schema 34 intervs
Generated schema 34 documentation (interventions)
This page is automatically generated from the following schema file: scenario_34.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
Preventative interventions
<interventions
name=string
>
IN ANY ORDER:
| [ <changeHS ... /> ]
| [ <changeEIR ... /> ]
| [ <importedInfections ... /> ]
| [ <insertR_0Case ... /> ]
| [ <uninfectVectors ... /> ]
| [ <vectorPop ... /> ]
| [ <human ... /> ]
</interventions>Documentation (element)
List of interventions. Generally these are either point-time distributions of something to some subset of the population, or continuous-time distribution targetting individuals when they reach a certain age.
Attributes
Name of intervention set
name=stringName of set of interventions
Change health system
→ scenario → interventions → changeHS
<changeHS
[ name=string ]
>
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</changeHS>Documentation (element)
Changes to the health system
Attributes
Name of intervention
name=stringName of intervention
timedDeployment
→ scenario → interventions → changeHS → timedDeployment
<timedDeployment
time=string
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <EventScheduler ... />
| | <ImmediateOutcomes ... />
| | <DecisionTree5Day ... />
| <CFR ... />
| <pSequelaeInpatient ... />
</timedDeployment>Documentation (type)
A complete replacement health system. Replaces all previous properties. (Health system can be replaced multiple times if necessary.)
Documentation (base type)
Description of case management system, used to specify the initial model or a replacement (an intervention). Encompasses case management data and some other data required to derive case outcomes.
Contains a sub-element describing the particular health-system in use. Health system data is here defined as data used to decide on a treatment strategy, given a case requiring treatment.
Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this replacement occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
uncomplicated
→ scenario → healthSystem → EventScheduler → uncomplicated
<uncomplicated
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</uncomplicated>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
multiple
→ scenario → interventions → human → component → decisionTree → multiple
<multiple
[ name=string ]
>
IN THIS ORDER:
| ( <caseType ... /> )*
| ( <diagnostic ... /> )*
| ( <random ... /> )*
| ( <age ... /> )*
| ( <treatPKPD ... /> )*
| [ <treatSimple ... /> ]
| ( <deploy ... /> )*
</multiple>Documentation (type)
A special node allowing multiple sub-trees to be evaluated.
This is different from an ordinary decision tree node in that:
a) multiple types of child can occur simultaneously (e.g. multiple types of treatment or treatment plus a 'random' sub-tree)
b) the 'noTreatment' and 'treatFailure' nodes are not allowed
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
caseType
→ scenario → interventions → human → component → decisionTree → multiple → caseType
<caseType
[ name=string ]
>
IN ANY ORDER:
| <firstLine ... />
| <secondLine ... />
</caseType>Documentation (type)
A switch which choses a branch deterministically, based on whether the patient was treated recently (second line) or not (first line).
For uncomplicated cases only.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
firstLine
→ scenario → interventions → human → component → decisionTree → multiple → caseType → firstLine
<firstLine
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</firstLine>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
caseType
→ scenario → interventions → human → component → decisionTree → caseType
<caseType
[ name=string ]
>
IN ANY ORDER:
| <firstLine ... />
| <secondLine ... />
</caseType>Documentation (type)
A switch which choses a branch deterministically, based on whether the patient was treated recently (second line) or not (first line).
For uncomplicated cases only.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
secondLine
→ scenario → interventions → human → component → decisionTree → multiple → caseType → secondLine
<secondLine
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</secondLine>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
diagnostic
→ scenario → interventions → human → component → decisionTree → diagnostic
<diagnostic
diagnostic=string
[ name=string ]
>
IN ANY ORDER:
| <positive ... />
| <negative ... />
</diagnostic>Documentation (type)
A switch which choses a branch deterministically, based on the outcome of some type of diagnostic.
Attributes
Name of diagnostic
diagnostic=stringShould match the name of some parameterised diagnostic (see scenario/diagnostics).
Name
name=stringAn optional piece of documentation attached to this node.
positive
→ scenario → interventions → human → component → decisionTree → multiple → diagnostic → positive
<positive
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</positive>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
random
→ scenario → interventions → human → component → decisionTree → random
<random
[ name=string ]
>
IN THIS ORDER:
| ( <outcome ... /> )+
</random>Documentation (type)
A switch which choses a branch randomly.
Each branch must be listed with a probability; the sum of all these probabilities must equal 1.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
outcome
→ scenario → interventions → human → component → decisionTree → multiple → random → outcome
<outcome
p=double
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</outcome>Documentation (base type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Probability
p=doubleUnits: None Min: 0 Max: 1
Probability of selecting this outcome. The sum of probabilities across all outcomes must be 1.
age
→ scenario → interventions → human → component → decisionTree → age
<age
[ name=string ]
>
IN THIS ORDER:
| ( <age ... /> )+
</age>Documentation (type)
A switch which choses a branch deterministically, based on the patient's age (in years).
Categories must uniquely cover all ages from birth, with no upper bound. Categories must be listed in order of age, increasing; the first must have lower bound 0. Upper bounds are equal to the lower bound of the next category, (but are exclusive where lower bounds are inclusive); the last category has no upper bound.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
Age range
→ scenario → interventions → human → component → decisionTree → multiple → age → age
<age
lb=double
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</age>Documentation (element)
Describes a branch, selected for patients of a certain age.
Documentation (base type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Lower bound (inclusive)
lb=doubleMin: 0
noTreatment
→ scenario → interventions → human → component → decisionTree → noTreatment
<noTreatment
[ name=string ]
/>Documentation (type)
An end node doing nothing. This exists to explicitly state that no treatment happens and to prevent trees from accidentally being left incomplete.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
treatFailure
→ scenario → interventions → human → component → decisionTree → treatFailure
<treatFailure
[ name=string ]
/>Documentation (type)
An end node which reports treatment but does not change parasitalogical status. This allows correct labelling of second-line cases.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
treatPKPD
→ scenario → interventions → human → component → decisionTree → treatPKPD
<treatPKPD
schedule=string
dosage=string
[ delay_h=double ] DEFAULT VALUE 0
/>Documentation (type)
A command to administer drugs according to a given schedule and dosage table, optionally with a delay.
Attributes
Name of treatment schedule
schedule=stringThe name of a schedule to use for treatment.
Name of dosage table
dosage=stringThe name of a dosage table to use for treatment.
Delay (hours)
delay_h=doubleDefault value: 0
Optionally, this can be given to delay the start of treatment by a given number of hours. If not specified, treatment is not delayed. If a delay is given, all medications within the treatment schedule used are delayed by this number of hours.
treatSimple
→ scenario → interventions → human → component → decisionTree → treatSimple
<treatSimple
durationLiver=string
durationBlood=string
/>Documentation (type)
Simple treatment model, targetting liver- and/or blood-stage infections. This is all-or-nothing treatment which, when deploymed, completely clears all infections of the targetted stages. This makes it unsuitable for modeling resistance, but suitable for use with simple infection models.
Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.
Attributes
Length of liver-stage effect
durationLiver=stringUnits: User defined
Controls action on liver-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all liver-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
Length of blood-stage effect
durationBlood=stringUnits: User defined
Controls action on blood-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all blood-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
deploy
→ scenario → interventions → human → component → decisionTree → deploy
<deploy
component=string
/>Documentation (type)
Deploy one or more intervention components.
Attributes
Component identifier
component=stringThe identifier (short name) of a component.
negative
→ scenario → interventions → human → component → decisionTree → multiple → diagnostic → negative
<negative
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</negative>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
diagnostic
→ scenario → interventions → human → component → decisionTree → multiple → diagnostic
<diagnostic
diagnostic=string
[ name=string ]
>
IN ANY ORDER:
| <positive ... />
| <negative ... />
</diagnostic>Documentation (type)
A switch which choses a branch deterministically, based on the outcome of some type of diagnostic.
Attributes
Name of diagnostic
diagnostic=stringShould match the name of some parameterised diagnostic (see scenario/diagnostics).
Name
name=stringAn optional piece of documentation attached to this node.
random
→ scenario → interventions → human → component → decisionTree → multiple → random
<random
[ name=string ]
>
IN THIS ORDER:
| ( <outcome ... /> )+
</random>Documentation (type)
A switch which choses a branch randomly.
Each branch must be listed with a probability; the sum of all these probabilities must equal 1.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
age
→ scenario → interventions → human → component → decisionTree → multiple → age
<age
[ name=string ]
>
IN THIS ORDER:
| ( <age ... /> )+
</age>Documentation (type)
A switch which choses a branch deterministically, based on the patient's age (in years).
Categories must uniquely cover all ages from birth, with no upper bound. Categories must be listed in order of age, increasing; the first must have lower bound 0. Upper bounds are equal to the lower bound of the next category, (but are exclusive where lower bounds are inclusive); the last category has no upper bound.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
treatPKPD
→ scenario → interventions → human → component → decisionTree → multiple → treatPKPD
<treatPKPD
schedule=string
dosage=string
[ delay_h=double ] DEFAULT VALUE 0
/>Documentation (type)
A command to administer drugs according to a given schedule and dosage table, optionally with a delay.
