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GeneratedSchema29Doc
This page is automatically generated from the following schema file: scenario_29.xsd.
I recommend against editing it because edits will likely be lost later.
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
schemaVersion=int
analysisNo=int
name=string
wuID=int
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="scenario_29.xsd"
>
IN ANY ORDER:
| <demography ... />
| <monitoring ... />
| <interventions ... />
| <healthSystem ... />
| <entomology ... />
| [ <pharmacology ... /> ]
| <model ... />
</scenario>Description of scenario
schemaVersion=intVersion of xml schema. If not equal to the current version an error is thrown. Use SchemaTranslator to update xml files.
analysisNo=intUnits: Number Min: 1 Max: 100000000
Unique identifier of scenario
name=stringUnits: string
Name of intervention
wuID=intUnits: Number Min: 1 Max: 100000000
Work unit ID. Only used to validate checkpointing, to prevent checkpoint cheats.
→ scenario → demography
<demography
name=string
popSize=int
maximumAgeYrs=double
[ growthRate=double ]
>
IN THIS ORDER:
| <ageGroup ... />
</demography>Description of demography
name=stringUnits: string
Name of demography data
popSize=intUnits: Count Min: 1 Max: 100000
Population size
maximumAgeYrs=doubleUnits: years Min: 0 Max: 100
Maximum age of simulated humans in years.
growthRate=doubleUnits: Number Min: 0 Max: 0
Growth rate of human population. (we should be able to implement this with non-zero values)
→ scenario → demography → ageGroup
<ageGroup
lowerbound=double
>
IN THIS ORDER:
| ( <group ... /> )+
</ageGroup>List of age groups included in demography
List of age groups included in demography or surveys
lowerbound=doubleUnits: Years Min: 0 Max: 100
Lower bound of age group
→ scenario → demography → ageGroup → group
<group
poppercent=double
upperbound=double
/>poppercent=doublePercentage of human population in age group
upperbound=doubleUnits: Years Min: 0 Max: 100
Upper bound of age group
→ scenario → monitoring
<monitoring
name=string
[ cohortOnly=boolean ]
[ firstBoutOnly=boolean ]
[ firstTreatmentOnly=boolean ]
[ firstInfectionOnly=boolean ]
>
IN THIS ORDER:
| [ <continuous ... /> ]
| <SurveyOptions ... />
| <surveys ... />
| <ageGroup ... />
</monitoring>Description of surveys
name=stringUnits: string
Name of monitoring data
cohortOnly=booleanIf 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.
firstBoutOnly=booleanIf 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.
firstTreatmentOnly=booleanIf 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.
firstInfectionOnly=booleanIf 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.
→ scenario → monitoring → continuous
<continuous
period=int
[ duringInit=boolean ]
>
IN THIS ORDER:
| ( <option ... /> )*
</continuous>period=intUnits: Days Min: 1 Max: unbounded
Number of timesteps between reports.
duringInit=booleanUnits: 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.
→ scenario → monitoring → continuous → option
<option
name=string
[ value=boolean ] DEFAULT VALUE true
/>name=stringOptions 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.
value=booleanDefault value: true
Option value (true/false). Each option has a default value used if the option is not listed (usually false but sometimes true).
→ scenario → monitoring → SurveyOptions
<SurveyOptions>
IN THIS ORDER:
| ( <option ... /> )*
</SurveyOptions>List of all active survey options. See include/Survey.h for a list of supported outputs. Should also be on the wiki.
→ scenario → monitoring → surveys
<surveys
detectionLimit=double
>
IN THIS ORDER:
| ( <surveyTime ... /> )+
</surveys>List of survey times
detectionLimit=doubleUnits: parasites/microlitre Min: 0
Limit above which a human's infection is reported as patent.
→ scenario → monitoring → surveys → surveyTime
<surveyTime>
int
</surveyTime>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.
→ scenario → monitoring → ageGroup
<ageGroup
lowerbound=double
>
IN THIS ORDER:
| ( <group ... /> )+
</ageGroup>List of age groups included in demography or surveys
List of age groups included in surveys
lowerbound=doubleUnits: Years Min: 0 Max: 100
Lower bound of age group
→ scenario → monitoring → ageGroup → group
<group
upperbound=double
/>upperbound=doubleUnits: Years Min: 0 Max: 100
Upper bound of age group
<interventions
name=string
>
IN ANY ORDER:
| [ <changeHS ... /> ]
| [ <changeEIR ... /> ]
| [ <MDA ... /> ]
| [ <vaccine ... /> ]
| [ <IPT ... /> ]
| [ <ITN ... /> ]
| [ <IRS ... /> ]
| [ <vectorDeterrent ... /> ]
| [ <cohort ... /> ]
| [ <importedInfections ... /> ]
| [ <immuneSuppression ... /> ]
| [ <insertR_0Case ... /> ]
| [ <uninfectVectors ... /> ]
| [ <larviciding ... /> ]
</interventions>- changeHS
- changeEIR
- MDA
- vaccine
- IPT
- ITN
- IRS
- vectorDeterrent
- cohort
- importedInfections
- immuneSuppression
- insertR_0Case
- uninfectVectors
- larviciding
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.
name=stringUnits: string
Name of set of interventions
→ scenario → interventions → changeHS
<changeHS
[ name=string ]
>
IN THIS ORDER:
| ( <timed ... /> )*
</changeHS>Changes to the health system
name=stringUnits: string
Name of intervention
→ scenario → interventions → changeHS → timed
<timed
time=int
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EventScheduler ... />
| | <ImmediateOutcomes ... />
| <CFR ... />
| <pSequelaeInpatient ... />
</timed>A complete replacement health system. Replaces all previous properties. (Health system can be replaced multiple times if necessary.)
