Schema 27 documentation
Generated from: scenario_27.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"
xsi:noNamespaceSchemaLocation="scenario_27.xsd"
>
IN THIS ORDER:
| <demography ... />
| <monitoring ... />
| <interventions ... />
| <healthSystem ... />
| <entoData ... />
| [ <drugDescription ... /> ]
| <model ... />
</scenario>
Documentation (element)
Units: List of elements
Description of scenario
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
Name of intervention
name=string
Units: string
Name of intervention
Work unit identifier
wuID=int
Units: Number Min: 1 Max: 100000000
Work unit ID. Only used to validate checkpointing, to prevent checkpoint cheats.
Human age distribution
→ scenario → demography
<demography
name=string
popSize=int
maximumAgeYrs=double
[ growthRate=double ]
>
IN THIS ORDER:
| <ageGroup ... />
</demography>
Documentation (element)
Units: List of elements
Description of demography
Attributes
Name of demography data
name=string
Units: string
Name of demography data
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>
Documentation (element)
Units: List of elements
List of age groups included in demography
Documentation (type)
Units: List of elements
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
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
[ cohortOnly=boolean ]
[ firstBoutOnly=boolean ]
[ firstTreatmentOnly=boolean ]
[ firstInfectionOnly=boolean ]
>
IN THIS ORDER:
| [ <continuous ... /> ]
| <SurveyOptions ... />
| <surveys ... />
| <ageGroup ... />
</monitoring>
Documentation (element)
Units: List of elements
Description of surveys
Attributes
Name of monitoring information
name=string
Units: string
Name of monitoring data
Survey only cohort
cohortOnly=boolean
If true, for many output measures, the output comes only from individuals in the cohort; otherwise output is from the entire population. Does not need to be specified if no cohort-selecting "interventions" are present.
Time to first episode only
firstBoutOnly=boolean
If true, remove individuals from the cohort at the start of the first episode (start of a clinical bout) since they were recruited into the cohort. This is intended for cohort studies that intend to measure time to first episode, using active case detection.
Time to first treatment only
firstTreatmentOnly=boolean
If true, remove individuals from the cohort when they first seek treatment since they were recruited into the cohort. This is intended for cohort studies that intend to measure time to first episode, using passive case detection.
Time to first infection only
firstInfectionOnly=boolean
If true, remove individuals from the cohort at completion of the first survey in which they present with a patent infection since they were recruited into the cohort. This intended for cohort studies that intend to measure time to first infection, using active case detection.
continuous
→ scenario → monitoring → continuous
<continuous
period=int
[ duringInit=boolean ]
>
IN THIS ORDER:
| ( <option ... /> )*
</continuous>
Attributes
Days between reports
period=int
Units: Days Min: 1 Max: unbounded
Number of timesteps between reports.
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
Model options required
name=string
Options define different model structures. Option name. Must be one of a strictly defined set. Options are not required to be listed if their default value is desired.
Indicator of whether option is required
value=boolean
Default value: true
Option value (true/false). Each option has a default value used if the option is not listed (usually false but sometimes true).
Name of quantity
→ scenario → monitoring → SurveyOptions
<SurveyOptions>
IN THIS ORDER:
| ( <option ... /> )*
</SurveyOptions>
Documentation (element)
List of all active survey options. See include/Survey.h for a list of supported outputs. Should also be on the wiki.
Survey times (time steps)
→ scenario → monitoring → surveys
<surveys
detectionLimit=double
>
IN THIS ORDER:
| ( <surveyTime ... /> )+
</surveys>
Documentation (element)
Units: List of elements
List of survey times
Attributes
Detection limit for parasitaemia
detectionLimit=double
Units: parasites/micolitre Min: 0 Max: 1000
Detection limit for parasitemia
Survey time
→ scenario → monitoring → surveys → surveyTime
<surveyTime>
int
</surveyTime>
Documentation (element)
Units: Number Min: 0
Survey time; 0 means just before start of main sim and is a valid survey-point. Reported data is either from a point-time survey (immediate data) or is collected over the previous year (data from previous timesteps-per-year period). Simulation will end immediately after last survey is taken.
Age groups
→ scenario → monitoring → ageGroup
<ageGroup
lowerbound=double
>
IN THIS ORDER:
| ( <group ... /> )+
</ageGroup>
Documentation (element)
Units: List of elements
List of age groups included in demography or surveys
Documentation (type)
Units: List of elements
List of age groups included in 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
Preventative interventions
<interventions
name=string
>
IN THIS ORDER:
| <descriptions ... />
| [ <continuous ... /> ]
| [ <timed ... /> ]
</interventions>
Documentation (element)
Units: List of elements
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
Intervention
name=string
Units: string
Name of age specific intervention
descriptions
→ scenario → interventions → descriptions
<descriptions>
IN THIS ORDER:
| [ <MDADescription ... /> ]
| ( <vaccineDescription ... /> ){0,3}
| [ <iptiDescription ... /> ]
| [ <ITNDecay ... /> ]
| [ <IRSDecay ... /> ]
| [ <VADecay ... /> ]
| ( <anopheles ... /> )*
</descriptions>
Documentation (element)
Descriptions applying across all uses of an intervention.
Description of MDA
→ scenario → interventions → descriptions → MDADescription
<MDADescription>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</MDADescription>
Documentation (element)
Description of treatment type used by mass treatment option of one-day case management model. Can be used to describe one-size-fits-all mass drug dosing, age-based mass drug dosing and screen-and-treat. Number of treatments given can be reported by the nMDAs option.
Documentation (type)
Units: List of elements
A set of decisions and a set of treatments.
decisions
→ scenario → interventions → descriptions → MDADescription → decisions
<decisions>
IN THIS ORDER:
| ( <decision ... /> )*
</decisions>
Documentation (type)
Description of decisions for a case management tree. A generic view of this tree would be that each decision is deterministic, or based on probabilities which may depend on other decisions. Probabilistic decisions are described here.