Attributes
Name of treatment schedule
schedule=stringThe name of a schedule to use for treatment.
Name of dosage table
dosage=stringThe name of a dosage table to use for treatment.
Delay (hours)
delay_h=doubleDefault value: 0
Optionally, this can be given to delay the start of treatment by a given number of hours. If not specified, treatment is not delayed. If a delay is given, all medications within the treatment schedule used are delayed by this number of hours.
treatSimple
→ scenario → interventions → human → component → decisionTree → multiple → treatSimple
<treatSimple
durationLiver=string
durationBlood=string
/>Documentation (type)
Simple treatment model, targetting liver- and/or blood-stage infections. This is all-or-nothing treatment which, when deploymed, completely clears all infections of the targetted stages. This makes it unsuitable for modeling resistance, but suitable for use with simple infection models.
Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.
Attributes
Length of liver-stage effect
durationLiver=stringUnits: User defined
Controls action on liver-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all liver-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
Length of blood-stage effect
durationBlood=stringUnits: User defined
Controls action on blood-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all blood-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
deploy
→ scenario → interventions → human → component → decisionTree → multiple → deploy
<deploy
component=string
/>Documentation (type)
Deploy one or more intervention components.
Attributes
Component identifier
component=stringThe identifier (short name) of a component.
complicated
→ scenario → healthSystem → EventScheduler → complicated
<complicated
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</complicated>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
ClinicalOutcomes
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes
<ClinicalOutcomes>
IN THIS ORDER:
| <maxUCSeekingMemory ... />
| <uncomplicatedCaseDuration ... />
| <complicatedCaseDuration ... />
| <complicatedRiskDuration ... />
| ( <dailyPrImmUCTS ... /> )+
</ClinicalOutcomes>- maxUCSeekingMemory
- uncomplicatedCaseDuration
- complicatedCaseDuration
- complicatedRiskDuration
- dailyPrImmUCTS
Documentation (type)
Description of base parameters of the clinical model.
Max UC treatment-seeking memory
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → maxUCSeekingMemory
<maxUCSeekingMemory>
int
</maxUCSeekingMemory>Documentation (element)
Units: Days Min: 0 Max: unbounded
Maximum number of timesteps (including first day of case) that an individual with an uncomplicated case of malaria will remember he/she was sick before resetting.
Uncomplicated case duration
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → uncomplicatedCaseDuration
<uncomplicatedCaseDuration>
int
</uncomplicatedCaseDuration>Documentation (element)
Units: Days Min: 1 Max: unbounded
Fixed length of an uncomplicated case of malarial or non-malarial sickness (from treatment seeking until return to life-as-usual). Usually 3.
Complicated case duration
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedCaseDuration
<complicatedCaseDuration>
int
</complicatedCaseDuration>Documentation (element)
Units: Days Min: 1 Max: unbounded
Fixed length of a complicated or severe case of malaria (from treatment seeking until return to life-as-usual).
Complicated risk duration
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → complicatedRiskDuration
<complicatedRiskDuration>
int
</complicatedRiskDuration>Documentation (element)
Units: Days Min: 1 Max: unbounded
Number of days for which humans are at risk of death during a severe or complicated case of malaria. Cannot be greater than the duration of a complicated case or less than 1 day.
Daily probability of immediate treatment seeking for uncomplicated cases
→ scenario → healthSystem → EventScheduler → ClinicalOutcomes → dailyPrImmUCTS
<dailyPrImmUCTS>
double
</dailyPrImmUCTS>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
It is sometimes desirable to model delays to treatment-seeking in uncomplicated cases. While treatment of drugs can be delayed within case management trees to provide a similar effect, this doesn't delay any of the decisions, including diagnostics using the current parasite density.
Instead a list of dailyPrImmUCTS elements can be used, describing successive daily probabilities of treatment (sum must be 1). For example, with a list of two elements with values 0.8 and 0.2, for 80% of UC cases the decision tree is evaluated immediately, and for 20% of cases evaluation is delayed by one day.
For no delay, use one element with a value of 1.
NonMalariaFevers
→ scenario → healthSystem → EventScheduler → NonMalariaFevers
<NonMalariaFevers>
IN THIS ORDER:
| <prTreatment ... />
| <effectNegativeTest ... />
| <effectPositiveTest ... />
| <effectNeed ... />
| <effectInformal ... />
| <CFR ... />
| <TreatmentEfficacy ... />
</NonMalariaFevers>Documentation (type)
Description of non-malaria fever health-system modelling (treatment, outcomes and costing). Incidence is described by the model->clinical->NonMalariaFevers element. Non-malaria fevers are only modelled if the NON_MALARIA_FEVERS option is used.
As further explanation of the parameters below, we first take: β₀ = logit(P₀) - β₃·P(need), and then calculate the probability of antibiotic administration, P(AB), dependent on treatment seeking location. No seeking: P(AB) = 0 Informal sector: logit(P(AB)) = β₀ + β₄ Health facility: logit(P(AB)) = β₀ + β₁·I(neg) + β₂·I(pos) + β₃·I(need) (where I(X) is 1 when event X is true and 0 otherwise, logit(p)=log(p/(1-p)), event "need" is the event that death may occur without treatment, events "neg" and "pos" are the events that a malaria parasite diagnositic was used and indicated no parasites and parasites respectively).
P(treatment|no diagnostic)
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → prTreatment
<prTreatment>
double
</prTreatment>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability of a non-malaria fever being treated with an antibiotic given that no malaria diagnostic was used but independent of need. Symbol: P₀.
Effect of a negative test
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNegativeTest
<effectNegativeTest>
double
</effectNegativeTest>Documentation (element)
The effect of a negative malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₁).
Effect of a positive test
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectPositiveTest
<effectPositiveTest>
double
</effectPositiveTest>Documentation (element)
The effect of a positive malaria diagnostic on the odds ratio of receiving antibiotics. Symbol: exp(β₂).
Effect of need
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectNeed
<effectNeed>
double
</effectNeed>Documentation (element)
The effect of needing antibiotic treatment on the odds ratio of receiving antibiotics. Symbol: exp(β₃).
Effect of informal provider
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → effectInformal
<effectInformal>
double
</effectInformal>Documentation (element)
The effect of seeking treatment from an informal provider (i.e. a provider untrained in NMF diagnosis) on the odds ratio of receiving antibiotics. Symbol: exp(β₄)
Case fatality rate
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → CFR
<CFR
[ interpolation=("none" or "linear") ]
>
IN THIS ORDER:
| ( <group ... /> )+
</CFR>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Base case fatality rate for non-malaria fevers (probability of death from a fever requiring antibiotic treatment given that no antibiotic treatment is received, per age-group).
Attributes
interpolation
interpolation=("none" or "linear")Interpolation algorithm. Normally it is desirable for age-based values to be continuous w.r.t. age. By default linear interpolation is used. With all algorithms except "none", the age groups are converted to a set of points centred within each age range. Extra points are added at each end (zero and infinity) to keep value constant at both ends of the function. A zero-length age group may be used as a kind of barrier to adjust the distribution; e.g. with age group boundaries at 15, 20 and 25 years, a (linear) spline would be drawn between ages 17.5 and 22.5, whereas with boundaries at 15, 20 and 20 years, a spline would be drawn between ages 17.5 and 20 years (may be desired if individuals are assumed to reach adult size at 20). Algorithms:
- none: input values are used directly
- linear: straight lines (on an age vs. value graph) are used to interpolate data points.
age group
→ scenario → healthSystem → CFR → group
<group
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
Lower bound
lowerbound=doubleUnits: Years Min: 0 Max: 100
Lower bound of age group
Treatment efficacy
→ scenario → healthSystem → EventScheduler → NonMalariaFevers → TreatmentEfficacy
<TreatmentEfficacy>
double
</TreatmentEfficacy>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that treatment would prevent a death (i.e. CFR is multiplied by one minus this when treatment occurs).
Description of drug regimen
→ scenario → healthSystem → ImmediateOutcomes → drugRegimen
<drugRegimen
firstLine=string
secondLine=string
inpatient=string
/>Documentation (element)
Description of drug regimen.