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.
time=intUnits: time-steps Min: 0
Time-step at which this replacement occurs, starting from 0, the first intervention-period time-step.
→ scenario → healthSystem → EventScheduler
<EventScheduler>
IN THIS ORDER:
| <uncomplicated ... />
| <complicated ... />
| <ClinicalOutcomes ... />
| [ <NonMalariaFevers ... /> ]
</EventScheduler>→ scenario → healthSystem → EventScheduler → uncomplicated
<uncomplicated>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</uncomplicated>A set of decisions and a set of treatments.
→ scenario → interventions → MDA → description → decisions
<decisions>
IN THIS ORDER:
| ( <decision ... /> )*
</decisions>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.
→ scenario → interventions → MDA → description → decisions → decision
<decision
name=string
depends=string
values=string
>
string
</decision>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.
name=stringThe 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.).
depends=stringA 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.
values=stringA 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).
→ scenario → interventions → MDA → description → treatments
<treatments>
IN THIS ORDER:
| ( <treatment ... /> )*
</treatments>A list of drug treatment tables. Each should have a name corresponding to one of the "drug" decision's values.
→ scenario → interventions → MDA → description → treatments → treatment
<treatment
name=string
>
IN THIS ORDER:
| <schedule ... />
| ( <modifier ... /> )*
</treatment>A description of a base treatment schedule along with modifiers to handle delays, quality variations, etc.
name=stringUnits: string
Name corresponding to one of the drug decision's output values.
→ scenario → interventions → MDA → description → treatments → treatment → schedule
<schedule>
IN THIS ORDER:
| ( <medicate ... /> )*
</schedule>The base (unmodified) schedule of drugs administered for this treatment.
→ scenario → interventions → MDA → description → treatments → treatment → schedule → medicate
<medicate
drug=string
mg=double
hour=double
[ duration=double ]
/>drug=stringUnits: string
Abbreviated name of drug compound
mg=doubleUnits: mg
Quantity of drug compound
hour=doubleUnits: hours Min: 0
Number of hours past start of timestep this drug dose is administered at (first dose should be at hour 0).
duration=doubleUnits: 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.
→ scenario → interventions → MDA → description → treatments → treatment → modifier
<modifier
decision=string
>
EXACTLY ONE OF:
| ( <multiplyQty ... /> )*
| ( <delay ... /> )*
| ( <selectTimeRange ... /> )*
</modifier>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.
decision=stringUnits: string
Specifies the decision that this modifier acts on.
→ scenario → interventions → MDA → description → treatments → treatment → modifier → multiplyQty
<multiplyQty
value=string
effect=string
[ affectsCost=boolean ]
/>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.
value=stringUnits: string
Specifies a value of the decision to act on.
effect=stringUnits: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
affectsCost=booleanUnits: 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.
→ scenario → interventions → MDA → description → treatments → treatment → modifier → delay
<delay
value=string
effect=string
[ affectsCost=boolean ]
/>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.
value=stringUnits: string
Specifies a value of the decision to act on.
effect=stringUnits: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
affectsCost=booleanUnits: 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.
→ scenario → interventions → MDA → description → treatments → treatment → modifier → selectTimeRange
<selectTimeRange
value=string
effect=string
[ affectsCost=boolean ]
/>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.
value=stringUnits: string
Specifies a value of the decision to act on.
effect=stringUnits: string
Comma-separated list of the effect the modifier has on each drug, in the form DRUG1(EFFECT1),DRUG2(EFFECT2), etc.
affectsCost=booleanUnits: 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.
→ scenario → healthSystem → EventScheduler → complicated
<complicated>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</complicated>A set of decisions and a set of treatments.
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes
<ClinicalOutcomes>
IN THIS ORDER:
| <maxUCSeekingMemory ... />
| <uncomplicatedCaseDuration ... />
| <complicatedCaseDuration ... />
| <complicatedRiskDuration ... />
| ( <dailyPrImmUCTS ... /> )+
</ClinicalOutcomes>- maxUCSeekingMemory
- uncomplicatedCaseDuration
- complicatedCaseDuration
- complicatedRiskDuration
- dailyPrImmUCTS
Description of base parameters of the clinical model.
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → maxUCSeekingMemory
<maxUCSeekingMemory>
int
</maxUCSeekingMemory>Maximum number of timesteps (including first of case) an individual will remember they are sick before resetting.
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → uncomplicatedCaseDuration
<uncomplicatedCaseDuration>
int
</uncomplicatedCaseDuration>Fixed length of an uncomplicated case of malarial/non-malarial sickness (from treatment seeking until return to life-as-usual). Usually 3.
