B is the base (without protection) probability of death, and P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).

Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+P ≤ 1 and P ≥ 0.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Post-prandial killing effect

→ scenario → interventions → human → component → IRS → anophelesParams → postprandialKillingEffect

<postprandialKillingEffect
    insecticideFactor=double
    insecticideScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of IRS on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors.

Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+P ≤ 1 and P ≥ 0.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Fecundity reduction

→ scenario → interventions → human → component → IRS → anophelesParams → fecundityReduction

<fecundityReduction
    insecticideFactor=double
    insecticideScalingFactor=double
    baseFactor=double
  />

Documentation (element)

Effect of IRS on fertility mosquitoes after successfully feeding on a human host, relative to an unproteced human. Parameterisations should take into account that mosquitoes do not always bite indoors.

First, we calculate K = B + P×p where B is the base (without protection) probability of death, and P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).

Fecundity is multiplied by (1−K) / (1−B). It is not allowed to be greater than 1. To keep this in the range [0,1], we require that B+P ≤ 1 and P ≥ 0.

Attributes

Insecticide factor

insecticideFactor=double

Units: none Max: 1

Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.

Insecticide scaling factor

insecticideScalingFactor=double

Units: none Min: 0

Probability of mosquito death without intervention

baseFactor=double

Units: dimensionless

Generic vector intervention

→ scenario → interventions → human → component → GVI

<GVI>
IN THIS ORDER:
| [ <usage ... /> ]
|   <decay ... /> 
| ( <anophelesParams ... /> )+
</GVI>

usage
decay
anophelesParams

Documentation (element)

Low-level description of intervention effects on vectors (i.e. mosquitoes). Can be used to describe simple ITN or IRS interventions (though more complex models are available for these interventions) or other interventions such as mosquito repellant or ivermectin.

Note that all actions of this intervention component will decay according to a single decay function. If independant decay is wanted, a separate component can be used for each action.

Proportion of generic vector interventions

→ scenario → interventions → human → component → GVI → usage

<usage
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Usage of Generic vector interventions, from 0 to 1.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Decay

→ scenario → interventions → human → component → GVI → decay

<decay
    function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
  [ L=string ]
  [ k=double ] DEFAULT VALUE 1.0
  [ mu=double ] DEFAULT VALUE 0
  [ sigma=double ] DEFAULT VALUE 0
  />

Documentation (element)

Description of decay of all intervention effects. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions

Documentation (type)

Specification of decay or survival of a parameter.

Attributes

function

function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")

Units: None Min: 0 Max: 1

L

L=string

Units: User-defined (defaults to years) Min: 0

k

k=double

Units: none Min: 0

Default value: 1.0

Shape parameter of distribution. If not specified, default value of 1 is used. Meaning depends on function; not used in some cases.

μ (mu)

mu=double

Min: 0

Default value: 0

σ (sigma)

sigma=double

Min: 0

Default value: 0

Per-mosquito species parameters

→ scenario → interventions → human → component → GVI → anophelesParams

<anophelesParams
    mosquito=string
  [ propActive=double ] DEFAULT VALUE 1
  >
IN THIS ORDER:
| [ <deterrency ... /> ]
| [ <preprandialKillingEffect ... /> ]
| [ <postprandialKillingEffect ... /> ]
| [ <fecundityReduction ... /> ]
</anophelesParams>

deterrency
preprandialKillingEffect
postprandialKillingEffect
fecundityReduction

Attributes

Mosquito species

mosquito=string

Name of the affected anopheles-mosquito species.

Proportion of bites for which IRS acts

propActive=double

Units: dimensionless Min: 0 Max: 1

Default value: 1

Relative attractiveness

→ scenario → interventions → human → component → GVI → anophelesParams → deterrency

<deterrency
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of intervention on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.

Attractiveness of the human is multiplied this factor times survival of effect.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Pre-prandial killing effect

→ scenario → interventions → human → component → GVI → anophelesParams → preprandialKillingEffect

<preprandialKillingEffect
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of intervention on survival of mosquitoes as they seek to bite a human after choosing that human, relative to the same person not protected by the intervention. Parameterisations should take into account that mosquitoes do not always bite indoors. This parameter has been added since some data shows IRS to have a preprandial killing effect.

Killing proportion is this factor multiplied by survival of effect.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Post-prandial killing effect

→ scenario → interventions → human → component → GVI → anophelesParams → postprandialKillingEffect

<postprandialKillingEffect
    value=double
  />

Documentation (element)

Units: dimensionless Min: 0 Max: 1

Effect of intervention on survival of mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not protected by the intervention. Parameterisations should take into account that mosquitoes do not always bite indoors.

Killing proportion is this factor multiplied by survival of effect.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Fecundity reduction effect

→ scenario → interventions → human → component → GVI → anophelesParams → fecundityReduction

<fecundityReduction
    value=double
  />

Documentation (element)

Min: 0

Effect of intervention on fertility mosquitoes after successfully feeding on a human host, relative to an unproteced human. Parameterisations should take into account that mosquitoes do not always bite indoors.

Fertility is multiplied by 1 - (fecundityReduction * decay).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Recruitment only

→ scenario → interventions → human → component → recruitmentOnly

<recruitmentOnly/>

Documentation (element)

Recruitment of a host into a sub-population.

All human-targeting intervention deployments recruit simulated humans into a sub-population which can be used for the purposes of cumulative deployment, deployment only to a sub-population and defining a cohort. This pseudo-intervention can be used to define a sub-population without also deploying some intervention.

Clear Immunity

→ scenario → interventions → human → component → clearImmunity

<clearImmunity/>

Documentation (element)

Removes all exposure-related immunitsy gained over time by hosts without removing infections (or affecting the ability to gain immunity through exposure).

Hypothetical, but potentially useful to simulate scenarios with unprotected humans.

subPopRemoval

→ scenario → interventions → human → component → subPopRemoval

<subPopRemoval
  [ onFirstBout=boolean ] DEFAULT VALUE false
  [ onFirstTreatment=boolean ] DEFAULT VALUE false
  [ onFirstInfection=boolean ] DEFAULT VALUE false
  [ afterYears=double ]
  />

Documentation (type)

Each human intervention component corresponds to a sub-population: those who have received or are considered to be protected by the intervention component. Humans automatically become members of this sub-population when receiving an intervention component; this element controls how humans are removed from the sub-population.

ITN attrition also removes humans from sub-populations.

Note that sub-populations do not directly correspond to an intervention's effects: lack of effectiveness does not imply removal from the sub-population (except as explicitly configured here) and removal from the sub-population does not halt an intervention's effects.

Sub-populations may be used to define a cohort, to restrict deployment of other interventions and to use cumulative deployment mode. A sub- population may or may not correspond (roughly) to humans protected by some intervention.

Attributes

Time to first episode only

onFirstBout=boolean

Default value: false

If true, remove individuals from the sub-population at the start of the first episode (start of a clinical bout) since they were recruited into the sub-population. This is intended for cohort studies which measure time to the first episode, using active case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.

Time to first treatment only

onFirstTreatment=boolean

Default value: false

If true, remove individuals from the sub-population when they first seektreatment since they were recruited into the sub-population. This is intended for cohort studies which measure the time to first episode, using passive case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.

Time to first infection only

onFirstInfection=boolean

Default value: false

If true, remove individuals from the sub-population at completion of the first survey in which they present with a patent infection since they were recruited into the sub-population. This intended for cohort studies which measure time to the first infection, using active case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.

Remove from sub-population after

afterYears=double

Units: Years Min: 0

If given, membership to the sub-population of humans who have received this intervention component expires after the given number of years. Note that future deployments renew membership (e.g. if this parameter is 4 years and the intervention is redeployed 3 years from now, expiry happens after 7 years). This provides a crude way of modelling a cohort protected by some intervention. A few interventions provide more detailed ways of modelling expiry of protection. In any case, "expiry of protection" is an abstract concept and does not imply that all protection has ceased, even in the simulator. This may also be useful for cumulative deployment. Minimum duration is zero, which implies the human is effectively never a member of the sub-population; a duration of one timestep implies the human is a member of the sub-population while any futher interventions are deployed on the same time as this human becomes a member and on the next update of the human (including transmission and health system events) but not beyond that. If this attribute is not given, the simulated human is a member until death or some other option triggers removal. Input is rounded to the nearest time step.