Attributes
First line drug
firstLine=stringUnits: Drug code
Code for first line drug
Second line drug
secondLine=stringUnits: Drug code
Code for second line drug
Drug use for treating inpatients
inpatient=stringUnits: 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: Dimensionless Min: 0.0 Max: 1.0
Initial cure rate
Chloroquine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → CQ
<CQ
value=double
/>Documentation (element)
Chloroquine
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Sulphadoxine-pyrimethamine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → SP
<SP
value=double
/>Documentation (element)
Sulphadoxine-pyrimethamine
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Amodiaquine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → AQ
<AQ
value=double
/>Documentation (element)
Amodiaquine
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Sulphadoxine-pyrimethamine/Amodiaquine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → SPAQ
<SPAQ
value=double
/>Documentation (element)
Sulphadoxine-pyrimethamine/Amodiaquine
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Artemisinine based combination therapy
→ scenario → healthSystem → ImmediateOutcomes → initialACR → ACT
<ACT
value=double
/>Documentation (element)
Artemisinine combination therapy
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Quinine
→ scenario → healthSystem → ImmediateOutcomes → initialACR → QN
<QN
value=double
/>Documentation (element)
Quinine
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
selfTreatment
→ scenario → healthSystem → ImmediateOutcomes → initialACR → selfTreatment
<selfTreatment
value=double
/>Documentation (element)
Units: Dimensionless Min: 0 Max: 1name:P(self-treat)
Probability of self-treatment
Attributes
Input parameter value
value=doubleA 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: Dimensionless Min: 0.0 Max: 1.0
Adherence to treatment
Effectiveness of treatment in non-adherent patients
→ scenario → healthSystem → ImmediateOutcomes → nonCompliersEffective
<nonCompliersEffective>
IN THIS ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
| <selfTreatment ... />
</nonCompliersEffective>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Effectiveness of treatment for non-compliant patients
treatmentActions
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions
<treatmentActions>
IN ANY ORDER:
| [ <CQ ... /> ]
| [ <SP ... /> ]
| [ <AQ ... /> ]
| [ <SPAQ ... /> ]
| [ <ACT ... /> ]
| [ <QN ... /> ]
</treatmentActions>CQ
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ
<CQ
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</CQ>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
deploy
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ → deploy
<deploy
[ maxAge=double ]
[ minAge=double ] DEFAULT VALUE 0
[ p=double ] DEFAULT VALUE 1
>
IN THIS ORDER:
| ( <component ... /> )+
</deploy>Attributes
Maximum age of eligible humans
maxAge=doubleUnits: Years Min: 0
Maximum age of eligible humans (defaults to no limit). Input is rounded to the nearest time step.
Minimum age of eligible humans
minAge=doubleUnits: Years Min: 0
Default value: 0
Minimum age of eligible humans (defaults to 0). Input is rounded to the nearest time step.
Probability of delivery to eligible humans
p=doubleUnits: dimensionless Min: 0 Max: 1
Default value: 1
Probability of this list of components being deployed, given that other constraints are met.
component
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → CQ → deploy → component
<component
id=string
/>Documentation (type)
The list of components deployed to eligible humans.
Attributes
Identifier
id=stringThe identifier (short name) of a component.
Prophylactic treatment
→ scenario → healthSystem → DecisionTree5Day → treatmentSevere → clearInfections
<clearInfections
timesteps=string
stage=("liver" or "blood" or "both")
/>Documentation (element)
This clears infections according to several options: it can clear all blood stage infections, all liver stage infections or both, and it can act on multiple timesteps. To have a probability of no action add another treatment option (which does nothing) and set the probabilities of selection appropriately.
This allows immediate (legacy) or delayed action, a prophylactic period, and selection of which stages are targeted. It is a simple model but appropriate enough for use with the five day timestep when assuming no resistance and that drug failure is mainly caused by bad drugs or compliance.
The old treatment action for the five-day timestep model is essentially this, with immediateAction (timesteps=-1) and stage=both, except for the IPT model's SP action, which was more like with timesteps>1 and stage=blood.
Attributes
Length of effect
timesteps=stringUnits: User defined (defaults to steps)
The number of timesteps during which this action remains in effect (e.g. 2 means clear infections during the next two timestep updates). Full clearance of the targeted stages occurs during this time. A special value of -1 means act immediately (retrospectively); this the old behaviour. A value of 1 means act on the next timestep only. Both of these can be thought of as a model for short-acting effective drug treatment; the main differences are that the latter means parasite densities will remain high from the point of view of surveys and diagnostics (i.e. mass screen and treat) used before the next timestep and that the latter will also remove infections starting the next timestep. Arguably the latter is a better model, but the differences are perhaps small, excepting where immediate treatment of fevers (i.e. through the health system) can hide high parasite densities from reporting and mass-screen-and-treat diagnostics. For use by interventions, the latter model has nicer behaviour in that the order of deployment of multiple interventions deployed at the same time does not matter, and that the former model retrospectively treats infections which may already have caused fever, thus may have a lower health impact than it should. It is recommended to use the new model (value 1, or greater than 1 if prophylactic effect is desired) unless wanting to emulate the old behaviour. Values of 0 or less than -1 are not allowed. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
Target stage
stage=("liver" or "blood" or "both")Controls whether liver-stage or blood-stage infections are cleared, or both. Infections are considered liver-stage for one 5-day timestep, blood-stage but pre-patent for some number of timesteps (latentp - 1), then start the patent blood stage. If stage is set to "liver", infections are only cleared during their first timestep; if stage is set to "blood", infections are cleared during pre-patent and patent blood stages; if stage is set to "both" all infections are cleared. The old behaviour (oddly considering the drugs it is meant to emulate) is to clear both stages, except for the IPT model of SP action, which cleared only patent blood-stage infections.
SP
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → SP
<SP
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</SP>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
AQ
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → AQ
<AQ
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</AQ>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
SPAQ
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → SPAQ
<SPAQ
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</SPAQ>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
ACT
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → ACT
<ACT
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</ACT>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
QN
→ scenario → healthSystem → ImmediateOutcomes → treatmentActions → QN
<QN
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</QN>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
Probability that a patient with uncomplicated disease seeks official care immediately.
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated1
<pSeekOfficialCareUncomplicated1
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with newly incident uncomplicated disease seeks official care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a patient with uncomplicated disease will self-treat.
→ scenario → healthSystem → ImmediateOutcomes → pSelfTreatUncomplicated
<pSelfTreatUncomplicated
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with uncomplicated disease without recent history of disease (i.e. first line) will self-treat.
Note that in second line cases there is no probability of self-treatment.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a recurring patient seeks official care
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareUncomplicated2
<pSeekOfficialCareUncomplicated2
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with recurrence of uncomplicated disease seeks official care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a patient with severe disease obtains appropriate care
→ scenario → healthSystem → ImmediateOutcomes → pSeekOfficialCareSevere
<pSeekOfficialCareSevere
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with severe disease obtains appropriate care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
liverStageDrug
→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug
<liverStageDrug>
IN ANY ORDER:
| <pHumanCannotReceive ... />
| [ <ignoreCannotReceive ... /> ]
| [ <pUseUncomplicated ... /> ]
| <effectivenessOnUse ... />
</liverStageDrug>Documentation (type)
Parameters for drug treatment which have an effect on the liver-stage of parasites (Primaquine and potentially Tafenoquine); for use with the Vivax model only.
Note: if this section is not listed, the following default values are assumed: pHumanCannotReceive=0, pUseUncomplicated=0, effectivenessOnUse=1.
Probability that human is incompatible with liver-stage drug treatment
→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → pHumanCannotReceive
<pHumanCannotReceive
value=double
/>Documentation (element)
Units: Probability Min: 0 Max: 1
Chance that a human is determined to be unable to receive liver-stage drug treatment. Treatment is neither reported or given for such humans.
This is sampled once per human at birth.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Ignore liver-stage drug treatment incompatibility
→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → ignoreCannotReceive
<ignoreCannotReceive
value=boolean
/>Documentation (element)
If true, ignore pHumanCannotReceive and consider all humans eligible for treatment; if false (or not specified), do not treat those demed incompatible with liver-stage drug treatment.
The point of this is that pHumanCannotReceive cannot be altered by changeHS interventions, but this property can be.
Attributes
Input parameter value
value=booleanA boolean value.
Prob use in UC case
→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → pUseUncomplicated
<pUseUncomplicated
value=double
/>Documentation (element)
Units: Probability Min: 0 Max: 1
This feature is deprecated; it is suggested to use the "simple treatment" feature configured to clear liver-stage parasites, leaving this option unset or zero.
Chance of liver-stage drug treatment being used for routine treatment of an uncomplicated case.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Effectiveness
→ scenario → healthSystem → ImmediateOutcomes → liverStageDrug → effectivenessOnUse
<effectivenessOnUse
value=double
/>Documentation (element)
Units: Probability Min: 0 Max: 1
Chance that liver-stage drug treatment is effective.
On application, a random variable is sampled against this probability. If false, the treatment does nothing; if true, the treatment clears all liver stage parasites. Where effectiveness is longer than a single time step (prophylactic effect), this sample still only happens once (thus either no effect or all liver stages cleared over multiple steps).
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a patient with uncomplicated disease seeks official care immediately.
→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareUncomplicated1
<pSeekOfficialCareUncomplicated1
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with newly incident uncomplicated disease seeks official care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a patient with uncomplicated disease will self-treat.
→ scenario → healthSystem → DecisionTree5Day → pSelfTreatUncomplicated
<pSelfTreatUncomplicated
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with uncomplicated disease without recent history of disease (i.e. first line) will self-treat.