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedCaseDuration
<complicatedCaseDuration>
int
</complicatedCaseDuration>Fixed length of a complicated/severe case of malaria (from treatment seeking until return to life-as-usual).
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedRiskDuration
<complicatedRiskDuration>
int
</complicatedRiskDuration>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.
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → dailyPrImmUCTS
<dailyPrImmUCTS>
double
</dailyPrImmUCTS>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.
→ scenario → healthSystem → EventScheduler → NonMalariaFevers
<NonMalariaFevers>
IN THIS ORDER:
| <prTreatment ... />
| <effectNegativeTest ... />
| <effectPositiveTest ... />
| <effectNeed ... />
| <effectInformal ... />
| <CFR ... />
| <TreatmentEfficacy ... />
</NonMalariaFevers>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).
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → prTreatment
<prTreatment>
double
</prTreatment>Probability of a non-malaria fever being treated with an antibiotic given that no malaria diagnostic was used but independent of need. Symbol: P₀.
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNegativeTest
<effectNegativeTest>
double
</effectNegativeTest>The effect of a negative malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₁).
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectPositiveTest
<effectPositiveTest>
double
</effectPositiveTest>The effect of a positive malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₂).
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNeed
<effectNeed>
double
</effectNeed>The effect of needing antibiotic treatment on the odds ratio of receiving antibiotics. Symbol: exp(β₃).
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectInformal
<effectInformal>
double
</effectInformal>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)
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → CFR
<CFR
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</CFR>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).
interpolation=("none" or "linear")Units: none
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.
→ scenario → healthSystem → CFR → group
<group
lowerbound=double
/>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).
lowerbound=doubleUnits: Years Min: 0 Max: 100
Lower bound of age group
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → TreatmentEfficacy
<TreatmentEfficacy>
double
</TreatmentEfficacy>Probability that treatment would prevent a death (i.e. CFR is multiplied by one minus this when treatment occurs).
→ 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
Description of "immediate outcomes" health system: Tediosi et al case management model (Case management as described in AJTMH 75 (suppl 2) pp90-103).
name=stringUnits: string
Name of health system
→ scenario → healthSystem → ImmediateOutcomes → drugRegimen
<drugRegimen
firstLine=string
secondLine=string
inpatient=string
/>Description of drug regimen
firstLine=stringUnits: Drug code
Code for first line drug
secondLine=stringUnits: Drug code
Code for second line drug
inpatient=stringUnits: Drug code
Code for drug used for treating inpatients
→ scenario → healthSystem → ImmediateOutcomes → initialACR
<initialACR>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
| <selfTreatment ... />
</initialACR>Initial cure rate
→ scenario → healthSystem → ImmediateOutcomes → initialACR → CQ
<CQ
value=double
/>Chloroquine
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → SP
<SP
value=double
/>Sulphadoxine-pyrimethamine
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → AQ
<AQ
value=double
/>Amodiaquine
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → SPAQ
<SPAQ
value=double
/>Sulphadoxine-pyrimethamine/Amodiaquine
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → ACT
<ACT
value=double
/>Artemisinine combination therapy
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → QN
<QN
value=double
/>Quinine
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → initialACR → selfTreatment
<selfTreatment
value=double
/>Probability of self-treatment
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → compliance
<compliance>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
| <selfTreatment ... />
</compliance>Adherence to treatment
→ scenario → healthSystem → ImmediateOutcomes → nonCompliersEffective
<nonCompliersEffective>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
| <selfTreatment ... />
</nonCompliersEffective>Effectiveness of treatment of non compliers
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated1
<pSeekOfficialCareUncomplicated1
value=double
/>Probability that a patient with newly incident uncomplicated disease seeks official care
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → pSelfTreatUncomplicated
<pSelfTreatUncomplicated
value=double
/>Probability that a patient with uncomplicated disease self-treats
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated2
<pSeekOfficialCareUncomplicated2
value=double
/>Probability that a patient with recurrence of uncomplicated disease seeks official care
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareSevere
<pSeekOfficialCareSevere
value=double
/>Probability that a patient with severe disease obtains appropriate care
value=doubleA double-precision floating-point value.
→ scenario → healthSystem → CFR
<CFR
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</CFR>Case fatality rate (probability of an inpatient fatality from a bout of severe malaria, per age-group).
interpolation=("none" or "linear")Units: none
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.
→ scenario → healthSystem → pSequelaeInpatient
<pSequelaeInpatient
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</pSequelaeInpatient>List of age specific probabilities of sequelae in inpatients, during a severe bout.
interpolation=("none" or "linear")Units: none
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.
→ scenario → interventions → changeEIR
<changeEIR
[ name=string ]
>
IN THIS ORDER:
| ( <timed ... /> )*
</changeEIR>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.
name=stringUnits: string
Name of intervention
→ scenario → interventions → changeEIR → timed
<timed
time=int
>
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</timed>Replacement transmission levels. Disables feedback of human infectiousness to mosquitoes on further mosquito to human transmission. Must last until end of simulation.
time=intUnits: time-steps Min: 0
Time-step at which this replacement occurs, starting from 0, the first intervention-period time-step.