Deployment

→ scenario → interventions → human → deployment

<deployment
  [ name=string ]
  >
IN THIS ORDER:
| ( <component ... /> )+
| ( <condition ... /> )*
| ( <continuous ... /> )*
| ( <timed ... /> )*
</deployment>

component
condition
continuous
timed

Documentation (element)

This element describes deployment of an intervention: which components are deployed, how humans are selected for deployment (via timed or age-based deployment) as well as a few additional restrictions (e.g. vaccine dosing restrictions).

All components deployed by this intervention are deployed to the same people (each timed or continuous deployment selects recipients and then gives each recipient all components of the intervention).

Attributes

Intervention name

name=string

Name of intervention

component

→ scenario → interventions → human → deployment → component

<component
    id=string
  />

Documentation (type)

The list of components deployed to eligible humans.

Attributes

Identifier

id=string

The identifier (short name) of a component.

Condition

→ scenario → interventions → human → deployment → condition

<condition
    measure=string
  [ minValue=double ]
  [ maxValue=double ]
    initialState=boolean
  />

Documentation (element)

If conditions are specified, deployment of this intervention will only go ahead if all specified conditions are true. Condition statements are evaluated only during surveys, so deployment is enabled or disabled depending on the results of the most recent survey. So called unreported surveys can be used to reevaluate conditions without increasing granularity of output.

Conditions are evaluated for the whole population, not for individual age-groups or cohorts.

This affects all types of deployment.

Attributes

Measure

measure=string

The monitoring measure to test. Not all measures are available for use.

Minimum value

minValue=double

Minimum value. If specified, the measured variable must be greater than or equal to this value for the condition to be satisfied.

Maximum value

maxValue=double

Maximum value. If specified, the measured variable must be less than or equal to this value for the condition to be satisfied.

Initial state

initialState=boolean

Whether this condition is considered true or false before updated by a survey.

Age-based (continuous) deployment

→ scenario → interventions → human → deployment → continuous

<continuous>
IN THIS ORDER:
| [ <restrictToSubPop ... /> ]
| ( <deploy ... /> )+
</continuous>

restrictToSubPop
deploy

Documentation (element)

List of ages at which deployment takes place (through EPI, post-natal and school-based programmes, etc.).

A sub-population restriction may be added as a property of the list of continuous deployments.

restrictToSubPop

→ scenario → interventions → human → deployment → continuous → restrictToSubPop

<restrictToSubPop
    id=string
  [ complement=boolean ] DEFAULT VALUE false
  />

Documentation (type)

If this element is specified, deployment is restricted to some sub-population (specified via the "id" attribute); otherwise the target population is the entire simulated population. Either way, other deployment restrictions (age, time, number of vaccine doeses) still apply.

Attributes

Sub-population identifier

id=string

The identifier (short name) of the sub-population (i.e. the "id" of some intervention component). Also see the "complement" attribute.

Complement

complement=boolean

Default value: false

If this is not specified or is false, deployment is restricted to the sub-population of people protected by the intervention component who's id is given. If complement is set to true, deployment is instead restricted to the complement of that sub-population, i.e. to those

deploy

→ scenario → interventions → human → deployment → continuous → deploy

<deploy
    coverage=double
  [ vaccMinPrevDoses=int ]
  [ vaccMaxCumDoses=int ]
    targetAgeYrs=double
  [ begin=string ]
  [ end=string ]
  />

Attributes

Coverage

coverage=double

Units: dimensionless Min: 0 Max: 1

Proportion of otherwise eligible individuals who will receive this deployment.

Vaccine min previous doses

vaccMinPrevDoses=int

Units: inoculations Min: 0

Applies to vaccines only: vaccine doses are only deployed by this deployment if the previous number of doses (for the component deployed) is at least this number. For example, if this is the second deployment opportunity for this vaccine and this value is 1, then this deployment cannot deploy the vaccine to individuals who did not receive the first deployment.

Vaccine max cumulative doses

vaccMaxCumDoses=int

Units: inoculations Min: 0

Applies to vaccines only: vaccine doses are only deployed by this deployment if the previous number of doses (for the component deployed) is less than this number.

Target age

targetAgeYrs=double

Units: Years Min: 0 Max: 100

Target age of intervention. Input is rounded to the nearest time step.

First time active

begin=string

Units: User defined (defauls to steps)

First time at which this deployment is active. If not specified, deployment starts at the beginning of the intervention period. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

End step

end=string

Units: User defined (defauls to steps)

End of the period during which the intervention is active (to be exact, the first step of the intervention period at which the item becomes inactive). If not specified, deployment never ceases after starting during the simulation. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).

Mass (timed) deployment

→ scenario → interventions → human → deployment → timed

<timed>
IN THIS ORDER:
| [ <restrictToSubPop ... /> ]
| [ <cumulativeCoverage ... /> ]
| ( <deploy ... /> )+
</timed>

restrictToSubPop
cumulativeCoverage
deploy

Documentation (element)

List of timed deployments of the intervention (that is, of deployment campaigns).

Cumulative deployment mode can be specified for all deployments in a timed list. To allow multiple cumulative deployment descriptions, the entire timed list may be repeated.

restrictToSubPop

→ scenario → interventions → human → deployment → timed → restrictToSubPop

<restrictToSubPop
    id=string
  [ complement=boolean ] DEFAULT VALUE false
  />

Documentation (type)

Attributes

Sub-population identifier

id=string

The identifier (short name) of the sub-population (i.e. the "id" of some intervention component). Also see the "complement" attribute.

Complement

complement=boolean

Default value: false

Cumulative coverage

→ scenario → interventions → human → deployment → timed → cumulativeCoverage

<cumulativeCoverage
    component=string
  />

Documentation (element)

If this element is not specified, standard deployment occurs, where a portion of the population as given by the coverage property of this campaign is selected, and interventions are deployed to all of these people (regardless of previous coverage).

If this attribute is specified, instead, the population is divided into two sets: those who are a member of a certain sub-population and those who are not (see "subPopRemoval" element). If the proportion of people in the first set is less than the desired coverage, then the proportion of people from the second set needed to increase total coverage to the desired coverage is calculated. This proportion is then used as the probablity of selection from the second set into a third set of people who then receive all interventions deployed by this campaign.

Note that selection is stochastic so the final coverage level may not be exactly that desired. Note also that the component used when selecting people need not actually be one of the components deployed by this intervention, although that is the intended use case.

Attributes

Component identifier

component=string

The identifier (short name) of the component used when selecting people.

deploy

→ scenario → interventions → human → deployment → timed → deploy

<deploy
    coverage=double
  [ vaccMinPrevDoses=int ]
  [ vaccMaxCumDoses=int ]
    time=string
  [ maxAge=double ]
  [ minAge=double ] DEFAULT VALUE 0
  [ repeatStep=string ]
  [ repeatEnd=string ]
  />

Attributes

Coverage

coverage=double

Units: dimensionless Min: 0 Max: 1

Proportion of otherwise eligible individuals who will receive this deployment.

Vaccine min previous doses

vaccMinPrevDoses=int

Units: inoculations Min: 0

Vaccine max cumulative doses

vaccMaxCumDoses=int

Units: inoculations Min: 0

Applies to vaccines only: vaccine doses are only deployed by this deployment if the previous number of doses (for the component deployed) is less than this number.

Time

time=string

Units: User defined (defauls to steps) Min: 0

Maximum age of eligible individuals

maxAge=double

Units: Years Min: 0

Maximum age of eligible individuals (defaults to no limit). Input is rounded to the nearest time step.

Minimum age of eligible individuals

minAge=double

Units: Years Min: 0

Default value: 0

Minimum age of eligible individuals (defaults to 0). Input is rounded to the nearest time step.

Step of repetition

repeatStep=string

Units: User defined

See repeatEnd's documentation.

End of repetition (exclusive)

repeatEnd=string

Units: User defined

Either both repeatStep and repeatEnd should be present or neither. If present, the deployment is repeated every repeatStep timesteps (i.e. if t0 is the initial time and x is repeatStep, depolyments are done at times t0, t0+x, t0+2*x, ...), ending before repeatEnd (final repetition is the one before repeatEnd). Note that repeatEnd may be specified as a date but repeatStep must be a duration (days, steps or years).

Health system description

→ scenario → healthSystem

<healthSystem>
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <EventScheduler ... /> 
| |   <ImmediateOutcomes ... /> 
| |   <DecisionTree5Day ... /> 
|   <CFR ... /> 
|   <pSequelaeInpatient ... /> 
</healthSystem>

EventScheduler
ImmediateOutcomes
DecisionTree5Day
CFR
pSequelaeInpatient

Documentation (element)

Description of health system.