Note that in second line cases there is no probability of self-treatment.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a recurring patient seeks official care
→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareUncomplicated2
<pSeekOfficialCareUncomplicated2
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with recurrence of uncomplicated disease seeks official care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Probability that a patient with severe disease obtains appropriate care
→ scenario → healthSystem → DecisionTree5Day → pSeekOfficialCareSevere
<pSeekOfficialCareSevere
value=double
/>Documentation (element)
Units: Dimensionless Min: 0.0 Max: 1.0
Probability that a patient with severe disease obtains appropriate care
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
liverStageDrug
→ scenario → healthSystem → DecisionTree5Day → liverStageDrug
<liverStageDrug>
IN ANY ORDER:
| <pHumanCannotReceive ... />
| [ <ignoreCannotReceive ... /> ]
| [ <pUseUncomplicated ... /> ]
| <effectivenessOnUse ... />
</liverStageDrug>Documentation (type)
Parameters for drug treatment which have an effect on the liver-stage of parasites (Primaquine and potentially Tafenoquine); for use with the Vivax model only.
Note: if this section is not listed, the following default values are assumed: pHumanCannotReceive=0, pUseUncomplicated=0, effectivenessOnUse=1.
treeUCOfficial
→ scenario → healthSystem → DecisionTree5Day → treeUCOfficial
<treeUCOfficial
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</treeUCOfficial>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
treeUCSelfTreat
→ scenario → healthSystem → DecisionTree5Day → treeUCSelfTreat
<treeUCSelfTreat
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</treeUCSelfTreat>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
Cure rate (severe cases)
→ scenario → healthSystem → DecisionTree5Day → cureRateSevere
<cureRateSevere
value=double
/>Documentation (element)
Min: 0 Max: 1
The probability of clearing parasites given access to appropriate (hospital) care, for a severe case.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
treatmentSevere
→ scenario → healthSystem → DecisionTree5Day → treatmentSevere
<treatmentSevere
[ name=string ]
>
IN THIS ORDER:
| ( <deploy ... /> )*
</treatmentSevere>Documentation (type)
Describes the effects of the treatment, assuming this compliance/adherence/... option is selected. Effects are described in terms of a list of options, each of which acts independently but with all effects being activated simultaneously.
Documentation (base type)
Lists intervention components which are deployed according to some external trigger (for example, screening with a negative patency outcome or health-system treatment).
Components are referenced from one or more sub-lists. Each of these lists is deployed independently if and only if its age constraints are met by the human host and a random sample with the given probability of a positive outcome is positive.
Attributes
Name
name=stringDescribes what this compliance option represents (e.g. "good compliance", "poor compliance with good drugs", ...).
Change transmission levels
→ scenario → interventions → changeEIR
<changeEIR
[ name=string ]
>
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</changeEIR>Documentation (element)
New description of transmission level for models not supporting vector control interventions. Use of this overrides previous transmission levels such that human infectiousness no longer has any feedback effect on transmission. Supplied EIR data must last until end of simulation.
Attributes
Name of intervention
name=stringName of intervention
timedDeployment
→ scenario → interventions → changeEIR → timedDeployment
<timedDeployment
time=string
>
IN THIS ORDER:
| ( <EIRDaily ... /> )+
</timedDeployment>Documentation (type)
Replacement transmission levels. Disables feedback of human infectiousness to mosquitoes on further mosquito to human transmission. Must last until end of simulation.
Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this replacement occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Imported infections
→ scenario → interventions → importedInfections
<importedInfections
[ name=string ]
>
IN THIS ORDER:
| <timed ... />
</importedInfections>Documentation (element)
Models importation of P. falciparum infections directly into humans from an external source. This is infections, not inoculations or EIR being imported.
Attributes
Name of intervention
name=stringName of intervention
Rate of importation
→ scenario → interventions → importedInfections → timed
<timed
[ period=string ] DEFAULT VALUE 0
>
IN THIS ORDER:
| ( <rate ... /> )+
</timed>Documentation (element)
Rate of case importation, as a step function. Each value is valid until replaced by the next value.
Attributes
Period of repetition
period=stringUnits: User defined (default: steps) Min: 0
Default value: 0
If period is 0 (or effectively infinite), the last specified value remains indefinitely in effect, otherwise the times of all values specified must be less than the period, and values are repeated modulo period (the step at time 'period+2t' has same value as the step at '2t', etc.). Can be specified in steps (e.g. 1t) or days (e.g. 365d).
rate
→ scenario → interventions → importedInfections → timed → rate
<rate
time=string
/>Documentation (type)
Units: Imported cases per thousand people per year
A time-rate pair.
Attributes
Time of start
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this importation rate becomes active. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Insert R_0 case
→ scenario → interventions → insertR_0Case
<insertR_0Case
[ name=string ]
>
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</insertR_0Case>Documentation (element)
Used to simulate R_0. First, infections should be eliminated, immunity removed, and the population given an effective transmission- blocking vaccine (not done by this intervention). Then this intervention may be used to: pick one human, infect him, administer a fully effective Preerythrocytic vaccine and remove transmission-blocking vaccine effect on this human. Thus only this one human will be a source of infections in an unprotected population, and will not reinfected himself.
Attributes
Name of intervention
name=stringName of intervention
timedDeployment
→ scenario → interventions → insertR_0Case → timedDeployment
<timedDeployment
time=string
/>Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this intervention occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Uninfect vectors
→ scenario → interventions → uninfectVectors
<uninfectVectors
[ name=string ]
>
IN THIS ORDER:
| ( <timedDeployment ... /> )*
</uninfectVectors>Documentation (element)
Units: List of elements
Removes all infections from mosquitoes -- resulting in zero EIR to humans, until such time that mosquitoes are re-infected and become infectious. Only efficacious in dynamic EIR mode (when changeEIR was not used).
Hypothetical, but potentially useful to simulate a setting starting from no infections, but with enough mosquitoes to reach a set equilibrium of exposure.
Attributes
Name of intervention
name=stringName of intervention
timedDeployment
→ scenario → interventions → uninfectVectors → timedDeployment
<timedDeployment
time=string
/>Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this intervention occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Vector population intervention
→ scenario → interventions → vectorPop
<vectorPop>
IN THIS ORDER:
| ( <intervention ... /> )+
</vectorPop>Documentation (element)
Units: List of elements
A list of parameterisations of generic vector host-inspecific interventions.
intervention
→ scenario → interventions → vectorPop → intervention
<intervention
name=string
>
IN THIS ORDER:
| <description ... />
| [ <timed ... /> ]
</intervention>Documentation (type)
Units: List of elements
An intervention which may have various effects on the vector populations as a whole. (Not host specific.)
Multiple instances of this intervention class are allowed (multiple parameterisations, not just deployments).
Each instance may have multiple deployments. In this case the effects of each instance are independent (effects are combined) but the effects of multiple deployments of a single instance are not independent (only the latest deployment has any effect).
Attributes
Name of intervention
name=stringName of intervention (e.g. larviciding, sugar bait).
description
→ scenario → interventions → vectorPop → intervention → description
<description>
IN THIS ORDER:
| ( <anopheles ... /> )+
</description>anopheles
→ scenario → interventions → vectorPop → intervention → description → anopheles
<anopheles
mosquito=string
>
IN ANY ORDER:
| [ <seekingDeathRateIncrease ... /> ]
| [ <probDeathOvipositing ... /> ]
| [ <emergenceReduction ... /> ]
</anopheles>Documentation (type)
Units: dimensionless Min: 0 Max: 1
Descriptions of the effects of vector interventions with per-species effects.
Attributes
Species/subspecies/variant name
mosquito=stringName of the species/subspecies/variant.
Proportional increase in deaths while host searching
→ scenario → interventions → vectorPop → intervention → description → anopheles → seekingDeathRateIncrease
<seekingDeathRateIncrease
initial=double
>
IN THIS ORDER:
| <decay ... />
</seekingDeathRateIncrease>Documentation (element)
Units: dimensionless
Describe an effect on the increase in the death rate while host seeking (mu_vA) due to this intervention.
Enter the rate increase (i.e. if rate increases to 120% of normal, give 0.2). New death rate while seeking is old × (1 + increase) where increase is this factor given. Must have increas ≥ -1.
Attributes
Initial proportion increase
initial=doubleUnits: dimensionless Min: -1 Max: inf
decay
→ scenario → interventions → vectorPop → intervention → description → anopheles → seekingDeathRateIncrease → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
Determines which decay function to use. Available decay functions, for age t in years: constant: 1 step: 1 for t less than L, otherwise 0 linear: 1 - t/L for t less than L, otherwise 0 exponential: exp( - t/L * log(2) ) weibull: exp( -(t/L)^k * log(2) ) hill: 1 / (1 + (t/L)^k) smooth-compact: exp( k - k / (1 - (t/L)^2) ) for t less than L, otherwise 0
L
L=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Proportion ovipositing mosquitoes killed
→ scenario → interventions → vectorPop → intervention → description → anopheles → probDeathOvipositing
<probDeathOvipositing
initial=double
>
IN THIS ORDER:
| <decay ... />
</probDeathOvipositing>Documentation (element)
Units: dimensionless
Describe an effect of increased mortality while ovipositing due to this intervention. Enter the probability of dying due to this intervention.