In general, each decision has a name, a defined set of outcome values, and a set of dependent decisions mentioned by name. The following decisions, with their associated outcomes, are provided by the code (and may not be included here):
- case (uncomplicated only): Returns "UC1" if there is no recent history of a malarial case, or "UC2" if there is.
- result: Dependent on decision "test", this performs a parasite density test. Output is one of "none" (no test performed), "positive", "negative".
The following decisions must be described here:
- test (uncomplicated only): Outputs must be "none", "microscopy" or "RDT" to describe which test the "result" decision uses.
- treatment: Describes which treatment to use. Values aren't restricted but must match up with a treatment described in the corresponding "treatments" section.
- hospitalisation (complicated only): none, immediate or delayed.
decision
→ scenario → interventions → descriptions → MDADescription → decisions → decision
<decision
name=string
depends=string
values=string
>
string
</decision>
Documentation (type)
A decision describes how to choose between a set of values.
Lexically, it can contain symbols matching "[_.a-zA-Z0-9]+", round brackets: (), braces: {} and colons. Whitespace is ignored except to separate symbols.
Syntactically, it must match one TREE, where SYMBOL is a symbol described above. (Here, "x|y" means x or y, "x+" means x occurs once or more, brackets show grouping.) TREE := BRANCH_SET | OUTCOME BRANCH_SET := BRANCH+ BRANCH := DECISION '(' VALUE ')' ( ':' OUTCOME | '{' TREE '}' ) OUTCOME, DECISION, VALUE := SYMBOL
For each BRANCH_SET each BRANCH must have the same DECISION. This DECISION must be one of the dependencies mentioned in "depends". This may be:
- another decision, in which case the VALUE immediately following in brackets must correspond to one of its output values. The BRANCH_SET immediately containing this BRANCH must represent each output value of the same decision exactly once, and no parent BRANCH_SET may be for the same DECISION.
- "p": this indicates a probabilistic decision. In this case the value is a probability, the sum of all values for the BRANCH_SET must be 1 and the decision must be associated directly with OUTCOMEs (not sub-TREEs).
- "age": this indicates an age-test. The VALUE must have the form "a-b", indicating that this branch will be taken for individuals aged such that a <= age < b, where a,b are non-negative real numbers or the special value "inf", and a <= b. All VALUEs in the BRANCH_SET must cover all possible (non-negative real) ages, with no overlap (hence, smallest a must be 0 and greatest b must be inf).
Semantically, each OUTCOME must be one of the values associated with this decision.
Attributes
Name of decision
name=string
The name of this decision. The name must match the regular expression "[_a-zA-Z0-9]+"; that is it can only contain letters, digits and _ characters (no spaces, punctuation, etc.).
Preceding decisions
depends=string
A comma-separated list of decisions that must have already been evaluated before this decision can be evaluated. Can be empty. Each must be hard-coded or described here. Can include the special decisions "p" and "age", though "age" cannot be combined with any other dependency.
Outcome values
values=string
A comma-separated list of outcome values this decision may have. The name of each value must be of the same form as decision names (i.e. only contain letters, digits and _ characters).
treatments
→ scenario → interventions → descriptions → MDADescription → treatments
<treatments>
IN THIS ORDER:
| ( <treatment ... /> )*
</treatments>
Documentation (type)
Units: string
A list of drug treatment tables. Each should have a name corresponding to one of the "drug" decision's values.
treatment
→ scenario → interventions → descriptions → MDADescription → treatments → treatment
<treatment
name=string
>
IN THIS ORDER:
| <schedule ... />
| ( <modifier ... /> )*
</treatment>
Documentation (type)
Units: List of elements
A description of a base treatment schedule along with modifiers to handle delays, quality variations, etc.
Attributes
Treatment administered
name=string
Units: string
Name corresponding to one of the drug decision's output values.
schedule
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → schedule
<schedule>
IN THIS ORDER:
| ( <medicate ... /> )*
</schedule>
Documentation (type)
Units: List of elements
The base (unmodified) schedule of drugs administered for this treatment.
medicate
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → schedule → medicate
<medicate
drug=string
mg=double
hour=double
[ duration=double ]
/>
Attributes
drug
drug=string
Units: string
Abbreviated name of drug compound
drug dose
mg=double
Units: mg
Quantity of drug compound
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).
duration of administration
duration=double
Units: hours Min: 0
If this attribute is given, use IV administration instead of orally. Specifies the number of hours over which the dose is administered.
modifier
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → modifier
<modifier
decision=string
>
EXACTLY ONE OF:
| ( <multiplyQty ... /> )*
| ( <delay ... /> )*
| ( <selectTimeRange ... /> )*
</modifier>
Documentation (type)
Units: Choice of elements
A modifier for this treatment, according to the outputs of a decision.
The "decision" attribute must be the name of a known decision. Then, there must be a set of multipyQty, delay or selectTimeRange sub-elements, each of which corresponds to one value output of the decision.
Attributes
decision
decision=string
Units: string
Specifies the decision that this modifier acts on.
Active ingredient multipliers
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → modifier → multiplyQty
<multiplyQty
value=string
effect=string
[ affectsCost=boolean ]
/>
Documentation (element)
Units: Comma separated list of values
Multiplies the quantity of active ingredients of drugs administered.
The "drugs" attribute is a comma-separated list of all active ingredients administered in the base schedule (each must be listed once) and the content of this element is a comma- separated list of multipliers for each active ingredient, listed in the same order as in the "drugs" attribute. E.g. with drugs="A,B" and content "0.5,1" the quantity of drug A is halved while that of B is unchanged.
Attributes
value of decision
value=string
Units: string
Specifies a value of the decision to act on.
List of drugs affected
effect=string
Units: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
Affects cost?
affectsCost=boolean
Units: none
Does this affect the cost? If false, the effective drug usage (w.r.t. cost) is unaffected by this modifier; if true it is. Defaults to true (if omitted). Is meaningless for delays.