Documentation (type)

Description of case management system, used to specify the initial model or a replacement (an intervention). Encompasses case management data and some other data required to derive case outcomes.

Contains a sub-element describing the particular health-system in use. Health system data is here defined as data used to decide on a treatment strategy, given a case requiring treatment.

Transmission and vector bionomics

→ scenario → entomology

<entomology
    name=string
    mode=("forced" or "dynamic")
  [ scaledAnnualEIR=double ]
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <nonVector ... /> 
| |   <vector ... /> 
</entomology>

nonVector
vector

Documentation (element)

Description of entomological data

Attributes

Entomology dataset name

name=string

Name of entomology data

Transmission model mode

mode=("forced" or "dynamic")

Transmission simulation mode: may be forced (in which case interventions and changes to human infectiousness cannot affect EIR) or dynamic (in which the above can affect EIR). The full vector model is only used in dynamic mode. This can not be changed by interventions, except for the changeEIR intervention for the non-vector model which replaces the EIR with a new description (used in forced mode).

Override annual EIR

scaledAnnualEIR=double

Units: Infectious bites per adult per year

If set, the annual EIR (for all species of vector) is scaled to this level; can be omitted if not needed.

Transmission setting (vector control not enabled)

→ scenario → entomology → nonVector

<nonVector
    eipDuration=int
  >
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</nonVector>

EIRDaily

Documentation (element)

Description of transmission setting for models without vector control interventions (included for backward compatibility)

Attributes

Duration of sporogony

eipDuration=int

Units: Days

The duration of sporogony in days

Transmission setting (vector control enabled)

→ scenario → entomology → vector

<vector>
IN THIS ORDER:
| ( <anopheles ... /> )+
| ( <nonHumanHosts ... /> )*
</vector>

anopheles
nonHumanHosts

Documentation (element)

Parameters of the transmission model

anopheles

→ scenario → entomology → vector → anopheles

<anopheles
    mosquito=string
    propInfected=double
    propInfectious=double
  >
IN THIS ORDER:
|   <seasonality ... /> 
|   <mosq ... /> 
| [ <lifeCycle ... /> ]
| [ <simpleMPD ... /> ]
| ( <nonHumanHosts ... /> )*
</anopheles>

seasonality
mosq
lifeCycle
simpleMPD
nonHumanHosts

Documentation (type)

Description of input EIR for one specific vector species in terms of a Fourier approximation to the ln of the EIR during the burn in period

Attributes

Identifier for this anopheles species

mosquito=string

Identifier for this anopheles species

Initial estimate of proportion of mosquitoes infected (ρ_O)

propInfected=double

Units: Proportion Min: 0 Max: 1

Initial guess of the proportion of mosquitoes which are infected, o: O_v(t) = o*N_v(t). Only used as a starting value.

Initial estimate of proportion of mosquitoes infectious (ρ_S)

propInfectious=double

Units: Proportion Min: 0 Max: 1

Initial estimate of the proportion of mosquitoes which are infectious, s: S_v(t) = s*N_v(t). Used as a starting value and then fit.

Seasonality of transmission

→ scenario → entomology → vector → anopheles → seasonality

<seasonality
    input=("EIR")
  [ annualEIR=double ]
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <fourierSeries ... /> 
| |   <monthlyValues ... /> 
| |   <dailyValues ... /> 
</seasonality>

fourierSeries
monthlyValues
dailyValues

Documentation (element)

Specifies the seasonality of transmission and optionally the level of annual transmission.

Attributes

Seasonality input

input=("EIR")

Specify what seasonality measure is given. At the moment, only EIR is supported, but in the future, all the below should be supported. EIR: seasonality of entomological inoculations is input. Units: entomological inoculations per adult per annum. hostSeeking: seasonality of densities of flying host-seeking mosquitoes is input (in the model this is notated N_v). Units: mosquitoes. emergence: seasonality of emergence pupa into adults. Units: mosquitoes. larvalResources: seasonality of larval resources. Units: X.

Annual EIR

annualEIR=double

Units: Inoculations per adult per annum Min: 0

If this attribute is included, EIR for this species is scaled to this level. Note that if the scaledAnnualEIR attribute of the entomology element is also used, EIR is scaled again, making this attribute the EIR relative to other species. With some seasonality inputs, this attribute is optional, in which case (if scaledAnnualEIR is also not specified) transmission depends on all parameters of the vector. With some seasonality inputs, however, this parameter must be specified.

Fourier approximation to pre-intervention EIR

→ scenario → entomology → vector → anopheles → seasonality → fourierSeries

<fourierSeries
    EIRRotateAngle=double
  >
IN THIS ORDER:
| ( <coeffic ... /> )*
</fourierSeries>

coeffic

Documentation (element)

Units: Infectious bites per adult per day

Seasonality is reproduced from the exponential of a fourier series specified by the following coefficients. Note that the a0 term is not needed; the annualEIR attribute of the seasonality element should be used to scale EIR instead.

Attributes

Rotation angle defining the origin of the Fourier approximation to ln (EIR)

EIRRotateAngle=double

Units: Radians

Rotation angle defining the origin of the Fourier approximation to ln (EIR)

Pair of Fourier coefficients

→ scenario → entomology → vector → anopheles → seasonality → fourierSeries → coeffic

<coeffic
    a=double
    b=double
  />

Documentation (element)

A pair of Fourier series coefficients. The first element specifies a1 and b1, the second a2 and b2, etc. Any number (from 0 up) of pairs may be given.

Attributes

a_n parameter of Fourier approximation to ln(EIR)

a=double

a_n parameter of Fourier approximation to ln(EIR) for some natural number n.

b_n parameter of Fourier approximation to ln(EIR)

b=double

b_n parameter of Fourier approximation to ln(EIR) for some natural number n.

List of monthly values

→ scenario → entomology → vector → anopheles → seasonality → monthlyValues

<monthlyValues
    smoothing=("none" or "fourier")
  >
IN THIS ORDER:
| ( <value ... /> ){12,12}
</monthlyValues>

value

Documentation (element)

Description of seasonality from monthly values. Multiple smoothing methods are possible (see smoothing attribute).

List should contain twelve entries: January to December.

Attributes

Smoothing function

smoothing=("none" or "fourier")

How the monthly values are converted into a daily sequence of values:

none: no smoothing (step function)
Fourier: a Fourier series (with terms up to a2/b2) is fit to the sequence of monthly values and used to generate a smoothed list of daily values.

Monthly value

→ scenario → entomology → vector → anopheles → seasonality → monthlyValues → value

<value>
    double
</value>

Documentation (element)

Units: (see "seasonality input" parameter)

Monthly value

List of daily values

→ scenario → entomology → vector → anopheles → seasonality → dailyValues

<dailyValues>
IN THIS ORDER:
| ( <value ... /> ){365,365}
</dailyValues>

value

Documentation (element)

Description of seasonality from daily values.

List should contain 365 entries: 1st January to 31st December.

Daily value

→ scenario → entomology → vector → anopheles → seasonality → dailyValues → value

<value>
    double
</value>

Documentation (element)

Units: (see "seasonality input" parameter)

Daily value

Mosquito feeding cycle parameters

→ scenario → entomology → vector → anopheles → mosq

<mosq
    minInfectedThreshold=double
  >
IN ANY ORDER:
|   <mosqRestDuration ... /> 
|   <extrinsicIncubationPeriod ... /> 
|   <mosqLaidEggsSameDayProportion ... /> 
|   <mosqSeekingDuration ... /> 
|   <mosqSurvivalFeedingCycleProbability ... /> 
|   <availabilityVariance ... /> 
|   <mosqProbBiting ... /> 
|   <mosqProbFindRestSite ... /> 
|   <mosqProbResting ... /> 
|   <mosqProbOvipositing ... /> 
|   <mosqHumanBloodIndex ... /> 
</mosq>

mosqRestDuration
extrinsicIncubationPeriod
mosqLaidEggsSameDayProportion
mosqSeekingDuration
mosqSurvivalFeedingCycleProbability
availabilityVariance
mosqProbBiting
mosqProbFindRestSite
mosqProbResting
mosqProbOvipositing
mosqHumanBloodIndex

Documentation (element)

Parameters describing the feeding cycle and human mosquito interaction of a single species of anopheles mosquito.

Attributes

Mininum infected threshold for mosquitos

minInfectedThreshold=double

Min: 0

If less than this many mosquitoes remain infected, transmission is interrupted.