Attributes
Initial probability of killing
initial=doubleUnits: dimensionless Min: 0 Max: 1
decay
→ scenario → interventions → vectorPop → intervention → description → anopheles → probDeathOvipositing → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Proportion of emerging pupa killed
→ scenario → interventions → vectorPop → intervention → description → anopheles → emergenceReduction
<emergenceReduction
initial=double
>
IN THIS ORDER:
| <decay ... />
</emergenceReduction>Documentation (element)
Units: dimensionless
Describe an effect on emergence of pupa into adults: this value is the proportion of emerging pupa which are killed by this intervention.
This can be used as a crude way of modelling larviciding. It ca also be used to increase emergence by giving a negative value. The emergence rate is "old rate" × (1 - factor) where factor is the value given here; thus, for example, using -1 will double emergence.
Attributes
Initial proportion reduction
initial=doubleUnits: dimensionless Min: -inf Max: 1
decay
→ scenario → interventions → vectorPop → intervention → description → anopheles → emergenceReduction → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Vector population intervention deployment
→ scenario → interventions → vectorPop → intervention → timed
<timed>
IN THIS ORDER:
| ( <deploy ... /> )+
</timed>Documentation (element)
List of timed vector population intervention deployment
deploy
→ scenario → interventions → vectorPop → intervention → timed → deploy
<deploy
time=string
/>Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this deployment occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Human-specific interventions
→ scenario → interventions → human
<human>
IN THIS ORDER:
| ( <component ... /> )+
| ( <deployment ... /> )*
</human>Documentation (element)
Encapsulates all interventions whose effects are specific to the human host: any interventions where target humans may be selected via population-coverage, age limits and sub-population membership.
Component
→ scenario → interventions → human → component
<component
id=string
[ name=string ]
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <screen ... />
| | <treatSimple ... />
| | <treatPKPD ... />
| | <decisionTree ... />
| | <PEV ... />
| | <BSV ... />
| | <TBV ... />
| | <ITN ... />
| | <IRS ... />
| | <GVI ... />
| | [ <recruitmentOnly ... /> ]
| | <clearImmunity ... />
| [ <subPopRemoval ... /> ]
</component>- screen
- treatSimple
- treatPKPD
- decisionTree
- PEV
- BSV
- TBV
- ITN
- IRS
- GVI
- recruitmentOnly
- clearImmunity
- subPopRemoval
Documentation (element)
A parameterisation of an effect achieved by one component of an intervention. (An intervention is described as the effects of a set of components plus deployments of those components. This describes the components individually, not deployments or which components comprise an intervention.)
Each element describes one component: its effects, decay of the(se) effect(s), and related stuff (e.g. description of indirect decay and of usage levels).
Different interventions can deploy the same component to the same perso. In most cases this will just deploy a fresh instance (e.g. a new bed net will replace the old (nobody uses multiple bed nets), or a new drug dose will act on top of previous doses, or in the case of a vaccine, effect depends on the total number of previous inoculations (including from other interventions).
Where multiple components of the same type (but with different ids) are deployed (whether within a single intervention or by multiple interventions), they act independently (e.g. two bed nets deployed to a single host would act to reduce attractiveness or survival of mosquitoes biting that host twice — this may be useful to simulate some novel vector intervention since the two nets may have separate parameters).
Attributes
Component identifier
id=stringA short name or code identifying the intervention component (used to refer to this component when describing an intervention). Also the id of the sub-population defined as those hosts who have received this intervention and who haven't subsequently been removed from the sub-population.
Name of component
name=stringAn informal name/description for the component
screen
→ scenario → interventions → human → component → screen
<screen
diagnostic=string
>
IN THIS ORDER:
| ( <positive ... /> )*
| ( <negative ... /> )*
</screen>Documentation (type)
This can be combined with MDA to achieve mass screen and treat (MSAT) or other types of mass screening intervention.
When deployed to a host, this simulates a test of patent malaria (microscopy, RDT or some such), then triggers deployment of whichever intervention components are configured (deployments for both positive and negative test outcomes can be configured).
The use of the screening itself is reported (if enabled), but not the outcome. Deployment of interventions triggered by the screening may be reported, however.
Attributes
Name of diagnostic
diagnostic=stringName of a parameterised diagnostic (see scenario/diagnostics).
positive
→ scenario → interventions → human → component → screen → positive
<positive
id=string
/>Documentation (type)
The list of components deployed to eligible humans.
Attributes
Identifier
id=stringThe identifier (short name) of a component.
negative
→ scenario → interventions → human → component → screen → negative
<negative
id=string
/>Documentation (type)
The list of components deployed to eligible humans.
Attributes
Identifier
id=stringThe identifier (short name) of a component.
treatSimple
→ scenario → interventions → human → component → treatSimple
<treatSimple
durationLiver=string
durationBlood=string
/>Documentation (type)
Simple treatment model, targetting liver- and/or blood-stage infections. This is all-or-nothing treatment which, when deploymed, completely clears all infections of the targetted stages. This makes it unsuitable for modeling resistance, but suitable for use with simple infection models.
Infections are considered liver-stage when less than five days old and blood-stage after that. Effects are described independently for the two stages.
Attributes
Length of liver-stage effect
durationLiver=stringUnits: User defined
Controls action on liver-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all liver-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
Length of blood-stage effect
durationBlood=stringUnits: User defined
Controls action on blood-stage infections. 0 means no action, -1 step is a compatibility option to act like treatment before schema version 32 (which removed infections retrospectively), 1 step or any duration which equals some whole number of steps n>0 means to clear all blood-stage infections found on the next 1 or n steps. Note on -1 compatibility option: the main difference to 1 step (clearing on the next timestep) is that parasite densities will be reduced immediately, and thus from the point of view of surveys and mass screen and treat interventions a peak in density which is immediately treated through case management will not be seen. Can be specified in steps (e.g. 1t, -1t) or days (e.g. 5d).
treatPKPD
→ scenario → interventions → human → component → treatPKPD
<treatPKPD
schedule=string
dosage=string
[ delay_h=double ] DEFAULT VALUE 0
/>Documentation (type)
A command to administer drugs according to a given schedule and dosage table, optionally with a delay.
Attributes
Name of treatment schedule
schedule=stringThe name of a schedule to use for treatment.
Name of dosage table
dosage=stringThe name of a dosage table to use for treatment.
Delay (hours)
delay_h=doubleDefault value: 0
Optionally, this can be given to delay the start of treatment by a given number of hours. If not specified, treatment is not delayed. If a delay is given, all medications within the treatment schedule used are delayed by this number of hours.
decisionTree
→ scenario → interventions → human → component → decisionTree
<decisionTree
[ name=string ]
>
EXACTLY ONE OF:
| <multiple ... />
| <caseType ... />
| <diagnostic ... />
| <random ... />
| <age ... />
| <noTreatment ... />
| <treatFailure ... />
| ( <treatPKPD ... /> )+
| <treatSimple ... />
| ( <deploy ... /> )+
</decisionTree>Documentation (type)
Describes how "decisions" are made, both probabilistically and deterministically, and what actions are carried out.
Quantities may also be reported as a side effect of decisions made in the tree, for example the number of diagnostics used.
Attributes
Name
name=stringAn optional piece of documentation attached to this node.
Vaccines
→ scenario → interventions → human → component → PEV
<PEV>
IN THIS ORDER:
| <decay ... />
| <efficacyB ... />
| ( <initialEfficacy ... /> )+
</PEV>Documentation (element)
Pre-erythrocytic vaccine (PEV): prevents a proportion of infections from commencing.
Documentation (type)
Description of a vaccine's effect
Decay of effect
→ scenario → interventions → human → component → PEV → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (element)
Specification of decay of efficacy. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions
Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Variance parameter for vaccine efficacy
→ scenario → interventions → human → component → PEV → efficacyB
<efficacyB
value=double
/>Documentation (element)
Units: Positive real Min: 0.001 Max: 1.00E+06
Measure of variation in vaccine efficacy: efficacy is sampled from a beta distribution with efficacyB its beta parameter and its alpha parameter fixed such that the mean is that given by initialEfficacy.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Initial mean efficacy
→ scenario → interventions → human → component → PEV → initialEfficacy
<initialEfficacy
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Mean efficacy values before decay (see efficacyB and decay parameter descriptions for sampling and decay). The i-th value in this list is used for the efficacy of the vaccine after the i-th dose. Where more doses are given than there are values in this list, the last value is repeated.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Vaccines
→ scenario → interventions → human → component → BSV
<BSV>
IN THIS ORDER:
| <decay ... />
| <efficacyB ... />
| ( <initialEfficacy ... /> )+
</BSV>Documentation (element)
Blood-stage vaccine (BSV): acts as a killing factor on blood-stage parasites. Exact action depends on the within host model.
