Interventions
Interventions.RmdInterventions contains the historical intervention information for a site. It is also the section of the site file that you would modify with intervention information for future scenarios. Details, references and methods for individual interventions are shown below:
Note on time: The model assumes all years have 365 days (no leap
years). Intervention time points are therefore specified using a
year column plus a “day_of_year” column, where day of year
runs from 1 to 365 inclusive.
ITNs
site_file$interventions$itn$implementation
Bed net implementation details
Variables
net_type Type of netdistribution_type Type of net distribution, mass-campaign
or routinedistribution_day_of_year Day of the year for the
distributiondistribution_lower Lower bound for possible
itn_input_distdistribution_upper Upper bound for possible
itn_input_dist
Description
Supported net types currently include: “pyrethroid_only”,
“pyrethroid_pbo”, “pyrethroid_pyrrole”. Net type introductions are
informed by data from the Alliance for Malaria Prevention. Current
available net types are: pyrethroid_only, pyrethroid_pbo and
pyrethroid_pyrrole. Absent systematic, country data on mass campaign
dates and locations we assume that mass campaigns occur on the first day
of the year and routine distributions occur quarterly. Lower and upper
bounds help to ensure that sensible model input distributions are
estimated, see stop_missing_itn_input_dist() for full
details. Due to differences in the availability of data sources the
approach for countries within sub-Saharan Africa differs to countries
outside of sub-Saharan Africa:
Within sub-Saharan Africa: The population at risk weighted mean ITN use estimates for each site are taken from the malaria atlas project.
Outside of sub-Saharan Africa: ITN use is much more heterogeneous outside of SSA and data are less systematically collected. As a result, there are strong assumptions associated with the historical scale and magnitude of ITN distributions. We make the assumption that any reported ITN distributions (as detailed by the world malaria report) are targeted such that areas with the highest baseline prevalence are prioritised first. Net use is implemented up to a maximum usage of 55% in each area, sequentially working through the targeting until the reported total number of bed nets have been allocated.
Available data on ITN use will not extend to the present year. Missing ITN use estimates to present are filled assuming a constant, continuing level of coverage. To respect the multi-year cyclical nature of ITN distribution cycles any missing estimates are filled in assuming that coverage is constant with respect to 3 years prior. For example if years 2024, 2025 and 2026 are missing then 2024 == 2021, 2025 == 2022 and 2026 == 2023.
⚠️ Warning: For ITNs to work when passed to
site::site_parameters() you will need to specify a column:
site_file$interventions$itn$implementation$itn_input_dist
indicating the size of the model ITN distributions each year. This is
not the same as ITN usage. Use
site::site_usage_to_model_distribution() to convert usage
to model input distributions, or call
stop_missing_itn_input_dist() for full details.
Given an ITN type and level of pyrethroid insecticide resistance, the site package will link to corresponding estimates of the key ITN efficacy parameters.
Description
Population at risk weighted average ITN use for each region. Usage measurements are assumed to occur on the first day of the year (aligns with the MAP net online model).
IRS
site_file$interventions$irs$implementation
IRS implementation details
Variables
irs_cov IRS coveragepeak_season Rainfall seasonal peak day of the yearinsecticide IRS insecticideround Spray roundspray_day_of_year Day of year of spray round
Description
As with ITNs, due to differences in the availability of data sources the approach for countries within sub-Saharan Africa differs to countries outside of sub-Saharan Africa:
Within sub-Saharan Africa: The population at risk weighted mean IRS coverage estimates for each spatial unit are summarised from the malaria atlas project
Outside of sub-Saharan Africa: IRS coverage is much
more heterogeneous outside of SSA and data are less systematically
collected. As a result, there are strong assumptions associated with the
historical scale and magnitude of IRS campaigns. We make the assumption
that any reported persons protected by IRS (as detailed by the world
malaria report) are targeted such that areas with the highest baseline
prevalence are prioritised first. IRS coverage
implemented up to a maximum usage of 80% in each area, sequentially
working through the targeting until the reported total number of persons
protected have been allocated.