Duration of the resting period of the vector

→ scenario → entomology → vector → anopheles → mosq → mosqRestDuration

<mosqRestDuration
    value=int
  />

Documentation (element)

Units: Days

name:Duration of the resting period of the vector (days);

Attributes

Input parameter value

value=int

An integer value.

Extrinsic incubation period

→ scenario → entomology → vector → anopheles → mosq → extrinsicIncubationPeriod

<extrinsicIncubationPeriod
    value=int
  />

Documentation (element)

Units: Days

name:Extrinsic incubation period (days)

Attributes

Input parameter value

value=int

An integer value.

Proportion of mosquitoes host seeking on same day as ovipositing

→ scenario → entomology → vector → anopheles → mosq → mosqLaidEggsSameDayProportion

<mosqLaidEggsSameDayProportion
    value=double
  />

Documentation (element)

Units: Proportion

Proportion of mosquitoes host seeking on same day as ovipositing

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Duration of the host-seeking period of the vector

→ scenario → entomology → vector → anopheles → mosq → mosqSeekingDuration

<mosqSeekingDuration
    value=double
  />

Documentation (element)

Units: Days

Duration of the host-seeking period of the vector (days)

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that the mosquito survives the feeding cycle

→ scenario → entomology → vector → anopheles → mosq → mosqSurvivalFeedingCycleProbability

<mosqSurvivalFeedingCycleProbability
    value=double
  />

Documentation (element)

Units: Proportion

Probability that the mosquito survives the feeding cycle

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Variance in human availability rate

→ scenario → entomology → vector → anopheles → mosq → availabilityVariance

<availabilityVariance
    value=double
  />

Documentation (element)

Variance in availability rate of humans to mosquitoes. The mean rate is calculated based on other parameters.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that the mosquito succesfully bites chosen host

→ scenario → entomology → vector → anopheles → mosq → mosqProbBiting

<mosqProbBiting
    mean=double
    variance=double
  />

Documentation (element)

Probability that the mosquito succesfully bites chosen host

Documentation (type)

Parameters of a normal distribution, provided as mean and variance.

Variates are sampled from Be(α,β) where α and β are determined from the mean and variance as follows: let v be the variance and c=mean/(1-mean). Then we set α=cβ and β=((c+1)²v - c)/((c+1)³v).

Attributes

mean

mean=double

Units: none

The mean of the beta distribution (must be in the open range (0,1)).

variance

variance=double

Units: none

The standard deviation of variates.

Probability that the mosquito escapes host and finds a resting place after biting

→ scenario → entomology → vector → anopheles → mosq → mosqProbFindRestSite

<mosqProbFindRestSite
    mean=double
    variance=double
  />

Documentation (element)

Probability that the mosquito escapes host and finds a resting place after biting

Documentation (type)

Parameters of a normal distribution, provided as mean and variance.

Attributes

mean

mean=double

Units: none

The mean of the beta distribution (must be in the open range (0,1)).

variance

variance=double

Units: none

The standard deviation of variates.

Probability of mosquito successfully resting after finding a resting site

→ scenario → entomology → vector → anopheles → mosq → mosqProbResting

<mosqProbResting
    mean=double
    variance=double
  />

Documentation (element)

Probability of mosquito successfully resting after finding a resting site

Documentation (type)

Parameters of a normal distribution, provided as mean and variance.

Attributes

mean

mean=double

Units: none

The mean of the beta distribution (must be in the open range (0,1)).

variance

variance=double

Units: none

The standard deviation of variates.

Probability of a mosquito successfully laying eggs given that it has rested

→ scenario → entomology → vector → anopheles → mosq → mosqProbOvipositing

<mosqProbOvipositing
    value=double
  />

Documentation (element)

Probability of a mosquito successfully laying eggs given that it has rested

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Human blood index

→ scenario → entomology → vector → anopheles → mosq → mosqHumanBloodIndex

<mosqHumanBloodIndex
    value=double
  />

Documentation (element)

Units: Proportion

The proportion of resting mosquitoes which fed on human blood during the last feed.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Mosquito life cycle parameters

→ scenario → entomology → vector → anopheles → lifeCycle

<lifeCycle
  [ estimatedLarvalResources=double ] DEFAULT VALUE 1e8
  >
IN ANY ORDER:
|   <eggStage ... /> 
|   <larvalStage ... /> 
|   <pupalStage ... /> 
|   <femaleEggsLaidByOviposit ... /> 
</lifeCycle>

eggStage
larvalStage
pupalStage
femaleEggsLaidByOviposit

Documentation (element)

Parameters describing the life-cycle of this species of mosquito

Attributes

Estimate of larval resources

estimatedLarvalResources=double

Units: X

Default value: 1e8

An estimate of mean annual availability of resources to larvae. Used to get the resource usage fitting algorithm going; if the algorithm fails to fit the resource availability then tweaking this parameter may help. In other cases tweaking this parameter shouldn't be necessary. Default value is 10⁸ (1e8). Units are arbitrary but must be the same as those used by the resourceUsage parameter.

Egg stage

→ scenario → entomology → vector → anopheles → lifeCycle → eggStage

<eggStage
    duration=int
    survival=double
  />

Documentation (element)

Parameters for the egg stage of development

Documentation (type)

Parameters associated with a mosquito development stage.

Attributes

Duration

duration=int

Units: Days

Duration of the stage (i.e. length of time mosquito is an egg/larva/pupa).

Probability of survival

survival=double

Units: Proportion

Probability that mosquito survives this size (probability of egg hatching, a larva becoming a pupa or a pupa emerging as an adult, at the start of that stage).

larvalStage

→ scenario → entomology → vector → anopheles → lifeCycle → larvalStage

<larvalStage>
</larvalStage>

daily

Documentation (type)

Parameters for the larval stage of development

Documentation (base type)

Parameters associated with a mosquito development stage.

Daily development

→ scenario → entomology → vector → anopheles → lifeCycle → larvalStage → daily

<daily
    resourceUsage=double
    effectCompetition=double
  />

Documentation (element)

List of parameters which apply during the larval stage of development. List length must equal stage duration, with first item corresponding to first 24 hours after hatching, second item to hours 24-48, and so on.

Attributes

Resource usage

resourceUsage=double

Units: X

Resource usage during larval stage of development. Units are arbitrary.

Effect of competition

effectCompetition=double

Units: none

Effect of competition over resources on development.

Pupal stage

→ scenario → entomology → vector → anopheles → lifeCycle → pupalStage

<pupalStage
    duration=int
    survival=double
  />

Documentation (element)

Parameters for the pupal stage of development

Documentation (type)

Parameters associated with a mosquito development stage.

Attributes

Duration

duration=int

Units: Days

Duration of the stage (i.e. length of time mosquito is an egg/larva/pupa).

Probability of survival

survival=double

Units: Proportion

Probability that mosquito survives this size (probability of egg hatching, a larva becoming a pupa or a pupa emerging as an adult, at the start of that stage).

Eggs laid by ovipositing mosquito

→ scenario → entomology → vector → anopheles → lifeCycle → femaleEggsLaidByOviposit

<femaleEggsLaidByOviposit
    value=double
  />

Documentation (element)

Units: Eggs per feeding cycle

The total number of female eggs laid by a female mosquito at the conclusion to a feeding cycle, after feeding on an unprotected human (non-human hosts and protected humans use a multiplication factor to adjust this number for mosquitoes feeding on them).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Simple Mosq-Pop-Dynamics parameters

→ scenario → entomology → vector → anopheles → simpleMPD

<simpleMPD>
IN ANY ORDER:
|   <developmentDuration ... /> 
|   <developmentSurvival ... /> 
|   <femaleEggsLaidByOviposit ... /> 
</simpleMPD>

developmentDuration
developmentSurvival
femaleEggsLaidByOviposit

Documentation (element)

Parameters describing the simple mosquito population dynamics model.

This is a simpler version of the life-cycle model, requiring less parameters and with much simpler initialisation.

Duration

→ scenario → entomology → vector → anopheles → simpleMPD → developmentDuration

<developmentDuration
    value=int
  />

Documentation (element)

Units: Days Min: 1

Duration from egg laying to emergence in days.

Attributes

Input parameter value

value=int

An integer value.

Probability of survival

→ scenario → entomology → vector → anopheles → simpleMPD → developmentSurvival

<developmentSurvival
    value=double
  />

Documentation (element)

Units: Proportion Min: 0 Max: 1

Probability that mosquito survives from the egg being laid to emergence, given no resouce limitations (no density constraints).