Documentation (type)
Description of a vaccine's effect
Vaccines
→ scenario → interventions → human → component → TBV
<TBV>
IN THIS ORDER:
| <decay ... />
| <efficacyB ... />
| ( <initialEfficacy ... /> )+
</TBV>Documentation (element)
Transmission-blocking vaccine (TBV): one minus this scales the probability of transmission to mosquitoes
Documentation (type)
Description of a vaccine's effect
Bed nets
→ scenario → interventions → human → component → ITN
<ITN>
IN THIS ORDER:
| [ <usage ... /> ]
| <holeRate ... />
| <ripRate ... />
| <ripFactor ... />
| <initialInsecticide ... />
| <insecticideDecay ... />
| <attritionOfNets ... />
| ( <anophelesParams ... /> )+
</ITN>- usage
- holeRate
- ripRate
- ripFactor
- initialInsecticide
- insecticideDecay
- attritionOfNets
- anophelesParams
Documentation (element)
Description of bed-net interventions (ITNs, LLINs).
Proportion of time nets are used by humans
→ scenario → interventions → human → component → ITN → usage
<usage
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Usage of nets by humans, from 0 to 1.
At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-usagepropActing) + intervention_factorusage*propActing.
See also "propActing" (proportion of bits for which net acts).
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Rate at which holes are made
→ scenario → interventions → human → component → ITN → holeRate
<holeRate
mean=double
sigma=double
/>Documentation (element)
Units: Holes per annum Min: 0
The rate at which new holes are made in nets.
nHoles(t) = nHoles(t-1) + X where X~Pois(R/T) where T is the number of time-steps per year. R is sampled from log-normal: R ~ log N( log(mean)-sigma²/2, sigma² ) and is covariant with ripRate and insecticideDecay. (To be exact, a single Gaussian sample is taken, adjusted for each sigma then exponentiated.)
Documentation (type)
Parameters of a log-normal distribution.
Variates are sampled as: X ~ ln N( log(mean)-sigma²/2, sigma² ).
Equivalent R sample: rlnorm(n, log(m) - s*s/2, s)
Attributes
mean
mean=doubleUnits: (same as base units)
The mean of the lognormal distribution.
sigma
sigma=doubleSigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.
Rate at which holes are enlarged
→ scenario → interventions → human → component → ITN → ripRate
<ripRate
mean=double
sigma=double
/>Documentation (element)
Units: Rips per existing hole per annum Min: 0
Each existing hole has a probability of being ripped bigger according to a Poisson process with this rate as (only) parameter.
New rips occur in a net at rate X~Pois(h×R/T) where h is the number of existing holes and T the number of time-steps per year. R is sampled from log-normal: R ~ log N( log(mean)-sigma²/2, sigma² ) and is covariant with holeRate and insecticideDecay. (To be exact, a single Gaussian sample is taken, adjusted for the each and sigma then exponentiated.)
Documentation (type)
Parameters of a log-normal distribution.
Variates are sampled as: X ~ ln N( log(mean)-sigma²/2, sigma² ).
Equivalent R sample: rlnorm(n, log(m) - s*s/2, s)
Attributes
mean
mean=doubleUnits: (same as base units)
The mean of the lognormal distribution.
sigma
sigma=doubleSigma parameter of the lognormal distribution; sigma squared is the variance of the log of samples.
Rip factor
→ scenario → interventions → human → component → ITN → ripFactor
<ripFactor
value=double
/>Documentation (element)
Units: none Min: 0
This factor expresses how important rips are in increasing the hole.
The hole index of a net is h + F×x where h and x are the total numbers of holes and rips respectively and F is the rip factor.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Initial insecticide
→ scenario → interventions → human → component → ITN → initialInsecticide
<initialInsecticide
mu=double
sigma=double
/>Documentation (element)
Units: mg/m² Min: 0
The insecticide concentration of new nets is Gaussian distributed with mean "mu" and a standard deviation "sigma". The standard deviation should be small relative to the mean to avoid negative initial concentration. Any negative values sampled are set to 0.
Documentation (type)
Parameters of a normal distribution.
Variates are sampled as: X ~ N( mu, sigma² ).
Attributes
mu
mu=doubleUnits: (same as base units)
The mean of the normal distribution.
sigma
sigma=doubleUnits: (same as base units)
The standard deviation of variates.
Decay of insecticide
→ scenario → interventions → human → component → ITN → insecticideDecay
<insecticideDecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (element)
Units: none
Decay curve for insecticide content of nets. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions
The distribution of decay rates over nets is covariant with the distribution of ripRate and holeRate over nets. This distribution is generated by taking one sample per net from a Gaussian distribution with mean 0 and standard deviation 1. For each variable, the sample is multiplied by the respective sigma and a constant added such that, once exponentiated, the mean of the variable over nets is 1. The variable is then exponentiated and multiplied by the required mean rate for the respective variable.
Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Attrition of nets
→ scenario → interventions → human → component → ITN → attritionOfNets
<attritionOfNets
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (element)
Units: dimensionless
Specifies the rate at which nets are disposed of over time. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions
In the current model, nets are disposed of randomly (no correlation with state of decay) such that the chance of each net surviving until age t is the value of this decay function at time t. Equivalently (where a large number of nets are distributed at the same time), the proportion of nets remaining in use should match this decay function over time.
Humans are removed from the intervention component's sub-population on disposal (attrition) of their nets. Currently this event is not reported.
Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
anophelesParams
→ scenario → interventions → human → component → ITN → anophelesParams
<anophelesParams
mosquito=string
[ propActive=double ] DEFAULT VALUE 1
>
IN THIS ORDER:
| EXACTLY ONE OF:
| | <deterrency ... />
| | <twoStageDeterrency ... />
| <preprandialKillingEffect ... />
| <postprandialKillingEffect ... />
</anophelesParams>Attributes
Mosquito species
mosquito=stringName of the affected anopheles-mosquito species.
Proportion of bites for which net acts
propActive=doubleUnits: dimensionless Min: 0 Max: 1
Default value: 1
The proportion of bites, when nets are in use, for which the net has any action whatsoever on the mosquito. At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-usagepropActing) + intervention_factorusage*propActing. See also "usage" (proportion of time nets are used by humans).
Relative attractiveness
→ scenario → interventions → human → component → ITN → anophelesParams → deterrency
<deterrency
holeFactor=double
interactionFactor=double
holeScalingFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of net on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.
Attractiveness of the human is multiplied by exp(log(H)×h + log(P)×p + log(I)×h×p where H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).
Attributes
Hole factor
holeFactor=doubleUnits: none Max: 1
Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.
Interaction factor
interactionFactor=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.
Hole scaling factor
holeScalingFactor=doubleUnits: none Min: 0
Relative attractiveness
→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency
<twoStageDeterrency>
IN ANY ORDER:
| <entering ... />
| <attacking ... />
</twoStageDeterrency>Documentation (element)
Units: dimensionless
Effect of net on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.
This deterrency model multiplies human attractiveness by pEnt×pAtt divided by a base factor to normalise the output to 1 when there are no nets.
RA (entering)
→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → entering
<entering
insecticideFactor=double
insecticideScalingFactor=double
/>Documentation (element)
Units: dimensionless
pEnt represents the relative probability of entering due to ITNs: pEnt = exp(log(P)×p) where P is the insecticide factor and p=1−exp(-insecticideContent×insecticideScalingFactor).
Attributes
Insecticide factor
insecticideFactor=doubleUnits: none Max: 1
Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.
Insecticide scaling factor
insecticideScalingFactor=doubleUnits: none Min: 0
RA (attacking)
→ scenario → interventions → human → component → ITN → anophelesParams → twoStageDeterrency → attacking
<attacking
baseFactor=double
/>Documentation (element)
Units: dimensionless
pAtt represents the relative probability of attacking a human after entering a house due to ITNs (i.e. of feeding/dying vs. flying off): pAtt = B + H×h + P×p + I×h×p where B is the base (without net) probability; H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).
Attributes
Probability of mosquito death without intervention
baseFactor=doubleUnits: dimensionless
Pre-prandial killing effect
→ scenario → interventions → human → component → ITN → anophelesParams → preprandialKillingEffect
<preprandialKillingEffect
baseFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of net on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not sleeping under a net. Parameterisations should take into account that mosquitoes do not always bite indoors.
Killing proportion is calculated as K = B + H×h + P×p + I×h×p where B is the base (without net) probability of death, H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).
Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+H ≤ 1, B+P ≤ 1, B+H+P+I ≤ 1, H ≥ 0, P ≥ 0 and H+P+I ≥ 0.