Active ingredient delays
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → modifier → delay
<delay
value=string
effect=string
[ affectsCost=boolean ]
/>
Documentation (element)
Units: Comma separated list of values
Delays administration of drugs listed in the base schedule by so many hours.
The "drugs" attribute is a comma-separated list of all active ingredients administered in the base schedule (each must be listed once) and the content of this element is a comma- separated list of delays (in hours) for each active ingredient, listed in the same order as in the "drugs" attribute. E.g. with drugs="A,B" and content "0,6", drug A is administered as in the base schedule while drug B is administered 6 hours later than specified.
Attributes
value of decision
value=string
Units: string
Specifies a value of the decision to act on.
List of drugs affected
effect=string
Units: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
Affects cost?
affectsCost=boolean
Units: none
Does this affect the cost? If false, the effective drug usage (w.r.t. cost) is unaffected by this modifier; if true it is. Defaults to true (if omitted). Is meaningless for delays.
Active ingredient time-ranges
→ scenario → interventions → descriptions → MDADescription → treatments → treatment → modifier → selectTimeRange
<selectTimeRange
value=string
effect=string
[ affectsCost=boolean ]
/>
Documentation (element)
Units: Comma separated list of values
Selects which drug doses to administer according to time of administration (before times are modified by delays). Half-open interval: [x,y)
The "drugs" attribute is a comma-separated list of all active ingredients administered in the base schedule (each must be listed once) and the content of this element is a comma- separated list of time-ranges (in hours) for each active ingredient, listed in the same order as in the "drugs" attribute. The time-ranges should be of the form x-y and are interpreted as the half-open interval [x,y); that is a drug listed with time t will only be administered if x <= t < y.
Attributes
value of decision
value=string
Units: string
Specifies a value of the decision to act on.
List of drugs affected
effect=string
Units: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
Affects cost?
affectsCost=boolean
Units: none
Does this affect the cost? If false, the effective drug usage (w.r.t. cost) is unaffected by this modifier; if true it is. Defaults to true (if omitted). Is meaningless for delays.
vaccineDescription
→ scenario → interventions → descriptions → vaccineDescription
<vaccineDescription
vaccineType=("PEV" or "BSV" or "TBV")
>
IN THIS ORDER:
| <decay ... />
| <efficacyB ... />
| ( <initialEfficacy ... /> )+
</vaccineDescription>
Attributes
Type of vaccine
vaccineType=("PEV" or "BSV" or "TBV")
Units: Code
Type of vaccine
Decay of effect
→ scenario → interventions → descriptions → vaccineDescription → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ sigma=double ] DEFAULT VALUE 0
/>
Documentation (element)
Specification of decay of efficacy
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=double
Units: Years Min: 0
Scale parameter of distribution. With the smooth-compact (smooth function with compact support), step and linear functions, this is the age at which the parameter has decayed to 0; with the other three functions, this is the age at which the parameter has decayed to half its original value. Not used for constant decay (though must be specified anyway).
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.
σ (sigma)
sigma=double
Units: none Min: 0
Default value: 0
If non-zero, heterogeneity of decay is introduced via a variate sampled from the log-normal distribution with mu zero (i.e. median is 1) and this sigma. Age is then divided by this variate before being passed to the decay function.
Variance parameter for vaccine efficacy
→ scenario → interventions → descriptions → vaccineDescription → efficacyB
<efficacyB
value=double
/>
Documentation (element)
Units: Positive real Min: 0.001 Max: 1.00E+06
Measure of variation in vaccine efficacy
Attributes
Input parameter value
value=double
A double-precision floating-point value.
initialEfficacy
→ scenario → interventions → descriptions → vaccineDescription → initialEfficacy
<initialEfficacy
value=double
/>
Attributes
Input parameter value
value=double
A double-precision floating-point value.
iptiDescription
→ scenario → interventions → descriptions → iptiDescription
<iptiDescription
iptiEffect=int
>
IN THIS ORDER:
| ( <infGenotype ... /> )+
</iptiDescription>
Attributes
Description of ipti effect
iptiEffect=int
Units: List of Elementes
Description of ipti effect
infGenotype
→ scenario → interventions → descriptions → iptiDescription → infGenotype
<infGenotype
name=string
freq=double
ACR=double
proph=int
tolPeriod=int
atten=double
/>
Attributes
Age specific intervention
name=string
Units: string
Name of age specific intervention
Frequency
freq=double
Frequency of parasite genotype
ACR
ACR=double
Adequate clinical response (proportion)
Prophylactic period
proph=int
Prophylactic period
Tolerance period
tolPeriod=int
Tolerance period
Tolerance period
atten=double
Tolerance period
ITNDecay
→ scenario → interventions → descriptions → ITNDecay
<ITNDecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ 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=double
Units: Years Min: 0
Scale parameter of distribution. With the smooth-compact (smooth function with compact support), step and linear functions, this is the age at which the parameter has decayed to 0; with the other three functions, this is the age at which the parameter has decayed to half its original value. Not used for constant decay (though must be specified anyway).
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.
σ (sigma)
sigma=double
Units: none Min: 0
Default value: 0
If non-zero, heterogeneity of decay is introduced via a variate sampled from the log-normal distribution with mu zero (i.e. median is 1) and this sigma. Age is then divided by this variate before being passed to the decay function.
IRSDecay
→ scenario → interventions → descriptions → IRSDecay
<IRSDecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ 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=double
Units: Years Min: 0
Scale parameter of distribution. With the smooth-compact (smooth function with compact support), step and linear functions, this is the age at which the parameter has decayed to 0; with the other three functions, this is the age at which the parameter has decayed to half its original value. Not used for constant decay (though must be specified anyway).
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.
σ (sigma)
sigma=double
Units: none Min: 0
Default value: 0
If non-zero, heterogeneity of decay is introduced via a variate sampled from the log-normal distribution with mu zero (i.e. median is 1) and this sigma. Age is then divided by this variate before being passed to the decay function.