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Eggs laid by ovipositing mosquito

→ scenario → entomology → vector → anopheles → simpleMPD → femaleEggsLaidByOviposit

<femaleEggsLaidByOviposit
    value=double
  />

Documentation (element)

Units: Eggs per feeding cycle

The total number of female eggs laid by a female mosquito at the conclusion to a feeding cycle.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Alternative (non-human) host paramters

→ scenario → entomology → vector → anopheles → nonHumanHosts

<nonHumanHosts
    name=string
  >
IN ANY ORDER:
|   <mosqRelativeEntoAvailability ... /> 
|   <mosqProbBiting ... /> 
|   <mosqProbFindRestSite ... /> 
|   <mosqProbResting ... /> 
| [ <hostFecundityFactor ... /> ]
</nonHumanHosts>

mosqRelativeEntoAvailability
mosqProbBiting
mosqProbFindRestSite
mosqProbResting
hostFecundityFactor

Documentation (element)

Min: 0

Non human host parameters, per type of host (must match up with non-species-specific parameters).

Attributes

Identifier for this category of non-human hosts

name=string

Identifier for this category of non-human hosts

Relative availability of non-human host (ξ_i)

→ scenario → entomology → vector → anopheles → nonHumanHosts → mosqRelativeEntoAvailability

<mosqRelativeEntoAvailability
    value=double
  />

Documentation (element)

Units: Proportion

Relative availability of the population of non-human hosts of type i to other non-human hosts; the sum of this across all non-human hosts must be 1.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability of mosquito successfully biting host

→ scenario → entomology → vector → anopheles → nonHumanHosts → mosqProbBiting

<mosqProbBiting
    value=double
  />

Documentation (element)

Units: Proportion

Probability of mosquito successfully biting host

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability that the mosquito escapes host and finds a resting place after biting

→ scenario → entomology → vector → anopheles → nonHumanHosts → mosqProbFindRestSite

<mosqProbFindRestSite
    value=double
  />

Documentation (element)

Units: Proportion

Probability that the mosquito escapes host and finds a resting place after biting

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Probability of mosquito successfully resting after finding a resting site

→ scenario → entomology → vector → anopheles → nonHumanHosts → mosqProbResting

<mosqProbResting
    value=double
  />

Documentation (element)

Units: Proportion

Probability of mosquito successfully resting after finding a resting site

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Relative fecundity of biting mosquitoes

→ scenario → entomology → vector → anopheles → nonHumanHosts → hostFecundityFactor

<hostFecundityFactor
    value=double
  />

Documentation (element)

Units: Proportion

Multiplicative factor for the number of fertile eggs laid by a mosquito after biting this type of host, relative to an unprotected human.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

nonHumanHosts

→ scenario → entomology → vector → nonHumanHosts

<nonHumanHosts
    name=string
    number=double
  />

Attributes

Species of alternative host

name=string

Name of this species of non human hosts (must match up with those described per anopheles section).

Population size of non-human host species

number=double

Units: Animals

Population size of this non-human host. Note: the availability of the population of this type of non-human host is determined by mosqRelativeEntoAvailability and mosqHumanBloodIndex. NHHs are not modelled individually, thus this parameter is not used. It might be useful in the future if there is ever an intervention to change the number of non-human hosts.

Parasite genetics

→ scenario → parasiteGenetics

<parasiteGenetics
    samplingMode=("initial" or "tracking")
  >
IN THIS ORDER:
| ( <locus ... /> )+
</parasiteGenetics>

locus

Documentation (element)

A specification of genotypes of infection parasites.

May be omitted; in this case there is no modelling of genetic differences of infections (resistance, fitness).

Attributes

samplingMode

samplingMode=("initial" or "tracking")

This controls how genotypes are determined for new infections during the intervention period. Prior to this (in initialisation phases), genotypes are always sampled using the specified initial frequencies. Mode "initial" continues to sample genotypes using initial frequencies (i.e. independent of the success of parent generations of parasites). Mode "tracking" samples genotypes based on the success parent generations of parasites have in infecting mosquitoes, tracked per genotype. It is possible that in the future a recombination option will be added to this list, however designing a suitable model is not trivial.

Locus

→ scenario → parasiteGenetics → locus

<locus
    name=string
  >
IN THIS ORDER:
| ( <allele ... /> )+
</locus>

allele

Documentation (element)

Describes a locus, or a point at which an infection may vary. The genotype of an infection is determined by choosing one allele at each locus. Initial frequencies of alleles are specified independently for each locus, but subsequent infections are selected according to success of genotypes.

Alleles at loci can affect fitness and resistance to any number of drugs.

Attributes

Name of locus

name=string

Name of the Locus

Allele

→ scenario → parasiteGenetics → locus → allele

<allele
    name=string
    initialFrequency=double
    fitness=double
  />

Documentation (element)

Describes an allele, or one possible genetic option of multiple at one point of variance.

Attributes

Name

name=string

Name of the allele; used to refer to it elsewhere.

Initial frequency

initialFrequency=double

Specification of how commonly this allele occurs during warmup relative to other alleles of the same locus. During the simulation's initialisation phases, the frequency at which each allele of each locus occurs is fixed. After the initialisation phase, frequency of alleles is modelled as an emergent property of the success of genotypes.

Fitness factor

fitness=double

Fitness factor of the allele. This is multiplication factor used to speed up or slow down replication of parasites. For example, if a genotype has an allele with a fitness factor of 1 at one locus and another allele with a fitness factor of 0.8 at a second locus, then the parasites with the genotype will replicate 20% slower than the baseline.

Drug parameters (PK, PD and usage)

→ scenario → pharmacology

<pharmacology>
IN THIS ORDER:
|   <treatments ... /> 
|   <drugs ... /> 
</pharmacology>

treatments
drugs

Documentation (element)

Drug model parameters and drug usage parameters

Documentation (type)

A library of drug related data for the PK/PD model.

Treatments library

→ scenario → pharmacology → treatments

<treatments>
IN THIS ORDER:
| ( <schedule ... /> )+
| ( <dosages ... /> )+
</treatments>

schedule
dosages

Documentation (element)

A library of drug deployment schedules and dosages.

schedule

→ scenario → pharmacology → treatments → schedule

<schedule
    name=string
  >
IN THIS ORDER:
| ( <medicate ... /> )*
</schedule>

medicate

Documentation (type)

A schedule for the administration of drugs in a course of treatment.

Note that dose sizes are multiplied by some multiplier (see dosages) and the times of all doses may be delayed.

Attributes

Name

name=string

Name for referring to this deployment schedule

medicate

→ scenario → pharmacology → treatments → schedule → medicate

<medicate
    drug=string
    mg=double
    hour=double
  />

Attributes

drug

drug=string

Abbreviated name of drug compound

Drug dose (mg with multiplier)

mg=double

Units: mg per something

Quantity of drug compound in mg per something. A separate dosage table must be used when medicating, which may specify multipliers of this number based on patient age or weight.

Time of administration

hour=double

Units: Hours Min: 0

Number of hours past start of timestep this drug dose is administered at (first dose should be at hour 0).

dosages

→ scenario → pharmacology → treatments → dosages

<dosages
    name=string
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| | ( <age ... /> )+
| | ( <bodymass ... /> )+
| |   <multiply ... /> 
</dosages>

age
bodymass
multiply

Documentation (type)

A table for selecting a dose size. There are several ways this can work: using the patient's age or body mass in a look-up table to get a multplier, or directly using body mass as the multiplier.

The doses specified in "mg" in the treatment schedule are then multiplied by this multiplier.

Attributes

Name

name=string

Name for referring to this dosage table

Look-up table (age)

→ scenario → pharmacology → treatments → dosages → age

<age
    lowerbound=double
    dose_mult=double
  />

Documentation (element)

Select dose multiplier from a look-up table using the patient's age.

Documentation (type)

A look-up table which uses patient age (in years) or weight (in kg) to find a multiplier.

Attributes

Lower bound (inclusive)

lowerbound=double

Units: years or kg Min: 0

Dose multiplier

dose_mult=double

Min: 0

The dose size given in the schedule (in "mg") is multiplied by this value for patients falling into this range when this dosage table is used.

Look-up table (weight)

→ scenario → pharmacology → treatments → dosages → bodymass

<bodymass
    lowerbound=double
    dose_mult=double
  />

Documentation (element)

Select dose multiplier from a look-up table using the patient's body mass.

Documentation (type)

A look-up table which uses patient age (in years) or weight (in kg) to find a multiplier.