Attributes
Probability of mosquito death without intervention
baseFactor=doubleUnits: dimensionless
Post-prandial killing effect
→ scenario → interventions → human → component → ITN → anophelesParams → postprandialKillingEffect
<postprandialKillingEffect
baseFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of net on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not sleeping under a net. Parameterisations should take into account that mosquitoes do not always bite indoors.
Killing proportion is calculated as K = B + H×h + P×p + I×h×p where B is the base (without net) probability of death, H, P and I are the hole, insecticide and interaction factors respectively, h=exp(-holeIndex×holeScalingFactor) and p=1−exp(-insecticideContent×insecticideScalingFactor).
Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+H ≤ 1, B+P ≤ 1, B+H+P+I ≤ 1, H ≥ 0, P ≥ 0 and H+P+I ≥ 0.
Attributes
Probability of mosquito death without intervention
baseFactor=doubleUnits: dimensionless
Indoor residual spraying
→ scenario → interventions → human → component → IRS
<IRS>
IN THIS ORDER:
| [ <usage ... /> ]
| <initialInsecticide ... />
| <insecticideDecay ... />
| ( <anophelesParams ... /> )+
</IRS>Documentation (element)
Description of indoor residual spraying interventions.
Documentation (type)
Description of effect for the more complex and probably more realistic Briet model: IRS has three effects, whos strength is calculated as a function of surviving insecticide content.
Proportion of Indoor residual spraying (IRS) interventions
→ scenario → interventions → human → component → IRS → usage
<usage
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Usage of Indoor residual spraying (IRS) interventions, from 0 to 1.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Initial insecticide
→ scenario → interventions → human → component → IRS → initialInsecticide
<initialInsecticide
mu=double
sigma=double
/>Documentation (element)
Units: μg/cm² Min: 0
The insecticide concentration of IRS (at time of spraying) is Gaussian distributed with mean "mu" and a standard deviation "sigma". The standard deviation should be small relative to the mean to avoid negative initial concentration. Any negative values sampled are set to 0.
Documentation (type)
Parameters of a normal distribution.
Variates are sampled as: X ~ N( mu, sigma² ).
Attributes
mu
mu=doubleUnits: (same as base units)
The mean of the normal distribution.
sigma
sigma=doubleUnits: (same as base units)
The standard deviation of variates.
Decay of insecticide
→ scenario → interventions → human → component → IRS → insecticideDecay
<insecticideDecay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (element)
Units: none
Decay curve for insecticide content of IRS. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions
Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Per-mosquito species parameters
→ scenario → interventions → human → component → IRS → anophelesParams
<anophelesParams
mosquito=string
[ propActive=double ] DEFAULT VALUE 1
>
IN THIS ORDER:
| <deterrency ... />
| <preprandialKillingEffect ... />
| <postprandialKillingEffect ... />
</anophelesParams>Attributes
Mosquito species
mosquito=stringName of the affected anopheles-mosquito species.
Proportion of bites for which IRS acts
propActive=doubleUnits: dimensionless Min: 0 Max: 1
Default value: 1
The proportion of bites for which the IRS has any action whatsoever on the mosquito. At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-propActing) + intervention_factor*propActing.
Relative attractiveness
→ scenario → interventions → human → component → IRS → anophelesParams → deterrency
<deterrency
insecticideFactor=double
insecticideScalingFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.
Attractiveness of the human is multiplied by exp(P×log(p)) where P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).
Attributes
Insecticide factor
insecticideFactor=doubleUnits: none Max: 1
Value expected to be at least 0. Negative values are not necessarily invalid, but allow nets to increase transmission.
Insecticide scaling factor
insecticideScalingFactor=doubleUnits: none Min: 0
Pre-prandial killing effect
→ scenario → interventions → human → component → IRS → anophelesParams → preprandialKillingEffect
<preprandialKillingEffect
baseFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors. This parameter has been added since some data shows IRS to have a preprandial killing effect.
Killing proportion is calculated as K = B + P×p where B is the base (without protection) probability of death, and P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).
Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+P ≤ 1 and P ≥ 0.
Attributes
Probability of mosquito death without intervention
baseFactor=doubleUnits: dimensionless
Post-prandial killing effect
→ scenario → interventions → human → component → IRS → anophelesParams → postprandialKillingEffect
<postprandialKillingEffect
baseFactor=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors.
Killing proportion is calculated as K = B + P×p where B is the base (without protection) probability of death, and P is the insecticide factor, p=1−exp(-insecticideContent×insecticideScalingFactor).
Survival of mosquitoes is adjusted via multiplication by (1−K) / (1−B). To keep this in the range [0,1], we require that B+P ≤ 1 and P ≥ 0.
Attributes
Probability of mosquito death without intervention
baseFactor=doubleUnits: dimensionless
Generic vector intervention
→ scenario → interventions → human → component → GVI
<GVI>
IN THIS ORDER:
| [ <usage ... /> ]
| <decay ... />
| ( <anophelesParams ... /> )+
</GVI>Documentation (element)
Low-level description of intervention effects on vectors (i.e. mosquitoes). Can be used to describe simple ITN or IRS interventions (though more complex models are available for these interventions) or other interventions such as mosquito repellant or ivermectin.
Note that all actions of this intervention component will decay according to a single decay function. If independant decay is wanted, a separate component can be used for each action.
Proportion of generic vector interventions
→ scenario → interventions → human → component → GVI → usage
<usage
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Usage of Generic vector interventions, from 0 to 1.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Decay
→ scenario → interventions → human → component → GVI → decay
<decay
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")
[ L=string ]
[ k=double ] DEFAULT VALUE 1.0
[ mu=double ] DEFAULT VALUE 0
[ sigma=double ] DEFAULT VALUE 0
/>Documentation (element)
Description of decay of all intervention effects. Documentation: see DecayFunction type or https://github.com/SwissTPH/openmalaria/wiki/ModelDecayFunctions
Documentation (type)
Specification of decay or survival of a parameter.
Attributes
function
function=("constant" or "step" or "linear" or "exponential" or "weibull" or "hill" or "smooth-compact")Units: None Min: 0 Max: 1
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=stringUnits: User-defined (defaults to years) Min: 0
(Time) scale parameter of distribution: this is either the age of complete decay (smooth-compact, step and linear functions) or the age at which the parameter has decayed to half its original value (exponential, weibull and hill). Not used when function="constant" (i.e. no decay). This value can be specified in years, days or steps (e.g. 2y, 180d or 100t). When the unit is not specified years are assumed. The value is used without rounding except when sampling an age of decay, when the rounding happens as late as possible.
k
k=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.
μ (mu)
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.
σ (sigma)
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).
Per-mosquito species parameters
→ scenario → interventions → human → component → GVI → anophelesParams
<anophelesParams
mosquito=string
[ propActive=double ] DEFAULT VALUE 1
>
IN THIS ORDER:
| [ <deterrency ... /> ]
| [ <preprandialKillingEffect ... /> ]
| [ <postprandialKillingEffect ... /> ]
</anophelesParams>Attributes
Mosquito species
mosquito=stringName of the affected anopheles-mosquito species.
Proportion of bites for which IRS acts
propActive=doubleUnits: dimensionless Min: 0 Max: 1
Default value: 1
The proportion of bites for which the IRS has any action whatsoever on the mosquito. At the moment this is constant across humans and deterministic: relative attractiveness and survival factors are base*(1-propActing) + intervention_factor*propActing.
Relative attractiveness
→ scenario → interventions → human → component → GVI → anophelesParams → deterrency
<deterrency
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on attractiveness of humans to mosquitoes relative to an unprotected adult human. Parameterisations should take into account that mosquitoes do not always bite indoors.
Attractiveness of the human is multiplied this factor times survival of effect.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Pre-prandial killing effect
→ scenario → interventions → human → component → GVI → anophelesParams → preprandialKillingEffect
<preprandialKillingEffect
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on survival mosquitoes as they seek to bite a human after choosing that human, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors. This parameter has been added since some data shows IRS to have a preprandial killing effect.
Killing proportion is this factor multiplied by survival of effect.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Post-prandial killing effect
→ scenario → interventions → human → component → GVI → anophelesParams → postprandialKillingEffect
<postprandialKillingEffect
value=double
/>Documentation (element)
Units: dimensionless Min: 0 Max: 1
Effect of IRS on survival mosquitoes as they seek to escape from a human host and rest after a blood meal, relative to the same person not protected by IRS. Parameterisations should take into account that mosquitoes do not always bite indoors.
Killing proportion is this factor multiplied by survival of effect.
Attributes
Input parameter value
value=doubleA double-precision floating-point value.
Recruitment only
→ scenario → interventions → human → component → recruitmentOnly
<recruitmentOnly/>Documentation (element)
Recruitment of a host into a sub-population.
All human-targeting intervention deployments recruit simulated humans into a sub-population which can be used for the purposes of cumulative deployment, deployment only to a sub-population and defining a cohort. This pseudo-intervention can be used to define a sub-population without also deploying some intervention.