→ scenario → entomology → nonVector → EIRDaily
<EIRDaily
[ origin=string ]
>
double
</EIRDaily>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.).
origin=string→ scenario → interventions → MDA
<MDA
[ name=string ]
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | [ <diagnostic ... /> ]
| | [ <description ... /> ]
| ( <timed ... /> )*
</MDA>Description and deployment of MDA interventions (can also be configured as screen and treat or intermittent preventative treatment with 1-day time-step models).
Currently neither diagnostic nor description need be provided for 5-day timestep model; this may change in the future.
name=stringUnits: string
Name of intervention
→ scenario → interventions → MDA → diagnostic
<diagnostic>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <deterministic ... />
| | <stochastic ... />
</diagnostic>Description of diagnostic used by mass treatment option of five-day case management model — may be used to model MDA without diagnostic or MSAT.
Drugs are administered whenever the test outcome is positive.
→ scenario → interventions → MDA → diagnostic → deterministic
<deterministic
minDensity=double
/>Specify that an artificial deterministic test is used: drugs are administered if parasite density is at least the minimum given.
minDensity=doubleUnits: parasites/microlitre Min: 0
The minimum density at which parasites can be detected. If 0, the test outcome is always positive.
→ scenario → interventions → MDA → diagnostic → stochastic
<stochastic
dens_50=double
specificity=double
/>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;
dens_50=doubleUnits: parasites/microlitre Min: 0
The density at which the test outcome has a 50% chance of being positive.
specificity=doubleUnits: none Min: 0 Max: 1
The probability of a positive test outcome in the absense of parasites.
→ scenario → interventions → MDA → description
<description
[ name=string ]
>
IN THIS ORDER:
| <decisions ... />
| <treatments ... />
</description>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.
A set of decisions and a set of treatments.
name=stringUnits: string
Name of set of interventions
→ scenario → interventions → MDA → timed
<timed
time=int
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
/>List of timed deployments of mass-drug-administration.
time=intUnits: time-steps Min: 0
Time-step at which this intervention occurs, starting from 0, the first intervention-period time-step.
maxAge=doubleUnits: Years Min: 0 Max: 100 Default value: 100
Maximum age of eligible individuals (defaults to 100)
minAge=doubleUnits: Years Min: 0 Max: 100 Default value: 0
Minimum age of eligible individuals (defaults to 0)
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort.
→ scenario → interventions → vaccine
<vaccine
[ name=string ]
>
IN THIS ORDER:
| ( <description ... /> ){0,3}
| ( <continuous ... /> )*
| ( <timed ... /> )*
</vaccine>Description and deployment of vaccine interventions.
name=stringUnits: string
Name of intervention
→ scenario → interventions → vaccine → description
<description
vaccineType=("PEV" or "BSV" or "TBV")
[ name=string ]
>
IN THIS ORDER:
| <decay ... />
| <efficacyB ... />
| ( <initialEfficacy ... /> )+
</description>List of vaccine descriptions
vaccineType=("PEV" or "BSV" or "TBV")Units: Code
Type of vaccine
name=stringUnits: string
Name of vaccine
→ scenario → interventions → vaccine → description → 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
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Specification of decay of efficacy
Specification of decay or survival of a parameter.
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=doubleUnits: 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=doubleUnits: 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.
mu=doubleMin: 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.
sigma=doubleMin: 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).
→ scenario → interventions → vaccine → description → efficacyB
<efficacyB
value=double
/>Measure of variation in vaccine efficacy
value=doubleA double-precision floating-point value.
→ scenario → interventions → vaccine → description → initialEfficacy
<initialEfficacy
value=double
/>value=doubleA double-precision floating-point value.
→ scenario → interventions → vaccine → continuous
<continuous
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>List of ages at which vaccination takes place (through EPI, post-natal and school-based programmes, etc.).
targetAgeYrs=doubleUnits: Years Min: 0 Max: 100
Target age of intervention
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort (default: false).
begin=intUnits: 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=intUnits: 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.
→ scenario → interventions → vaccine → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of timed mass vaccinations in the community
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → IPT
<IPT
[ name=string ]
>
IN THIS ORDER:
| <description ... />
| ( <continuous ... /> )*
| ( <timed ... /> )*
</IPT>Description and deployment of IPT interventions.
name=stringUnits: string
Name of intervention
→ scenario → interventions → IPT → description
<description
iptiEffect=int
[ name=string ]
>
IN THIS ORDER:
| ( <infGenotype ... /> )+
</description>iptiEffect=intUnits: List of Elementes
Description of ipti effect
name=stringUnits: string
Name of IPT intervention
→ scenario → interventions → IPT → description → infGenotype
<infGenotype
name=string
freq=double
ACR=double
proph=int
tolPeriod=int
atten=double
/>name=stringUnits: string
Name of age specific intervention
freq=doubleFrequency of parasite genotype
ACR=doubleAdequate clinical response (proportion)
proph=intProphylactic period
tolPeriod=intTolerance period
atten=doubleTolerance period
→ scenario → interventions → IPT → continuous
<continuous
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>List of ages at which IPTi/IPTc deployment takes place (through EPI, post-natal and school-based programmes, etc.).
targetAgeYrs=doubleUnits: Years Min: 0 Max: 100
Target age of intervention
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort (default: false).
begin=intUnits: 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=intUnits: 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.