Attributes

Lower bound (inclusive)

lowerbound=double

Units: years or kg Min: 0

Dose multiplier

dose_mult=double

Min: 0

The dose size given in the schedule (in "mg") is multiplied by this value for patients falling into this range when this dosage table is used.

Multiply dose

→ scenario → pharmacology → treatments → dosages → multiply

<multiply
    by=("kg")
  />

Documentation (element)

Multiply the dose by some quantity, such as patient weight.

Attributes

By what?

by=("kg")

Quantity to multiply the dose by. Only option is "kg" (patient weight in kg).

Drug library

→ scenario → pharmacology → drugs

<drugs>
IN THIS ORDER:
| ( <drug ... /> )+
</drugs>

drug

Documentation (element)

A library of drug PK/PD data.

drug

→ scenario → pharmacology → drugs → drug

<drug
    abbrev=string
  >
IN THIS ORDER:
|   <PD ... /> 
|   <PK ... /> 
</drug>

Documentation (type)

A drug description with PK/PD parameters.

Attributes

abbrev

abbrev=string

PD

→ scenario → pharmacology → drugs → drug → PD

<PD
  [ locus=string ]
  >
IN THIS ORDER:
| ( <phenotype ... /> )+
</PD>

phenotype

Attributes

Locus

locus=string

Optional; if present specifies the locus corresponding to this drug's PD phenotypes: each phenotype must then match one of that locus's alleles. Otherwise the drug should specify only one phenotype. There is currently a one-to-many correspondance between loci and drugs.

PD parameters for some allele / resistance phenotype

→ scenario → pharmacology → drugs → drug → PD → phenotype

<phenotype
  [ name=string ]
  >
IN THIS ORDER:
| ( <restriction ... /> )*
|   <max_killing_rate ... /> 
|   <IC50 ... /> 
|   <slope ... /> 
</phenotype>

restriction
max_killing_rate
IC50
slope

Documentation (element)

Pharmaco-Dynamic parameters for some resistance phenotype.

To model resistance to this drug, describe multiple infection phenotypes (with respect to these PD parameters) and list one or more "restrict" elements for each phenotype.

Loci are specified elsewhere. Multiple loci may influence the action of a single drug and each locus may influence multiple drugs.

Attributes

Name of phenotype

name=string

Name of the phenotype; for documentation use only.

Restrict phenotype applicability to certain alleles

→ scenario → pharmacology → drugs → drug → PD → phenotype → restriction

<restriction
    onLocus=string
    toAllele=string
  />

Documentation (element)

Specifies the mapping from genotype to phenotype. For each drug type, if only one phenotype is present, restrictions need not be specified, but otherwise restrictions must be specified.

The set of loci affecting phenotypes of this drug's action must be fixed for any drug type. Each phenotype must list, for each of these loci, a restriction to one or more alleles under the locus.

Attributes

Locus relevant to the mapping of alleles to this phenotype

onLocus=string

A locus under which only a restricted set of alleles map to this phenotype.

Alleles mapping to this phenotype

toAllele=string

One allele of a locus upon which phenotype choice depends. If multiple alleles under this locus should map to the same phenotype, repeat the whole "restriction onLocus..." element.

Maximal parasite killing rate

→ scenario → pharmacology → drugs → drug → PD → phenotype → max_killing_rate

<max_killing_rate>
    double
</max_killing_rate>

Documentation (element)

Units: 1/days Min: 0

k1 — Maximal parasite killing rate.

IC50

→ scenario → pharmacology → drugs → drug → PD → phenotype → IC50

<IC50
  [ sigma=double ] DEFAULT VALUE 0
  >
    double
</IC50>

Documentation (element)

Units: mg/l Min: 0

Half maximal effect concentration. If sigma > 0, the IC50 is sampled for each infection from a log-normal distribution with mean of this value and the sigma value specified, i.e. X ~ log N( log(mean) - s^2 / 2, s^2 ) .

Attributes

Sigma parameter for per-infection variation of IC50

sigma=double

Default value: 0

Distribution parameter describing per-infection variation of IC50. If zero or not specified, the IC50 is not sampled. See documentation of parent element.

Slope of effect curve

→ scenario → pharmacology → drugs → drug → PD → phenotype → slope

<slope>
    double
</slope>

Documentation (element)

Units: dimensionless

n — Slope of the concentration effect curve

PK

→ scenario → pharmacology → drugs → drug → PK

<PK>
IN THIS ORDER:
|   <negligible_concentration ... /> 
| EXACTLY ONE OF:
| |   <half_life ... /> 
| | IN THIS ORDER:
| | |   <k ... /> 
| | |   <m_exponent ... /> 
| [ <k_a ... /> ]
| [ <conversion ... /> ]
|   <vol_dist ... /> 
| [ <compartment2 ... /> ]
| [ <compartment3 ... /> ]
</PK>

negligible_concentration
half_life
k
m_exponent
k_a
conversion
vol_dist
compartment2
compartment3

Drug concentration considered negligible

→ scenario → pharmacology → drugs → drug → PK → negligible_concentration

<negligible_concentration>
    double
</negligible_concentration>

Documentation (element)

Units: mg/l Min: 0

Concentration below which drug's effects are deemed negligible and can be removed from simulation.

drug half-life

→ scenario → pharmacology → drugs → drug → PK → half_life

<half_life>
    double
</half_life>

Documentation (element)

Units: days Min: 0

Used to calculate elimination rate λ, calculated as λ = ln(2) / half_life. The basic form of decay is C(t) = C0 * exp(-λ*t).

Alternatively, elimination rate can be specified via k and m_exponent.

Constant associated with elimination rate (k)

→ scenario → pharmacology → drugs → drug → PK → k

<k
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Units: day^-1 Min: 0

Constant used to calculate the elimination rate λ, which is calculated as λ = k / (body_mass ^ m_exponent), where body_mass is the patient's weight in kg and m_exponent is the next parameter. The basic form of decay is C(t) = C0 * exp(-λ*t).

If sigma > 0, k is sampled per-human from the log-normal distribution: ln N( ln(mean) - σ^2 / 2, σ^2).

Alternatively, elimination rate can be specified via half_life.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Constant associated with elimination rate (m_exponent)

→ scenario → pharmacology → drugs → drug → PK → m_exponent

<m_exponent>
    double
</m_exponent>

Documentation (element)

Units: day^-1 Min: 0

Constant used to calculate the elimination rate λ, which is calculated as λ = k / (body_mass ^ m_exponent), where body_mass is the patient's weight in kg and k is the previous parameter. The basic form of decay is C(t) = C0 * exp(-λ*t).

Alternatively, elimination rate can be specified via half_life.

Note that in the case of a conversion model, this applies to both the elimination and the conversion rates.

Absorption rate constant (k_a)

→ scenario → pharmacology → drugs → drug → PK → k_a

<k_a
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Min: 0

Absorption rate parameter. Not allowed for one compartment models, but required for two and three compartment models and one compartment with conversion model (for the parent drug only).

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Conversion parameters (parent drug)

→ scenario → pharmacology → drugs → drug → PK → conversion

<conversion>
IN ANY ORDER:
|   <metabolite ... /> 
|   <rate ... /> 
|   <molRatio ... /> 
</conversion>

metabolite
rate
molRatio

Documentation (element)

Configures the parent drug in a conversion model.

To use a conversion model, the parent drug should have this section defined as well as half-life or k (direct elimination; this may be zero) and k_a (absorption rate; this may be large).

The metabolite drug should define half-life or k (elimination of metabolite), but not k_a (absorption rate) or this section (conversion). It is not possible for the metabolite to itself undergo conversion with the current models.

Metabolite drug (abbreviation)

→ scenario → pharmacology → drugs → drug → PK → conversion → metabolite

<metabolite>
    string
</metabolite>

Documentation (element)

The abbreviation of the metabolite drug (e.g. "DHA" or "DHA_AR").

Rate of conversion

→ scenario → pharmacology → drugs → drug → PK → conversion → rate

<rate
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Rate of conversion of parent drug to metabolite.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Molecular weight ratio

→ scenario → pharmacology → drugs → drug → PK → conversion → molRatio

<molRatio>
    double
</molRatio>

Documentation (element)

Ratio of molecular weights: molecular weight of the metabolite divided by molecular weight of the parent.