VADecay
→ scenario → interventions → descriptions → VADecay
<VADecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ 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=double
Units: Years Min: 0
Scale parameter of distribution. With the smooth-compact (smooth function with compact support), step and linear functions, this is the age at which the parameter has decayed to 0; with the other three functions, this is the age at which the parameter has decayed to half its original value. Not used for constant decay (though must be specified anyway).
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.
σ (sigma)
sigma=double
Units: none Min: 0
Default value: 0
If non-zero, heterogeneity of decay is introduced via a variate sampled from the log-normal distribution with mu zero (i.e. median is 1) and this sigma. Age is then divided by this variate before being passed to the decay function.
anopheles
→ scenario → interventions → descriptions → anopheles
<anopheles
mosquito=string
>
IN THIS ORDER:
| [ <ITNDescription ... /> ]
| [ <IRSDescription ... /> ]
| [ <VADescription ... /> ]
</anopheles>
Attributes
Entomological effects of interventions
mosquito=string
Description of the entomological effects of interventions
ITNDescription
→ scenario → interventions → descriptions → anopheles → ITNDescription
<ITNDescription>
IN THIS ORDER:
| <deterrency ... />
</ITNDescription>
Documentation (base type)
Units: None Min: 0 Max: 1
Descriptions of initial effectiveness of each of the effects of interventions. Decay is specified by a separate element (ITNDecay etc.)
Deterrency
→ scenario → interventions → descriptions → anopheles → ITNDescription → deterrency
<deterrency
value=double
/>
Documentation (element)
Units: None Min: 0 Max: 1
One minus this multiplies the host's availability.
Attributes
Input parameter value
value=double
A double-precision floating-point value.
Pre-prandial killing effect
→ scenario → interventions → descriptions → anopheles → ITNDescription → preprandialKillingEffect
<preprandialKillingEffect
value=double
/>
Documentation (element)
Units: None Min: 0 Max: 1
One minus this multiplies the survival rate of mosquitoes attempting to bite a host.
Attributes
Input parameter value
value=double
A double-precision floating-point value.
Post-prandial killing effect
→ scenario → interventions → descriptions → anopheles → ITNDescription → postprandialKillingEffect
<postprandialKillingEffect
value=double
/>
Documentation (element)
Units: None Min: 0 Max: 1
One minus this multiplies the survival rate of mosquitoes attempting to escape after biting a host.
Attributes
Input parameter value
value=double
A double-precision floating-point value.
IRSDescription
→ scenario → interventions → descriptions → anopheles → IRSDescription
<IRSDescription>
IN THIS ORDER:
| <deterrency ... />
</IRSDescription>
Documentation (base type)
Units: None Min: 0 Max: 1
Descriptions of initial effectiveness of each of the effects of interventions. Decay is specified by a separate element (ITNDecay etc.)
killing effect
→ scenario → interventions → descriptions → anopheles → IRSDescription → killingEffect
<killingEffect
value=double
/>
Documentation (element)
Units: None Min: 0 Max: 1
One minus this multiplies the survival rate of resting mosquitoes.
Attributes
Input parameter value
value=double
A double-precision floating-point value.
VADescription
→ scenario → interventions → descriptions → anopheles → VADescription
<VADescription
name=string
>
IN THIS ORDER:
| <deterrency ... />
</VADescription>
Documentation (type)
Units: None Min: 0 Max: 1
Descriptions of initial effectiveness of each of the effects of interventions. Decay is specified by a separate element (ITNDecay etc.)
Attributes
Name of intervention
name=string
Units: string
Name of intervention
Continuously applied interventions
→ scenario → interventions → continuous
<continuous>
IN THIS ORDER:
| ( <vaccine ... /> )*
| ( <ITN ... /> )*
| ( <ipti ... /> )*
| ( <cohort ... /> )*
</continuous>
Documentation (element)
Continuously applied interventions
Documentation (type)
Interventions to be deployed continuously
List of age groups for vaccination
→ scenario → interventions → continuous → vaccine
<vaccine
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>
Documentation (element)
Units: List of elements
List of age groups for vaccination
Attributes
Target age
targetAgeYrs=double
Units: Years Min: 0 Max: 100
Target age of intervention
Proportion covered
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort (default: false).
First timestep active
begin=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 0
First timestep (from 0 at the beginning of the intervention period) this item is active. Defaults to 0.
End timestep
end=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 2147483647
End of the period during which the intervention is active (to be exact, the first timestep of the intervention period at which the item becomes inactive). Defaults to 2147483647.
Description of ITN deployment
→ scenario → interventions → continuous → ITN
<ITN
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>
Documentation (element)
Units: List of elements
Description of ITN deployment in the community
Attributes
Target age
targetAgeYrs=double
Units: Years Min: 0 Max: 100
Target age of intervention
Proportion covered
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort (default: false).
First timestep active
begin=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 0
First timestep (from 0 at the beginning of the intervention period) this item is active. Defaults to 0.
End timestep
end=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 2147483647
End of the period during which the intervention is active (to be exact, the first timestep of the intervention period at which the item becomes inactive). Defaults to 2147483647.
List of age groups for IPTi
→ scenario → interventions → continuous → ipti
<ipti
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>
Documentation (element)
Units: List of elements
List of age groups for IPTi distribution
Attributes
Target age
targetAgeYrs=double
Units: Years Min: 0 Max: 100
Target age of intervention
Proportion covered
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort (default: false).
First timestep active
begin=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 0
First timestep (from 0 at the beginning of the intervention period) this item is active. Defaults to 0.
End timestep
end=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 2147483647
End of the period during which the intervention is active (to be exact, the first timestep of the intervention period at which the item becomes inactive). Defaults to 2147483647.
Continuous cohort selection
→ scenario → interventions → continuous → cohort
<cohort
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>
Documentation (element)
Units: List of elements
Description of continuous selection of individuals for a cohort study.
Attributes
Target age
targetAgeYrs=double
Units: Years Min: 0 Max: 100
Target age of intervention
Proportion covered
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort (default: false).