→ scenario → interventions → IPT → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of timed IPTi/IPTc distribution
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → ITN
<ITN
[ name=string ]
>
IN THIS ORDER:
| <description ... />
| ( <continuous ... /> )*
| ( <timed ... /> )*
</ITN>Description and deployment of bed-net interventions (ITNs, LLINs).
name=stringUnits: string
Name of intervention
→ scenario → interventions → ITN → description
<description>
IN THIS ORDER:
| <usage ... />
| <holeRate ... />
| <ripRate ... />
| <ripFactor ... />
| <initialInsecticide ... />
| <insecticideDecay ... />
| <attritionOfNets ... />
| ( <anophelesParams ... /> )+
</description>- usage
- holeRate
- ripRate
- ripFactor
- initialInsecticide
- insecticideDecay
- attritionOfNets
- anophelesParams
→ scenario → interventions → ITN → description → usage
<usage
value=double
/>The proportion of the time during the night that humans use nets while indoors (fixed parameter).
At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-usagem) + intervention_factorusage*m where m is the proportion of time mosquitoes bite while humans are indoors.
value=doubleA double-precision floating-point value.
→ scenario → interventions → ITN → description → holeRate
<holeRate
mean=double
sigma=double
/>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.)
Parameters of a log-normal distribution.
Variates are sampled as: X ~ log N( log(mean)-sigma²/2, sigma² ).
mean=doubleUnits: (same as base units)
The mean of the lognormal distribution.
sigma=doubleSigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.
→ scenario → interventions → ITN → description → ripRate
<ripRate
mean=double
sigma=double
/>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.)
Parameters of a log-normal distribution.
Variates are sampled as: X ~ log N( log(mean)-sigma²/2, sigma² ).
mean=doubleUnits: (same as base units)
The mean of the lognormal distribution.
sigma=doubleSigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.
→ scenario → interventions → ITN → description → ripFactor
<ripFactor
value=double
/>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.
value=doubleA double-precision floating-point value.
→ scenario → interventions → ITN → description → initialInsecticide
<initialInsecticide
mu=double
sigma=double
/>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.
Parameters of a normal distribution.
Variates are sampled as: X ~ N( mu, sigma² ).
mu=doubleUnits: (same as base units)
The mean of the normal distribution.
sigma=doubleUnits: (same as base units)
The standard deviation of variates.
→ scenario → interventions → ITN → description → insecticideDecay
<insecticideDecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Decay curve for insecticide content of nets.
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.
Specification of decay or survival of a parameter.
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=doubleUnits: 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=doubleUnits: 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.
mu=doubleMin: 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.
sigma=doubleMin: 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).
→ scenario → interventions → ITN → description → attritionOfNets
<attritionOfNets
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
L=double
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Specifies the rate at which nets are disposed of over time.
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.
Specification of decay or survival of a parameter.
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=doubleUnits: 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=doubleUnits: 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.
mu=doubleMin: 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.
sigma=doubleMin: 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).
→ scenario → interventions → ITN → description → anophelesParams
<anophelesParams
mosquito=string
>
IN THIS ORDER:
| <deterrency ... />
| <preprandialKillingEffect ... />
| <postprandialKillingEffect ... />
</anophelesParams>mosquito=stringName of the affected anopheles-mosquito species.
→ scenario → interventions → ITN → description → anophelesParams → deterrency
<deterrency
holeFactor=double
insecticideFactor=double
interactionFactor=double
holeScalingFactor=double
insecticideScalingFactor=double
/>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).
holeFactor=doubleUnits: none Max: 1
Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.
insecticideFactor=doubleUnits: none Max: 1
Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.
interactionFactor=doubleUnits: 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.
holeScalingFactor=doubleUnits: none Min: 0
insecticideScalingFactor=doubleUnits: none Min: 0
→ scenario → interventions → ITN → description → anophelesParams → preprandialKillingEffect
<preprandialKillingEffect
baseFactor=double
/>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.
baseFactor=doubleUnits: none
→ scenario → interventions → ITN → description → anophelesParams → postprandialKillingEffect
<postprandialKillingEffect
baseFactor=double
/>Effect of net on survival mosquitoes as they seek to escape from a human host 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.
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.
baseFactor=doubleUnits: none
→ scenario → interventions → ITN → continuous
<continuous
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>List of ages at which bed-net deployment takes place (through EPI, post-natal and school-based programmes, etc.).
targetAgeYrs=doubleUnits: Years Min: 0 Max: 100
Target age of intervention
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort (default: false).
begin=intUnits: 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=intUnits: 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.
→ scenario → interventions → ITN → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of timed ITN deployment in the community
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → IRS
<IRS
[ name=string ]
>
IN THIS ORDER:
| <decay ... />
| ( <anophelesParams ... /> )+
| ( <timed ... /> )*
</IRS>Description and deployment of indoor insecticide interventions (IRS, durable wall linings, insecticide-treated-paint, etc.)
name=stringUnits: string
Name of intervention
→ scenario → interventions → IRS → 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
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Specification of decay or survival of a parameter.