Volume of Distribution (Vd)

→ scenario → pharmacology → drugs → drug → PK → vol_dist

<vol_dist
  [ sigma=double ] DEFAULT VALUE 0
  >
    double
</vol_dist>

Documentation (element)

Units: l/kg Min: 0

Volume of Distribution

Attributes

Sigma parameter for per-human variation of Vd

sigma=double

Default value: 0

Distribution parameter describing per-human variation of volume of distribution. If zero or not specified, the parameter is not sampled. See documentation of parent element.

Second compartment parameters

→ scenario → pharmacology → drugs → drug → PK → compartment2

<compartment2>
IN ANY ORDER:
|   <a12 ... /> 
|   <a21 ... /> 
</compartment2>

Documentation (element)

Optional element specifying conversion parameters to- and from- a second compartment.

Absorption rate to compartment 2 (a12)

→ scenario → pharmacology → drugs → drug → PK → compartment2 → a12

<a12
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Units: day^-1 Min: 0

Absorption rate from the central compartment to the first periphery compartment (2). The parameter k12 = a12 / m where m is the body mass (kg).

It is sampled per-patient when sigma > 0.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Absorption rate from compartment 2 (a21)

→ scenario → pharmacology → drugs → drug → PK → compartment2 → a21

<a21
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Units: day^-1 Min: 0

Absorption rate from the first periphery compartment (2) to the central compartment. The parameter k21 = a21 / m where m is the body mass (kg).

It is sampled per-patient when sigma > 0.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Third compartment parameters

→ scenario → pharmacology → drugs → drug → PK → compartment3

<compartment3>
IN ANY ORDER:
|   <a13 ... /> 
|   <a31 ... /> 
</compartment3>

Documentation (element)

Optional element specifying conversion parameters to- and from- a third compartment.

Absorption rate to compartment 3 (a13)

→ scenario → pharmacology → drugs → drug → PK → compartment3 → a13

<a13
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Units: day^-1 Min: 0

Absorption rate from the central compartment to the second periphery compartment (3). The parameter k13 = a13 / m where m is the body mass (kg).

It is sampled per-patient when sigma > 0.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Absorption rate from compartment 3 (a31)

→ scenario → pharmacology → drugs → drug → PK → compartment3 → a31

<a31
    mean=double
  [ cv=double ]
    distr=("lnorm" or "const")
  />

Documentation (element)

Units: day^-1 Min: 0

Absorption rate from the second periphery compartment (3) to the central compartment. The parameter k31 = a31 / m where m is the body mass (kg).

It is sampled per-patient when sigma > 0.

Documentation (type)

Parameters of some distribution. The mean is that provided and the standard deviation is cv*mean.

Log-normal: σ = cv * mean ; μ = ln(mean) - σ² / 2 ; X ~ ln N( μ, σ² ) ; equivalent R sample: rlnorm(1, log(mean) - ((cv * mean)^2) / 2, cv * mean).

Attributes

mean

mean=double

Units: (same as base units)

The mean of the distribution.

Coefficient of variance

cv=double

Units: unitless

Coefficient of variance (mean * cv gives standard deviation). Must be specified when distribution is not constant.

Distribution

distr=("lnorm" or "const")

Selects the distribution to use. const: constant (no distribution). Setting cv=0 has the same behaviour. lnorm: log-normal distribution

Diagnostic parameters

→ scenario → diagnostics

<diagnostics>
IN THIS ORDER:
| ( <diagnostic ... /> )*
</diagnostics>

diagnostic

Documentation (element)

Diagnostic model parameters

diagnostic

→ scenario → diagnostics → diagnostic

<diagnostic
    name=string
  [ units=("Other" or "Garki" or "Malariatherapy") ]
  >
IN THIS ORDER:
| EXACTLY ONE OF:
| |   <deterministic ... /> 
| |   <stochastic ... /> 
</diagnostic>

deterministic
stochastic

Attributes

Name of diagnostic

name=string

Name of this diagnostic (parameterisation). May be used elsewhere in the XML document to refer to this set of diagnostic parameters.

Parasite density units / methodology

units=("Other" or "Garki" or "Malariatherapy")

Parasite densities, as estimated according to standard microscopy methods, the Garki method, and as derived from Malariatherapy data are not equivalent. Internally, a "bias" factor is used to convert values estimated by one methods to values comparable with another (see AJTMHv75 supplement 2 pp20-21). This option allows specification of which methodology the density given in the diagnostic specification is measured with. Values allowed are: Malariatherapy, Garki and Other. If not specified, Other is assumed, unless the GARKI_DENSITY_BIAS model option is used, in which case this option must be specified.

Deterministic detection

→ scenario → diagnostics → diagnostic → deterministic

<deterministic
    minDensity=double
  />

Documentation (element)

Specify that an artificial deterministic test is used: outcome is positive if parasite density is at least the minimum given.

Attributes

Minimum detectible density

minDensity=double

Units: parasites/microlitre Min: 0

The minimum density at which parasites can be detected. If 0, the test outcome is always positive.

Non-deterministic detection

→ scenario → diagnostics → diagnostic → stochastic

<stochastic
    dens_50=double
    specificity=double
  />

Documentation (element)

An improved model of detection which is non-deterministic, including false positive results as well as false negatives.

The probability of a positive outcome is modelled as 1 + s×(x/(x+d) - 1) where x is the parasite density, d is the density at which the test outcome has a 50% chance of being positive, and s is the probability of a positive outcome given no parasites (the specificity).

Some parameterisations:

Microscopy sensitivity/specificity data in Africa; Source: expert opinion — Allan Schapira dens_50 = 20.0 specificity = .75

RDT sensitivity/specificity for Plasmodium falciparum in Africa Source: Murray et al (Clinical Microbiological Reviews, Jan. 2008) dens_50 = 50.0; specificity = .942;

Attributes

Density 50

dens_50=double

Units: parasites/microlitre Min: 0

The density at which the test outcome has a 50% chance of being positive.

Specificity

specificity=double

Units: Dimensionless Min: 0 Max: 1

The probability of a positive test outcome in the absense of parasites.

Model options and parameters

→ scenario → model

<model>
IN ANY ORDER:
|   <ModelOptions ... /> 
|   <clinical ... /> 
|   <human ... /> 
| [ <vivax ... /> ]
|   <parameters ... /> 
</model>

ModelOptions
clinical
human
vivax
parameters

Documentation (element)

Encapsulation of all parameters which describe the model according to which fitting is done.

Model Options

→ scenario → model → ModelOptions

<ModelOptions>
IN THIS ORDER:
| ( <option ... /> )*
</ModelOptions>

option

Documentation (element)

All model options (bug fixes, choices between models, etc.).
The list of recognised options can be found in the code at: model/util/ModelOptions.h and should also be in the wiki.

clinical

→ scenario → model → clinical

<clinical
    healthSystemMemory=string
  >
IN ANY ORDER:
| [ <NeonatalMortality ... /> ]
| [ <NonMalariaFevers ... /> ]
</clinical>

NeonatalMortality
NonMalariaFevers

Documentation (type)

Description of clinical parameters that are related to the health-system description, but which contain data that cannot be changed as part of an intervention and that are not restricted to treatment.

Attributes

Follow-up period during which recurrence is considered a treatment failure

healthSystemMemory=string

Units: User-defined (defaults to steps)

Follow-up period during which a recurrence is considered to be a treatment failure Can be specified in steps (e.g. 6t) or days (e.g. 28d).

Neonatal mortality parameters

→ scenario → model → clinical → NeonatalMortality

<NeonatalMortality
    diagnostic=string
  />

Attributes

Diagnostic used to parameterise model

diagnostic=string

The name of a diagnostic used to parameterise the model. Neonatal mortality is derived from malaria patency of a certain sub-population of humans. This is the diagnostic used to asses patency for this purpose. If this is not specified, the monitoring diagnostic is used.

NonMalariaFevers

→ scenario → model → clinical → NonMalariaFevers

<NonMalariaFevers>
IN THIS ORDER:
|   <incidence ... /> 
| [ <prNeedTreatmentNMF ... /> ]
| [ <prNeedTreatmentMF ... /> ]
</NonMalariaFevers>

incidence
prNeedTreatmentNMF
prNeedTreatmentMF

Documentation (type)

Description of the incidence of non-malaria fever. Non-malaria fevers are only modelled if the NON_MALARIA_FEVERS option is used.

P(NMF)

→ scenario → model → clinical → NonMalariaFevers → incidence

<incidence
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</incidence>

group

Documentation (element)

Units: Dimensionless Min: 0.0 Max: 1.0

Probability that a non-malaria fever occurs given that no concurrent malaria fever occurs.