First timestep active
begin=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 0
First timestep (from 0 at the beginning of the intervention period) this item is active. Defaults to 0.
End timestep
end=int
Units: Timesteps Min: 0 Max: 2147483647
Default value: 2147483647
End of the period during which the intervention is active (to be exact, the first timestep of the intervention period at which the item becomes inactive). Defaults to 2147483647.
Timed interventions
→ scenario → interventions → timed
<timed>
IN THIS ORDER:
| ( <intervention ... /> )+
</timed>
Documentation (element)
Units: List of elements
List of timed interventions
Documentation (type)
Units: List of elements
List of timed interventions
intervention
→ scenario → interventions → timed → intervention
<intervention
time=int
>
IN THIS ORDER:
| [ <changeHS ... /> ]
| [ <changeEIR ... /> ]
| [ <importedInfectionsPerThousandHosts ... /> ]
| [ <MDA ... /> ]
| [ <vaccinate ... /> ]
| [ <ITN ... /> ]
| [ <IRS ... /> ]
| [ <VectorAvailability ... /> ]
| [ <ipti ... /> ]
| [ <immuneSuppression ... /> ]
| [ <insertR_0Case ... /> ]
| [ <uninfectVectors ... /> ]
| [ <larviciding ... /> ]
| [ <cohort ... /> ]
</intervention>
- changeHS
- changeEIR
- importedInfectionsPerThousandHosts
- MDA
- vaccinate
- ITN
- IRS
- VectorAvailability
- ipti
- immuneSuppression
- insertR_0Case
- uninfectVectors
- larviciding
- cohort
Attributes
Time step of intervention
time=int
Units: Number Min: 1 Max: 10000
Time step of intervention
changeHS
→ scenario → interventions → timed → intervention → changeHS
<changeHS>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EventScheduler ... />
| | <ImmediateOutcomes ... />
| <CFR ... />
| <pSequelaeInpatient ... />
</changeHS>
Documentation (type)
Units: List of elements
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.
EventScheduler
→ scenario → healthSystem → EventScheduler
<EventScheduler>
IN THIS ORDER:
| <uncomplicated ... />
| <complicated ... />
| <ClinicalOutcomes ... />
| [ <NonMalariaFevers ... /> ]
</EventScheduler>
uncomplicated
→ scenario → healthSystem → EventScheduler → uncomplicated
<uncomplicated>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</uncomplicated>
Documentation (type)
Units: List of elements
A set of decisions and a set of treatments.
complicated
→ scenario → healthSystem → EventScheduler → complicated
<complicated>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</complicated>
Documentation (type)
Units: List of elements
A set of decisions and a set of treatments.
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 of case) an individual will remember they are 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/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/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: none Min: 0 Max: 1
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>
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: none Min: 0 Max: 1
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") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</CFR>
Documentation (element)
Units: none Min: 0 Max: 1
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")
Units: none
Default value: 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: none Min: 0 Max: 1
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
>
IN THIS ORDER:
| <drugRegimen ... />
| <initialACR ... />
| <compliance ... />
| <nonCompliersEffective ... />
| <pSeekOfficialCareUncomplicated1 ... />
| <pSelfTreatUncomplicated ... />
| <pSeekOfficialCareUncomplicated2 ... />
| <pSeekOfficialCareSevere ... />
</ImmediateOutcomes>
- drugRegimen
- initialACR
- compliance
- nonCompliersEffective
- pSeekOfficialCareUncomplicated1
- pSelfTreatUncomplicated
- pSeekOfficialCareUncomplicated2
- pSeekOfficialCareSevere
Documentation (type)
Units: List of elements
Description of "immediate outcomes" health system: Tediosi et al case management model (Case management as described in AJTMH 75 (suppl 2) pp90-103).
Attributes
Case Management model
name=string
Units: string
Name of health system
Description of drug regimen
→ scenario → healthSystem → ImmediateOutcomes → drugRegimen
<drugRegimen
firstLine=string
secondLine=string
inpatient=string
/>
Documentation (element)
Units: List of elements
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>
Documentation (element)
Units: Proportion Min: 0 Max: 1
Initial cure rate
Chloroquine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → CQ
<CQ
value=double
/>
Documentation (element)
Units: List of elements
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)
Units: List of elements
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)
Units: List of elements
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)
Units: List of elements
Sulphadoxine-pyrimethamine/Amodiaquine
Attributes
Input parameter value
value=double
A double-precision floating-point value.
ACT
→ scenario → healthSystem → ImmediateOutcomes → initialACR → ACT
<ACT
value=double
/>
Documentation (element)
Units: List of elements
Artemisinine combination therapy
Attributes
Input parameter value
value=double
A double-precision floating-point value.
QN
→ scenario → healthSystem → ImmediateOutcomes → initialACR → QN
<QN
value=double
/>
Documentation (element)
Units: List of elements
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: Proportion Min: 0 Max: 1
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>
Documentation (element)
Units: Proportion Min: 0 Max: 1
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>
Documentation (element)
Units: Proportion Min: 0 Max: 1
Effectiveness of treatment of non compliers
Probability that a patient with uncomplicated disease seeks official care immediately
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated1
<pSeekOfficialCareUncomplicated1
value=double
/>
Documentation (element)
Units: Proportion Min: 0 Max: 1
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 self-treats
→ scenario → healthSystem → ImmediateOutcomes → pSelfTreatUncomplicated
<pSelfTreatUncomplicated
value=double
/>
Documentation (element)
Units: Proportion Min: 0 Max: 1
Probability that a patient with uncomplicated disease self-treats
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: Proportion Min: 0 Max: 1
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: Proportion Min: 0 Max: 1
Probability that a patient with severe disease obtains appropriate care
Attributes
Input parameter value
value=double
A double-precision floating-point value.
Case fatality rate in inpatients
→ scenario → healthSystem → CFR
<CFR
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</CFR>
Documentation (element)
Units: List of elements
Case fatality rate (probability of an inpatient fatality from a bout of severe malaria, per age-group).