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=doubleUnits: 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=doubleUnits: 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.
mu=doubleMin: 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.
sigma=doubleMin: 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).
→ scenario → interventions → IRS → anophelesParams
<anophelesParams>
IN THIS ORDER:
| <deterrency ... />
</anophelesParams>Descriptions of initial effectiveness of each of the effects of interventions. Decay is specified by a separate element (ITNDecay etc.)
→ scenario → interventions → IRS → anophelesParams → deterrency
<deterrency
value=double
/>One minus this multiplies the host's availability.
value=doubleA double-precision floating-point value.
→ scenario → interventions → IRS → anophelesParams → killingEffect
<killingEffect
value=double
/>One minus this multiplies the survival rate of resting mosquitoes.
value=doubleA double-precision floating-point value.
→ scenario → interventions → IRS → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of timed IRS deployment in the community
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → vectorDeterrent
<vectorDeterrent
[ name=string ]
>
IN THIS ORDER:
| <decay ... />
| ( <anophelesParams ... /> )+
| ( <timed ... /> )*
</vectorDeterrent>Description and deployment of interventions affecting only human-mosquito availability (deterrents).
name=stringUnits: string
Name of intervention
→ scenario → interventions → vectorDeterrent → 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
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Specification of decay or survival of a parameter.
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=doubleUnits: 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=doubleUnits: 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.
mu=doubleMin: 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.
sigma=doubleMin: 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).
→ scenario → interventions → vectorDeterrent → anophelesParams
<anophelesParams
mosquito=string
>
IN THIS ORDER:
| <deterrency ... />
</anophelesParams>Descriptions of initial effectiveness of each of the effects of interventions. Decay is specified by a separate element (ITNDecay etc.)
mosquito=stringName of the affected anopheles-mosquito species.
→ scenario → interventions → vectorDeterrent → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of timed mosquito deterrent deployment in the community
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → cohort
<cohort
[ name=string ]
>
IN THIS ORDER:
| ( <continuous ... /> )*
| ( <timed ... /> )*
</cohort>Recruitment of cohort as a pseudo-intervention.
name=stringUnits: string
Name of intervention
→ scenario → interventions → cohort → continuous
<continuous
targetAgeYrs=double
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
[ begin=int ] DEFAULT VALUE 0
[ end=int ] DEFAULT VALUE 2147483647
/>List of ages at which cohort recruitment takes place.
targetAgeYrs=doubleUnits: Years Min: 0 Max: 100
Target age of intervention
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort (default: false).
begin=intUnits: 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=intUnits: 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.
→ scenario → interventions → cohort → timed
<timed
[ cumulativeWithMaxAge=double ]
/>List of times of mass cohort selection.
cumulativeWithMaxAge=doubleUnits: 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).
→ scenario → interventions → importedInfections
<importedInfections
[ name=string ]
>
IN THIS ORDER:
| <timed ... />
</importedInfections>Models importation of P. falciparum infections directly into humans from an external source. This is infections, not inoculations or EIR being imported.
name=stringUnits: string
Name of intervention
→ scenario → interventions → importedInfections → timed
<timed
[ period=int ] DEFAULT VALUE 0
>
IN THIS ORDER:
| ( <rate ... /> )+
</timed>Rate of case importation, as a step function. Each value is valid until replaced by the next value.
period=intUnits: time-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 (timestep period+2 has same value as timestep 2, etc.).
→ scenario → interventions → importedInfections → timed → rate
<rate
time=int
/>A time-rate pair.
time=intUnits: time-steps Min: 0
→ scenario → interventions → immuneSuppression
<immuneSuppression>
IN THIS ORDER:
| ( <timed ... /> )*
</immuneSuppression>Removes all exposure-related immunity gained over time by hosts without removing infections.
Hypothetical, but potentially useful to simulate scenarios with unprotected humans.
→ scenario → interventions → immuneSuppression → timed
<timed
time=int
[ maxAge=double ] DEFAULT VALUE 100
[ minAge=double ] DEFAULT VALUE 0
coverage=double
[ cohort=boolean ] DEFAULT VALUE false
/>time=intUnits: time-steps Min: 0
Time-step at which this intervention occurs, starting from 0, the first intervention-period time-step.
maxAge=doubleUnits: Years Min: 0 Max: 100 Default value: 100
Maximum age of eligible individuals (defaults to 100)
minAge=doubleUnits: Years Min: 0 Max: 100 Default value: 0
Minimum age of eligible individuals (defaults to 0)
coverage=doubleUnits: Proportion Min: 0 Max: 1
Coverage of intervention
cohort=booleanUnits: Proportion Min: 0 Max: 1 Default value: false
Restrict distribution to chosen cohort.
→ scenario → interventions → insertR_0Case
<insertR_0Case>
IN THIS ORDER:
| ( <timed ... /> )*
</insertR_0Case>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.
→ scenario → interventions → insertR_0Case → timed
<timed
time=int
/>time=intUnits: time-steps Min: 0
Time-step at which this intervention occurs, starting from 0, the first intervention-period time-step.
→ scenario → interventions → uninfectVectors
<uninfectVectors>
IN THIS ORDER:
| ( <timed ... /> )*
</uninfectVectors>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.