Clear Immunity
→ scenario → interventions → human → component → clearImmunity
<clearImmunity/>Documentation (element)
Removes all exposure-related immunitsy gained over time by hosts without removing infections (or affecting the ability to gain immunity through exposure).
Hypothetical, but potentially useful to simulate scenarios with unprotected humans.
subPopRemoval
→ scenario → interventions → human → component → subPopRemoval
<subPopRemoval
[ onFirstBout=boolean ] DEFAULT VALUE false
[ onFirstTreatment=boolean ] DEFAULT VALUE false
[ onFirstInfection=boolean ] DEFAULT VALUE false
[ afterYears=double ]
/>Documentation (type)
Each human intervention component corresponds to a sub-population: those who have received or are considered to be protected by the intervention component. Humans automatically become members of this sub-population when receiving an intervention component; this element controls how humans are removed from the sub-population.
ITN attrition also removes humans from sub-populations.
Note that sub-populations do not directly correspond to an intervention's effects: lack of effectiveness does not imply removal from the sub-population (except as explicitly configured here) and removal from the sub-population does not halt an intervention's effects.
Sub-populations may be used to define a cohort, to restrict deployment of other interventions and to use cumulative deployment mode. A sub- population may or may not correspond (roughly) to humans protected by some intervention.
Attributes
Time to first episode only
onFirstBout=booleanDefault value: false
If true, remove individuals from the sub-population at the start of the first episode (start of a clinical bout) since they were recruited into the sub-population. This is intended for cohort studies which measure time to the first episode, using active case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.
Time to first treatment only
onFirstTreatment=booleanDefault value: false
If true, remove individuals from the sub-population when they first seektreatment since they were recruited into the sub-population. This is intended for cohort studies which measure the time to first episode, using passive case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.
Time to first infection only
onFirstInfection=booleanDefault value: false
If true, remove individuals from the sub-population at completion of the first survey in which they present with a patent infection since they were recruited into the sub-population. This intended for cohort studies which measure time to the first infection, using active case detection. Reports delayed due to health-system memory are forced out when this occurs. Note that this can increase the number of uncomplicated cases reported across the entire population; for this reason reports are not forced on recruitment or most removal options. This does not prevent re-recruitment in the case that recruitment settings could conceivably recruit the same individual twice.
Remove from sub-population after
afterYears=doubleUnits: Years Min: 0
If given, membership to the sub-population of humans who have received this intervention component expires after the given number of years. Note that future deployments renew membership (e.g. if this parameter is 4 years and the intervention is redeployed 3 years from now, expiry happens after 7 years). This provides a crude way of modelling a cohort protected by some intervention. A few interventions provide more detailed ways of modelling expiry of protection. In any case, "expiry of protection" is an abstract concept and does not imply that all protection has ceased, even in the simulator. This may also be useful for cumulative deployment. Minimum duration is zero, which implies the human is effectively never a member of the sub-population; a duration of one timestep implies the human is a member of the sub-population while any futher interventions are deployed on the same time as this human becomes a member and on the next update of the human (including transmission and health system events) but not beyond that. If this attribute is not given, the simulated human is a member until death or some other option triggers removal. Input is rounded to the nearest time step.
Deployment
→ scenario → interventions → human → deployment
<deployment
[ name=string ]
>
IN THIS ORDER:
| ( <component ... /> )+
| ( <continuous ... /> )*
| ( <timed ... /> )*
</deployment>Documentation (element)
This element describes deployment of an intervention: which components are deployed, how humans are selected for deployment (via timed or age-based deployment) as well as a few additional restrictions (e.g. vaccine dosing restrictions).
All components deployed by this intervention are deployed to the same people (each timed or continuous deployment selects recipients and then gives each recipient all components of the intervention).
Attributes
Intervention name
name=stringName of intervention
component
→ scenario → interventions → human → deployment → component
<component
id=string
/>Documentation (type)
The list of components deployed to eligible humans.
Attributes
Identifier
id=stringThe identifier (short name) of a component.
Age-based (continuous) deployment
→ scenario → interventions → human → deployment → continuous
<continuous>
IN THIS ORDER:
| [ <restrictToSubPop ... /> ]
| ( <deploy ... /> )+
</continuous>Documentation (element)
List of ages at which deployment takes place (through EPI, post-natal and school-based programmes, etc.).
A sub-population restriction may be added as a property of the list of continuous deployments.
restrictToSubPop
→ scenario → interventions → human → deployment → continuous → restrictToSubPop
<restrictToSubPop
id=string
[ complement=boolean ] DEFAULT VALUE false
/>Documentation (type)
If this element is specified, deployment is restricted to some sub-population (specified via the "id" attribute); otherwise the target population is the entire simulated population. Either way, other deployment restrictions (age, time, number of vaccine doeses) still apply.
Attributes
Sub-population identifier
id=stringThe identifier (short name) of the sub-population (i.e. the "id" of some intervention component). Also see the "complement" attribute.
Complement
complement=booleanDefault value: false
If this is not specified or is false, deployment is restricted to the sub-population of people protected by the intervention component who's id is given. If complement is set to true, deployment is instead restricted to the complement of that sub-population, i.e. to those
deploy
→ scenario → interventions → human → deployment → continuous → deploy
<deploy
targetAgeYrs=double
[ begin=string ]
[ end=string ]
/>Attributes
Target age
targetAgeYrs=doubleUnits: Years Min: 0 Max: 100
Target age of intervention. Input is rounded to the nearest time step.
First time active
begin=stringUnits: User defined (defauls to steps)
First time at which this deployment is active. If not specified, deployment starts at the beginning of the intervention period. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
End step
end=stringUnits: User defined (defauls to steps)
End of the period during which the intervention is active (to be exact, the first step of the intervention period at which the item becomes inactive). If not specified, deployment never ceases after starting during the simulation. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Mass (timed) deployment
→ scenario → interventions → human → deployment → timed
<timed>
IN THIS ORDER:
| [ <restrictToSubPop ... /> ]
| [ <cumulativeCoverage ... /> ]
| ( <deploy ... /> )+
</timed>Documentation (element)
List of timed deployments of the intervention (that is, of deployment campaigns).
Cumulative deployment mode can be specified for all deployments in a timed list. To allow multiple cumulative deployment descriptions, the entire timed list may be repeated.
restrictToSubPop
→ scenario → interventions → human → deployment → timed → restrictToSubPop
<restrictToSubPop
id=string
[ complement=boolean ] DEFAULT VALUE false
/>Documentation (type)
If this element is specified, deployment is restricted to some sub-population (specified via the "id" attribute); otherwise the target population is the entire simulated population. Either way, other deployment restrictions (age, time, number of vaccine doeses) still apply.
Attributes
Sub-population identifier
id=stringThe identifier (short name) of the sub-population (i.e. the "id" of some intervention component). Also see the "complement" attribute.
Complement
complement=booleanDefault value: false
If this is not specified or is false, deployment is restricted to the sub-population of people protected by the intervention component who's id is given. If complement is set to true, deployment is instead restricted to the complement of that sub-population, i.e. to those
Cumulative coverage
→ scenario → interventions → human → deployment → timed → cumulativeCoverage
<cumulativeCoverage
component=string
/>Documentation (element)
If this element is not specified, standard deployment occurs, where a portion of the population as given by the coverage property of this campaign is selected, and interventions are deployed to all of these people (regardless of previous coverage).
If this attribute is specified, instead, the population is divided into two sets: those who are a member of a certain sub-population and those who are not (see "subPopRemoval" element). If the proportion of people in the first set is less than the desired coverage, then the proportion of people from the second set needed to increase total coverage to the desired coverage is calculated. This proportion is then used as the probablity of selection from the second set into a third set of people who then receive all interventions deployed by this campaign.
Note that selection is stochastic so the final coverage level may not be exactly that desired. Note also that the component used when selecting people need not actually be one of the components deployed by this intervention, although that is the intended use case.
Attributes
Component identifier
component=stringThe identifier (short name) of the component used when selecting people.
deploy
→ scenario → interventions → human → deployment → timed → deploy
<deploy
time=string
[ maxAge=double ]
[ minAge=double ] DEFAULT VALUE 0
/>Attributes
Time
time=stringUnits: User defined (defauls to steps) Min: 0
Time at which this deployment occurs. See doc on intervention period and on monitoring/startDate for details of how times work. Can be specified in steps, days, years, or as a date (examples: 15t, 75d, 0.2y, 2000-03-16).
Maximum age of eligible individuals
maxAge=doubleUnits: Years Min: 0
Maximum age of eligible individuals (defaults to no limit). Input is rounded to the nearest time step.
Minimum age of eligible individuals
minAge=doubleUnits: Years Min: 0
Default value: 0
Minimum age of eligible individuals (defaults to 0). Input is rounded to the nearest time step.
| Download openmalaria | Installation instructions | XML Schema Documentation | |-----------------------|------------------------|-----------------------|-----------------------|-----------------------|
Status
| XML Schema Version | Program version | master |
develop |
|---|---|---|---|
| 36 | schema-36.1 |
 |
 |