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
Probabilities of sequelae in inpatients
→ scenario → healthSystem → pSequelaeInpatient
<pSequelaeInpatient
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</pSequelaeInpatient>
Documentation (element)
Units: List of elements
List of age specific probabilities of sequelae in inpatients, during a severe bout.
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
Replacement transmission setting (vector control not enabled)
→ scenario → interventions → timed → intervention → changeEIR
<changeEIR
eipDuration=int
>
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</changeEIR>
Documentation (element)
Units: List of elements
New description of transmission setting for models without vector control interventions (included for backward compatibility)
Attributes
Duration of sporogony (days)
eipDuration=int
The duration of sporogony in days
EIRDaily
→ scenario → entoData → 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 a 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
Import infections
→ scenario → interventions → timed → intervention → importedInfectionsPerThousandHosts
<importedInfectionsPerThousandHosts>
double
</importedInfectionsPerThousandHosts>
Documentation (element)
Units: Number per 1000 hosts Min: 0
Import infections (as if a mosquito bites a human). Imported via a Bernoulli trial per host.
Mass Drug Administration
→ scenario → interventions → timed → intervention → MDA
<MDA
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
/>
Documentation (element)
Units: List of elements
Description of mass drug deployment in the community.
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Vaccination
→ scenario → interventions → timed → intervention → vaccinate
<vaccinate
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
Description of vaccine deployment in the community
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
ITN deployment
→ scenario → interventions → timed → intervention → ITN
<ITN
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
Description of ITN deployment in the community
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
IRS deployment
→ scenario → interventions → timed → intervention → IRS
<IRS
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
Description of IRS deployment in the community
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
Mosquito deterrent
→ scenario → interventions → timed → intervention → VectorAvailability
<VectorAvailability
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
Description of a mosquito deterrent deployment in the community
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
IPTi deployment
→ scenario → interventions → timed → intervention → ipti
<ipti
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
List of age groups for IPTi distribution
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
Immune suppression deployment
→ scenario → interventions → timed → intervention → immuneSuppression
<immuneSuppression
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
/>
Documentation (element)
Units: List of elements
Removes all exposure-related immunity gained over time by hosts without removing infections.
Hypothetical, but potentially useful to simulate scenarios with unprotected humans.
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Insert R_0 case
→ scenario → interventions → timed → intervention → insertR_0Case
<insertR_0Case/>
Documentation (element)
Units: List of elements
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.
Uninfect vectors
→ scenario → interventions → timed → intervention → uninfectVectors
<uninfectVectors/>
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 effectious 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.
Simple larviciding intervention
→ scenario → interventions → timed → intervention → larviciding
<larviciding>
IN THIS ORDER:
| ( <anopheles ... /> )+
</larviciding>
Documentation (element)
Units: List of elements
Simple larviciding intervention description.
anopheles
→ scenario → interventions → timed → intervention → larviciding → anopheles
<anopheles
mosquito=string
effectiveness=double
duration=int
/>
Attributes
Mosquito to be larvicided
mosquito=string
Mosquito to be larvicided
Proportionate reduction in emergence
effectiveness=double
Units: none Min: 0 Max: 1
Proportional reduction in emergence rate
Duration of activity
duration=int
Units: days Min: 0 Max: inf
Number of days for which the intervention is active.
Mass Cohort Selection
→ scenario → interventions → timed → intervention → cohort
<cohort
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ cumulativeWithMaxAge=double ]
/>
Documentation (element)
Units: List of elements
Description of mass cohort selection.
Attributes
Maximum age of eligible individuals
maxAge=double
Units: Years Min: 0 Max: 100
Default value: 100
Maximum age of eligible individuals (defaults to 100)
Minimum age of eligible individuals
minAge=double
Units: Years Min: 0 Max: 100
Default value: 0
Minimum age of eligible individuals (defaults to 0)
Coverage
coverage=double
Units: Proportion Min: 0 Max: 1
Coverage of intervention
Cohort only
cohort=boolean
Units: Proportion Min: 0 Max: 1
Default value: false
Restrict distribution to chosen cohort.
Cumulative deployment: maximum age
cumulativeWithMaxAge=double
Units: Years Min: 0
If present, activate cumulate deployment mode where intervention is only deployed to individuals not already considered protected in sufficient quantity to bring the total proportion of people covered up to level described by "coverage". Individuals are considered already protected by this intervention when the age of the last net/dose/etc. received is less than "maximum age" (this attribute) years old (i.e. when timeLastDeployment+maximumAge>currentTimeStep).
Health system description
→ scenario → healthSystem
<healthSystem>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EventScheduler ... />
| | <ImmediateOutcomes ... />
| <CFR ... />
| <pSequelaeInpatient ... />
</healthSystem>
Documentation (element)
Units: List of elements
Description of health system.
Documentation (type)
Units: List of elements
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
<entoData
name=string
mode=("2" or "4")
[ annualEIR=double ]
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <nonVector ... />
| | <vector ... />
</entoData>
Documentation (element)
Units: List of elements
Description of entomological data
Attributes
Entomology dataset
name=string
Units: string
Name of entomology data
Transmission simulation mode
mode=("2" or "4")
Units: Code
Transmission simulation mode -- enter dynamic mode (4) or forced mode (2) at start of intervention period. Mode 3 (transient EIR from data provided as intervention) is set when intervention data is applied, and is no longer a valid value to specify here.
Override annual EIR
annualEIR=double
Units: Infectious bites per adult per year
If set, overrides the annual EIR by scaling it to this level. If ommitted, EIR levels are as specified elsewhere.