→ scenario → interventions → uninfectVectors → timed
<timed
time=int
/>time=intUnits: time-steps Min: 0
Time-step at which this intervention occurs, starting from 0, the first intervention-period time-step.
→ scenario → interventions → larviciding
<larviciding
[ name=string ]
>
IN THIS ORDER:
| ( <anopheles ... /> )+
</larviciding>Simple larviciding intervention description.
name=stringUnits: string
Name of intervention
→ scenario → interventions → larviciding → anopheles
<anopheles
mosquito=string
effectiveness=double
duration=int
/>mosquito=stringMosquito to be larvicided
effectiveness=doubleUnits: none Min: 0 Max: 1
Proportional reduction in emergence rate
duration=intUnits: days Min: 0 Max: inf
Number of days for which the intervention is active.
→ scenario → healthSystem
<healthSystem>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EventScheduler ... />
| | <ImmediateOutcomes ... />
| <CFR ... />
| <pSequelaeInpatient ... />
</healthSystem>Description of health system.
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.
→ scenario → entomology
<entomology
name=string
mode=("2" or "4")
[ annualEIR=double ]
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <nonVector ... />
| | <vector ... />
</entomology>Description of entomological data
name=stringUnits: string
Name of entomology data
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.
annualEIR=doubleUnits: 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.
→ scenario → entomology → nonVector
<nonVector
eipDuration=int
>
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</nonVector>Description of transmission setting for models without vector control interventions (included for backward compatibility)
eipDuration=intThe duration of sporogony in days
→ scenario → entomology → vector
<vector>
IN THIS ORDER:
| ( <anopheles ... /> )+
| ( <nonHumanHosts ... /> )*
</vector>Parameters of the transmission model.
→ scenario → entomology → vector → anopheles
<anopheles
mosquito=string
propInfected=double
propInfectious=double
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EIR ... />
| | <monthlyEIR ... />
| <mosq ... />
| ( <nonHumanHosts ... /> )*
</anopheles>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
mosquito=stringIdentifier for this anopheles species
propInfected=doubleUnits: 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.
propInfectious=doubleUnits: 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.
→ scenario → entomology → vector → anopheles → EIR
<EIR
a0=double
a1=double
b1=double
a2=double
b2=double
EIRRotateAngle=double
/>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.
a0=doublea0 parameter of Fourier approximation to ln(EIR)
a1=doublea1 parameter of Fourier approximation to ln(EIR)
b1=doubleb1 parameter of Fourier approximation to ln(EIR)
a2=doublea2 parameter of Fourier approximation to ln(EIR)
b2=doubleb2 parameter of Fourier approximation to ln(EIR)
EIRRotateAngle=doubleUnits: radians
Rotation angle defining the origin of the Fourier approximation to ln (EIR)
→ scenario → entomology → vector → anopheles → monthlyEIR
<monthlyEIR
annualEIR=double
>
IN THIS ORDER:
| ( <item ... /> ){12,12}
</monthlyEIR>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.
annualEIR=doubleUnits: Infectious bites per adult per year Min: 0
Scales the monthly values to give this annual innoculation rate.
→ scenario → entomology → vector → anopheles → monthlyEIR → item
<item>
double
</item>Inoculations per person per month
→ scenario → entomology → 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
/>Vector species
mosqRestDuration=intname:Duration of the resting period of the vector (days);
extrinsicIncubationPeriod=intname:Extrinsic incubation period (days)
mosqLaidEggsSameDayProportion=doubleProportion of mosquitoes host seeking on same day as ovipositing
mosqSeekingDuration=doubleDuration of the host-seeking period of the vector (days)
mosqSurvivalFeedingCycleProbability=doubleProbability that the mosquito survives the feeding cycle
mosqProbBiting=doubleProbability that the mosquito succesfully bites chosen host
mosqProbFindRestSite=doubleProbability that the mosquito escapes host and finds a resting place after biting
mosqProbResting=doubleProbability of mosquito successfully resting after finding a resting site
mosqProbOvipositing=doubleProbability of a mosquito successfully laying eggs given that it has rested
mosqHumanBloodIndex=doubleThe proportion of resting mosquitoes which fed on human blood during the last feed.
minInfectedThreshold=doubleMin: 0
If less than this many mosquitoes remain infected, transmission is interrupted.
→ scenario → entomology → vector → anopheles → nonHumanHosts
<nonHumanHosts
name=string
mosqRelativeEntoAvailability=double
mosqProbBiting=double
mosqProbFindRestSite=double
mosqProbResting=double
/>Non human host parameters, per type of host (must match up with non-species-specific parameters).
name=stringIdentifier for this category of non-human hosts
mosqRelativeEntoAvailability=doubleRelative availability of nonhuman hosts of type i (to other nonhuman hosts)
mosqProbBiting=doubleProbability of mosquito successfully biting host
mosqProbFindRestSite=doubleProbability that the mosquito escapes host and finds a resting place after biting
mosqProbResting=doubleProbability of mosquito successfully resting after finding a resting site
→ scenario → entomology → vector → nonHumanHosts
<nonHumanHosts
name=string
number=double
/>name=stringUnits: List of elements
Name of this species of non human hosts (must match up with those described per anopheles section)
number=double→ scenario → pharmacology
<pharmacology>
IN THIS ORDER:
| ( <drug ... /> )+
</pharmacology>Drug model parameters
→ scenario → pharmacology → drug
<drug
abbrev=string
>
IN THIS ORDER:
| <PD ... />
| <PK ... />
</drug>Sequence of drug descriptions forming a library of drug parameters.