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

P(need treatment | NMF)

→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentNMF

<prNeedTreatmentNMF
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentNMF>

group

Documentation (element)

Units: Dimensionless Min: 0 Max: 1

Probability that a non-malarial fever requires treatment with antibiotics (assuming fever is not induced by malaria, although concurrent parasites may be present).

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

P(need treatment | MF)

→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentMF

<prNeedTreatmentMF
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentMF>

group

Documentation (element)

Units: Dimensionless Min: 0 Max: 1

Probability that a malaria fever needs treatment with antibiotics (assuming fever is induced by malaria, although concurrent bacteria may be present).

Meaning partially overlaps with separate model for comorbidity given malaria.

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

human

→ scenario → model → human

<human>
IN THIS ORDER:
|   <availabilityToMosquitoes ... /> 
| [ <weight ... /> ]
</human>

availabilityToMosquitoes
weight

Documentation (type)

Parameters of host models.

Availability to mosquitoes

→ scenario → model → human → availabilityToMosquitoes

<availabilityToMosquitoes
  [ interpolation=("none" or "linear") ]
  >
IN THIS ORDER:
| ( <group ... /> )+
</availabilityToMosquitoes>

group

Documentation (element)

Units: None Min: 0 Max: 1

Availability of humans to mosquitoes relative to an adult, categorized by age group

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

Weight

→ scenario → model → human → weight

<weight
  [ interpolation=("none" or "linear") ]
    multStdDev=double
  >
IN THIS ORDER:
| ( <group ... /> )+
</weight>

group

Documentation (element)

Units: kg Min: 0

By age group data on human weight (mass).

Attributes

interpolation

interpolation=("none" or "linear")

none: input values are used directly
linear: straight lines (on an age vs. value graph) are used to interpolate data points.

Standard deviation

multStdDev=double

Units: None Min: 0

Each human is assigned a weight multiplier from a normal distribution with mean 1 and this standard deviation at birth. His/her weight is this multiplier times the mean from age distribution. A standard deviation of zero for no heterogeneity is valid; a rough value from Tanzanian data is 0.14.

Vivax model parameters

→ scenario → model → vivax

<vivax>
IN ANY ORDER:
|   <probBloodStageInfectiousToMosq ... /> 
|   <hypnozoiteRelease ... /> 
|   <bloodStageProtectionLatency ... /> 
|   <bloodStageLengthDays ... /> 
|   <clinicalEvents ... /> 
</vivax>

probBloodStageInfectiousToMosq
hypnozoiteRelease
bloodStageProtectionLatency
bloodStageLengthDays
clinicalEvents

Documentation (element)

This describes Vivax model parameters, and is required when using the VIVAX_SIMPLE_MODEL model option.

Probability of mosquito infection

→ scenario → model → vivax → probBloodStageInfectiousToMosq

<probBloodStageInfectiousToMosq
    value=double
  />

Documentation (element)

Units: None Min: 0 Max: 1

The chance of a feeding mosquito becoming infected, given that the host is patent. (This may be adjusted by transmission-blocking vaccines.)

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Hypnozoite releases

→ scenario → model → vivax → hypnozoiteRelease

<hypnozoiteRelease
  [ pSecondRelease=double ] DEFAULT VALUE 0
  >
IN ANY ORDER:
|   <numberHypnozoites ... /> 
|   <firstRelease ... /> 
| [ <secondRelease ... /> ]
</hypnozoiteRelease>

numberHypnozoites
firstRelease
secondRelease

Documentation (element)

Describes the number and times of hypnozoite releases.

Documentation (type)

This element defines probabilites when and how many hypnozoites are released from the liverstage into the blood.

The gap between the start of a new brood of hypnozoites and its release are defined as follows:

latentP + latentRelapseDays + randomReleaseDelay

randomReleaseDelay is based on one or two lognormal distributions, which are defined in firstRelease and optionally secondRelease.

You can define 2 release distributions, which get added together and represent the probability of hypnozoites which get released before winter (first release) or after (second release).

You can omit the secondRelease element if no release to the blood happens after winter.

Attributes

latent relapse days

pSecondRelease=double

Default value: 0

Probability of a second release. If undefined it is zero.

Number of Hypnozoites

→ scenario → model → vivax → hypnozoiteRelease → numberHypnozoites

<numberHypnozoites
    max=int
    base=double
  />

Documentation (element)

numberHypnozoites calculates the number of hypnozoites in the liver stage based on a base which is between 0 and 1.

This number is random based on the following distribution and normalized:

max ∑ (base ^ n) n = 0

Attributes

max

max=int

base

base=double

firstRelease

→ scenario → model → vivax → hypnozoiteRelease → firstRelease

<firstRelease
    mu=double
    sigma=double
    latentRelapseDays=int
  />

Documentation (base type)

Parameters of a normal distribution.

Variates are sampled as: X ~ N( mu, sigma² ).

Attributes

mu

mu=double

Units: (same as base units)

The mean of the normal distribution.

sigma

sigma=double

Units: (same as base units)

The standard deviation of variates.

latent relapse days

latentRelapseDays=int

Usually 15 days or 10 days (3 or 2 5-day timesteps).

secondRelease

→ scenario → model → vivax → hypnozoiteRelease → secondRelease

<secondRelease
    mu=double
    sigma=double
    latentRelapseDays=int
  />

Documentation (base type)

Parameters of a normal distribution.

Variates are sampled as: X ~ N( mu, sigma² ).

Attributes

mu

mu=double

Units: (same as base units)

The mean of the normal distribution.

sigma

sigma=double

Units: (same as base units)

The standard deviation of variates.

latent relapse days

latentRelapseDays=int

Usually 15 days or 10 days (3 or 2 5-day timesteps).

Blood stage protection latency

→ scenario → model → vivax → bloodStageProtectionLatency

<bloodStageProtectionLatency
    value=double
  />

Documentation (element)

Min: 0

The length of time after expiry of a blood-stage infection during which relapses from the same brood are supressed by the immune system.

This is rounded to the nearest time-step.

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Blood stage length

→ scenario → model → vivax → bloodStageLengthDays

<bloodStageLengthDays
    weibullScale=double
    weibullShape=double
  />

weibullScale=double

weibullShape

weibullShape=double

clinicalEvents

→ scenario → model → vivax → clinicalEvents

<clinicalEvents>
IN THIS ORDER:
|   <pPrimaryInfection ... /> 
|   <pRelapseOne ... /> 
|   <pRelapseTwoPlus ... /> 
|   <pEventIsSevere ... /> 
</clinicalEvents>

pPrimaryInfection
pRelapseOne
pRelapseTwoPlus
pEventIsSevere

Documentation (type)

This elements holds all information about probabilites for clinical events from infections and relapses.

pPrimaryInfection

→ scenario → model → vivax → clinicalEvents → pPrimaryInfection

<pPrimaryInfection
    a=double
    b=double
  />

Attributes

a

a=double

b

b=double

pRelapseOne

→ scenario → model → vivax → clinicalEvents → pRelapseOne

<pRelapseOne
    a=double
    b=double
  />

Attributes

a

a=double

b

b=double

pRelapseTwoPlus

→ scenario → model → vivax → clinicalEvents → pRelapseTwoPlus

<pRelapseTwoPlus
    a=double
    b=double
  />

Attributes

a

a=double

b

b=double

pEventIsSevere

→ scenario → model → vivax → clinicalEvents → pEventIsSevere

<pEventIsSevere
    value=double
  />

Attributes

Input parameter value

value=double

A double-precision floating-point value.

Parameters of the model of epidemiology

→ scenario → model → parameters

<parameters
    interval=int
    iseed=int
    latentp=string
  >
IN THIS ORDER:
| ( <parameter ... /> )+
</parameters>

parameter

interval=int

Units: Days

Simulation step

Random number seed

iseed=int

Units: Number

Seed for RNG

Pre-erythrocytic latent period

latentp=string

Units: User defined (default: steps) Min: 0 Max: 20

Pre-erythrocytic latent period Can be specified in steps (e.g. 3t) or days (e.g. 15d).

parameter

→ scenario → model → parameters → parameter

<parameter
  [ name=string ]
    number=int
    value=double
  [ include=boolean ]
  />

Attributes

Name of parameter

name=string

Units: string

Name of parameter

Parameter number

number=int

Units: Number Min: 1 Max: 100

Reference number of input parameter

Parameter value

value=double

Units: Number Min: 0

Parameter value

Sampling indicator

include=boolean

Units: Number Min: 0 Max: 1

True if parameter is to be sampled in optimization runs. Not used in simulator app.