It is assumed that a DDT-type insecticide is used prior to 2017, after which there is a switch to an actellic-like insecticide. Current available IRS insecticide options are: “ddt”, “actellic”, “bendiocarb” and “sumishield”. We assume a single IRS spray round per year.
Given an IRS type and level of pyrethroid insecticide resistance, the site package will link to corresponding estimates of the key ITN efficacy parameters.
Available data on IRS coverage (via MAP or the world malaria report) will not extend to the present year. Missing IRS coverage estimates to present are filled assuming a constant, continuing level of coverage.
Treatment
site_file$interventions$treatment$implementation
Treatment implementation details
Variables
year Year at which coverage changesday_of_year Day of the year at which coverage changestx_cov Effective treatment coverageprop_act Proportion of first line treatments that are an
ACT
Description
The population at risk weighted mean effective treatment coverage estimates for each spatial unit are summarised from the malaria atlas project. Note, as this metric describes “effective” treatment, we implement specifically parameterised drugs in the site file with efficacy = 1. For the proportion of treatments that are an ACT, for SSA estimates by year are expanded by linear interpolation between data points and an assumption of constant coverage after the most recent data point. We assume that ACT coverage is zero before 2006, when the WHO recommendation was first issued. For outside of SSA the DHS indicator is confounded by treatment for Plasmodium vivax, and we therefore assume the mean values by year from data within SSA.
Description
This is useful for costing. We assume a constant proportion over time by country, estimated as the mean from all country survey estimates since 2010. For countries without survey data, we assume the median across all estimates.
Seasonal malaria chemoprevention (SMC)
site_file$interventions$smc$implementation
SMC implementation details
Variables
smc_cov SMC coveragepeak_season Rainfall seasonal peak day of the yearsmc_min_age Lower bound of SMC-eligibility age range
(days)smc_max_age Upper bound of SMC-eligibility age range
(days)round Delivery roundround_day_of_year Day of year each round is delivered
Description
Historical SMC implementation and coverage estimates are fragmented. We identify historical SMC implementation areas from maps presented by both Access SMC and more recently SMC alliance. We assume a linear increase in coverage post implementation initiation up to a maximum of 80% to capture an increasing number of smaller sub-national units being targeted over time. We assume 4 rounds, centred on the seasonal peak in rainfall.
Vaccine
site_file$interventions$vaccine$implementation
Vaccine implementation details
Variables
year Year at which coverage changesday_of_year Day of the year at which coverage changesr21_primary_cov Coverage of R21 primary seriesrtss_primary_cov Coverage of RTS,S primary seriespeak_season Rainfall seasonal peak day of the yearr21_booster1_cov Coverage of the R21 first boosterrtss_booster1_cov Coverage of the RTS,S first booster
Description
We include historical RTS,S coverage that has occurred as part of the MVIP implementation trial, sub-nationally in Malawi, Ghana and Kenya. The spatial distribution is informed from an MVIP briefing presentation.
EPI-based vaccine scale up of R21 and RTS,S has been manually collated from the UNICEF immunization dashboard. Absent sub-national data, the total doses delivered is converted into a country-wide coverage.
Description
Vaccines are assumed to have a primary schedule of 3-doses. Efficacy is assumed to start on receipt of the 3rd dose.
Description
For age-based delivery booster timing is calculated relative to when an individual completes their primary vaccine series (after the third dose). If delivery is hybrid, the first booster spacing timestep is relative to the start of the year, otherwise they are relative to the last primary dose.
Perennial malaria chemoprevention (PMC)
site_file$interventions$pmc$implementation
PMC implementation information
Variables
year Year at which coverage changesday_of_year Day of the year at which coverage changespmc_cov PMC coverage
Description
This intervention has been known in the past as intermittent preventative treatment of infants (IPTi). Due to the very limited (non-trial setting) implementation of PMC historically, we mostly assume 0 coverage.
site_file$interventions$pmc$drug
PMC drug