abbrev=string→ scenario → pharmacology → drug → PD
<PD>
IN THIS ORDER:
| ( <allele ... /> )+
</PD>→ scenario → pharmacology → drug → PD → allele
<allele
name=string
>
IN THIS ORDER:
| <initial_frequency ... />
| <max_killing_rate ... />
| <IC50 ... />
| <slope ... />
</allele>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.
name=string→ scenario → pharmacology → drug → PD → allele → initial_frequency
<initial_frequency>
double
</initial_frequency>Frequency, relative to the total frequency of all alleles for this drug/locus.
→ scenario → pharmacology → drug → PD → allele → max_killing_rate
<max_killing_rate>
double
</max_killing_rate>k1 — Maximal parasite killing rate.
→ scenario → pharmacology → drug → PD → allele → IC50
<IC50>
double
</IC50>Half maximal effect concentration.
→ scenario → pharmacology → drug → PD → allele → slope
<slope>
double
</slope>n — Slope of the concentration effect curve
→ scenario → pharmacology → drug → PK
<PK>
IN THIS ORDER:
| <negligible_concentration ... />
| <half_life ... />
| <vol_dist ... />
</PK>→ scenario → pharmacology → drug → PK → negligible_concentration
<negligible_concentration>
double
</negligible_concentration>Concentration below which drug's effects are deemed negligible and can be removed from simulation.
→ scenario → pharmacology → drug → PK → half_life
<half_life>
double
</half_life>Used to calculate elimination rate (which is: ln(2) / half_life).
→ scenario → pharmacology → drug → PK → vol_dist
<vol_dist>
double
</vol_dist>Volume of Distribution
<model>
IN ANY ORDER:
| <ModelOptions ... />
| <clinical ... />
| <human ... />
| <parameters ... />
</model>Encapsulation of all parameters which describe the model according to which fitting is done.
→ scenario → model → ModelOptions
<ModelOptions>
IN THIS ORDER:
| ( <option ... /> )*
</ModelOptions>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
healthSystemMemory=int
>
IN THIS ORDER:
| [ <NonMalariaFevers ... /> ]
</clinical>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.
healthSystemMemory=intUnits: Time steps Min: 1 Max: 100
Follow-up period during which a recurrence is considered to be a treatment failure
→ scenario → model → clinical → NonMalariaFevers
<NonMalariaFevers>
IN THIS ORDER:
| <incidence ... />
| <prNeedTreatmentNMF ... />
| <prNeedTreatmentMF ... />
</NonMalariaFevers>Description of non-malaria fever incidence. Non-malaria fevers are only modelled if the NON_MALARIA_FEVERS option is used.
→ scenario → model → clinical → NonMalariaFevers → incidence
<incidence
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</incidence>Probability that a non-malaria fever occurs given that no concurrent malaria fever occurs.
interpolation=("none" or "linear")Units: none
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.
→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentNMF
<prNeedTreatmentNMF
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentNMF>Probability that a non-malaria fever needs treatment with antibiotics (assuming fever is not induced by malaria, although concurrent parasites may be present).
interpolation=("none" or "linear")Units: none
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.
→ scenario → model → clinical → NonMalariaFevers → prNeedTreatmentMF
<prNeedTreatmentMF
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</prNeedTreatmentMF>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.
interpolation=("none" or "linear")Units: none
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>
IN THIS ORDER:
| <availabilityToMosquitoes ... />
| [ <weight ... /> ]
</human>Parameters of host models.
→ scenario → model → human → availabilityToMosquitoes
<availabilityToMosquitoes
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</availabilityToMosquitoes>By age group data on availability of humans to mosquitoes relative to an adult.
interpolation=("none" or "linear")Units: none
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.
→ scenario → model → human → weight
<weight
multStdDev=double
>
IN THIS ORDER:
| ( <group ... /> )+
</weight>By age group data on human weight (mass).
multStdDev=doubleUnits: 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.
→ scenario → model → parameters
<parameters
interval=int
iseed=int
latentp=int
>
IN THIS ORDER:
| ( <parameter ... /> )+
</parameters>Parameters of the epidemiological model
interval=intUnits: Days
Simulation step
iseed=intUnits: Number
Seed for RNG
latentp=intUnits: Time steps Min: 0 Max: 20
pre-erythrocytic latent period, in time steps
→ scenario → model → parameters → parameter
<parameter
name=string
number=int
value=double
include=boolean
/>name=stringUnits: string
Name of parameter
number=intUnits: Number Min: 1 Max: 100
Reference number of input parameter
value=doubleUnits: Number Min: 0
Parameter value
include=booleanUnits: Number Min: 0 Max: 1
True if parameter is to be sampled in optimization runs. Not used in simulator app.