Transmission setting (vector control not enabled)
→ scenario → entoData → nonVector
<nonVector
eipDuration=int
>
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</nonVector>
Documentation (element)
Units: List of elements
Description of transmission setting for models without vector control interventions (included for backward compatibility)
Attributes
Duration of sporogony (days)
eipDuration=int
The duration of sporogony in days
Transmission setting (vector control enabled)
→ scenario → entoData → vector
<vector>
IN THIS ORDER:
| ( <anopheles ... /> )+
| ( <nonHumanHosts ... /> )*
</vector>
Documentation (element)
Units: List of elements
Parameters of the transmission model.
anopheles
→ scenario → entoData → vector → anopheles
<anopheles
mosquito=string
propInfected=double
propInfectious=double
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <eir ... />
| | <monthlyEir ... />
| <mosq ... />
| ( <nonHumanHosts ... /> )*
</anopheles>
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 guess 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 guess of proportion of mosquitoes infectious (ρ_S)
propInfectious=double
Units: Proportion Min: 0 Max: 1
Initial guess of the proportion of mosquitoes which are infectious, s: S_v(t) = s*N_v(t). Used as a starting value and then fit.
Fourier approximation to pre-intervention EIR
→ scenario → entoData → vector → anopheles → eir
<eir
a0=double
a1=double
b1=double
a2=double
b2=double
EIRRotateAngle=double
/>
Documentation (element)
Units: Infectious bites per adult per day
Description of target entomological inoculation rate as a Fourier series. This is used to estimate a suitible vector emergence rate. The annual (target) EIR is thus the exponent of the fourier series with these parameters, with period scaled to 365 days.
Attributes
a0 parameter of Fourier approximation to ln(EIR)
a0=double
a0 parameter of Fourier approximation to ln(EIR)
a1 parameter of Fourier approximation to ln(EIR)
a1=double
a1 parameter of Fourier approximation to ln(EIR)
b1 parameter of Fourier approximation to ln(EIR)
b1=double
b1 parameter of Fourier approximation to ln(EIR)
a2 parameter of Fourier approximation to ln(EIR)
a2=double
a2 parameter of Fourier approximation to ln(EIR)
b2 parameter of Fourier approximation to ln(EIR)
b2=double
b2 parameter of Fourier approximation to ln(EIR)
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)
Monthly values for pre-intervention EIR
→ scenario → entoData → vector → anopheles → monthlyEir
<monthlyEir
annualEIR=double
>
IN THIS ORDER:
| ( <item ... /> ){12,12}
</monthlyEir>
Documentation (element)
Units: Infectious bites per adult per month
Description of target entomological inoculation rate as monthly values plus an annual override (monthly values are scaled to fit the annual EIR described). This is used to estimate a suitible vector emergence rate. The annual (target) EIR is derived from a Fourier series fit to these monthly values (used as a smoothing factor). List should contain twelve entries: January to December.
Attributes
Annual EIR
annualEIR=double
Units: Infectious bites per adult per year Min: 0
Scales the monthly values to give this annual innoculation rate.
Monthly pre-intervention EIR
→ scenario → entoData → vector → anopheles → monthlyEir → item
<item>
double
</item>
Documentation (element)
Units: Inoculations per person per month
Inoculations per person per month
Vector Species
→ scenario → entoData → vector → anopheles → mosq
<mosq
mosqRestDuration=int
extrinsicIncubationPeriod=int
mosqLaidEggsSameDayProportion=double
mosqSeekingDuration=double
mosqSurvivalFeedingCycleProbability=double
mosqProbBiting=double
mosqProbFindRestSite=double
mosqProbResting=double
mosqProbOvipositing=double
mosqHumanBloodIndex=double
minInfectedThreshold=double
/>
Documentation (element)
Units: List of elements
Vector species
Attributes
Duration of the resting period of the vector (days)
mosqRestDuration=int
name:Duration of the resting period of the vector (days);
Extrinsic incubation period (days)
extrinsicIncubationPeriod=int
name:Extrinsic incubation period (days)
Proportion of mosquitoes host seeking on same day as ovipositing
mosqLaidEggsSameDayProportion=double
Proportion of mosquitoes host seeking on same day as ovipositing
Duration of the host-seeking period of the vector (days)
mosqSeekingDuration=double
Duration of the host-seeking period of the vector (days)
Probability that the mosquito survives the feeding cycle
mosqSurvivalFeedingCycleProbability=double
Probability that the mosquito survives the feeding cycle
Probability that the mosquito succesfully bites chosen host
mosqProbBiting=double
Probability that the mosquito succesfully bites chosen host
Probability that the mosquito escapes host and finds a resting place after biting
mosqProbFindRestSite=double
Probability that the mosquito escapes host and finds a resting place after biting
Probability of mosquito successfully resting after finding a resting site
mosqProbResting=double
Probability of mosquito successfully resting after finding a resting site
Probability of a mosquito successfully laying eggs given that it has rested
mosqProbOvipositing=double
Probability of a mosquito successfully laying eggs given that it has rested
Human blood index
mosqHumanBloodIndex=double
The proportion of resting mosquitoes which fed on human blood during the last feed.
Min infected threshold
minInfectedThreshold=double
Min: 0
If less than this many mosquitoes remain infected, transmission is interrupted.
nonHumanHosts
→ scenario → entoData → vector → anopheles → nonHumanHosts
<nonHumanHosts
name=string
mosqRelativeEntoAvailability=double
mosqProbBiting=double
mosqProbFindRestSite=double
mosqProbResting=double
/>
Documentation (element)
Units: List of elements
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 nonhuman hosts of type i (to other nonhuman hosts)
mosqRelativeEntoAvailability=double
Relative availability of nonhuman hosts of type i (to other nonhuman hosts)
Probability of mosquito successfully biting host
mosqProbBiting=double
Probability of mosquito successfully biting host
Probability that the mosquito escapes host and finds a resting place after biting
mosqProbFindRestSite=double
Probability that the mosquito escapes host and finds a resting place after biting
Probability of mosquito successfully resting after finding a resting site
mosqProbResting=double
Probability of mosquito successfully resting after finding a resting site
nonHumanHosts
→ scenario → entoData → vector → nonHumanHosts
<nonHumanHosts
name=string
number=double
/>
Attributes
Species of alternative host
name=string
Units: List of elements
Name of this species of non human hosts (must match up with those described per anopheles section)
number
number=double
Pharmacokinetics and pharmacodynamics
<drugDescription>
IN THIS ORDER:
| ( <drug ... /> )+
</drugDescription>
Documentation (element)
Units: List of elements
Drug model parameters
Library of drug parameters
→ scenario → drugDescription → drug
<drug
abbrev=string
>
IN THIS ORDER:
| <PD ... />
| <PK ... />
</drug>
Documentation (element)
Sequence of drug descriptions forming a library of drug parameters.
Attributes
abbrev
abbrev=string
PD
→ scenario → drugDescription → drug → PD
<PD>
IN THIS ORDER:
| ( <allele ... /> )+
</PD>
PD parameters per allele
→ scenario → drugDescription → drug → PD → allele
<allele
name=string
>
IN THIS ORDER:
| <initial_frequency ... />
| <max_killing_rate ... />
| <IC50 ... />
| <slope ... />
</allele>
Documentation (element)
PD parameters per allele, plus initial frequency of each allele.
Note: we assume a one-to-one correspondance of drugs to loci, hence each drug has an independent set of alleles here.
Attributes
name
name=string
Relative frequency
→ scenario → drugDescription → drug → PD → allele → initial_frequency
<initial_frequency>
double
</initial_frequency>
Documentation (element)
Units: relative frequency Min: 0
Frequency, relative to the total frequency of all alleles for this drug/locus.
Maximal parasite killing rate
→ scenario → drugDescription → drug → PD → allele → max_killing_rate
<max_killing_rate>
double
</max_killing_rate>
Documentation (element)
Units: 1/days Min: 0
k1 — Maximal parasite killing rate.
IC50
→ scenario → drugDescription → drug → PD → allele → IC50
<IC50>
double
</IC50>
Documentation (element)
Units: mg/l Min: 0
Half maximal effect concentration.
Slope of effect curve
→ scenario → drugDescription → drug → PD → allele → slope
<slope>
double
</slope>
Documentation (element)
Units: no units
n — Slope of the concentration effect curve
PK
→ scenario → drugDescription → drug → PK
<PK>
IN THIS ORDER:
| <negligible_concentration ... />
| <half_life ... />
| <vol_dist ... />
</PK>
Drug concentration considered negligible
→ scenario → drugDescription → 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 → drugDescription → drug → PK → half_life
<half_life>
double
</half_life>
Documentation (element)
Units: days Min: 0
Used to calculate elimination rate (which is: ln(2) / half_life).
Volume of Distribution
→ scenario → drugDescription → drug → PK → vol_dist
<vol_dist>
double
</vol_dist>
Documentation (element)
Units: l/kg Min: 0
Volume of Distribution
Model options and parameters
<model>
IN THIS ORDER:
| <ModelOptions ... />
| <clinical ... />
| <human ... />
| <parameters ... />
</model>
Documentation (element)
Units: List of elements
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>
Documentation (element)
Units: List of elements
All model options (bug fixes, choices between models, etc.).
The list of recognised options can be found in the code at:
include/util/ModelOptions.h and should also be in the wiki.
clinical
<clinical
healthSystemMemory=int
>
IN THIS ORDER:
| [ <NonMalariaFevers ... /> ]
</clinical>
Documentation (type)
Units: List of elements
Description of clinical parameters.
This is related to the health-system description, but contains data which can't be changed as part of an intervention and is not restricted to treatment.
Attributes
Follow-up period during which recurrence is treated as a failure
healthSystemMemory=int
Units: Time steps Min: 1 Max: 100
Follow-up period during which a recurrence is considered to be a treatment failure
NonMalariaFevers
→ scenario → model → clinical → NonMalariaFevers
<NonMalariaFevers>
IN THIS ORDER:
| <incidence ... />
| <prNeedTreatmentNMF ... />
| <prNeedTreatmentMF ... />
</NonMalariaFevers>
Documentation (type)
Description of non-malaria fever incidence. 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") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</incidence>
Documentation (element)
Units: none Min: 0 Max: 1
Probability that a non-malaria fever occurs given that no concurrent malaria fever occurs.
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
P(need treatment | NMF)
→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentNMF
<prNeedTreatmentNMF
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentNMF>
Documentation (element)
Units: none Min: 0 Max: 1
Probability that a non-malaria fever needs treatment with antibiotics (assuming fever is not induced by malaria, although concurrent parasites may be present).
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
P(need treatment | MF)
→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentMF
<prNeedTreatmentMF
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentMF>
Documentation (element)
Units: none 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")
Units: none
Default value: 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.
human
<human>
IN THIS ORDER:
| <availabilityToMosquitoes ... />
| [ <weight ... /> ]
</human>
Documentation (type)
Parameters of host models.
Availability to mosquitoes
→ scenario → model → human → availabilityToMosquitoes
<availabilityToMosquitoes
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
>
IN THIS ORDER:
| ( <group ... /> )+
</availabilityToMosquitoes>
Documentation (element)
Units: None Min: 0 Max: 1
By age group data on availability of humans to mosquitoes relative to an adult.
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
Weight
→ scenario → model → human → weight
<weight
[ interpolation=("none" or "linear") ] DEFAULT VALUE linear
multStdDev=double
>
IN THIS ORDER:
| ( <group ... /> )+
</weight>
Documentation (element)
Units: kg Min: 0
By age group data on human weight (mass).
Attributes
interpolation
interpolation=("none" or "linear")
Units: none
Default value: 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.
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.
Parameters of the model of epidemiology
→ scenario → model → parameters
<parameters
interval=int
iseed=int
latentp=int
>
IN THIS ORDER:
| ( <parameter ... /> )+
</parameters>
Documentation (element)
Units: List of elements
Parameters of the epidemiological model
Attributes
Simulation step
interval=int
Units: Days
Simulation step
Random number seed
iseed=int
Units: Number
Seed for RNG
Pre-erythrocytic latent period
latentp=int
Units: Time steps Min: 0 Max: 20
pre-erythrocytic latent period, in time steps
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.