Malaria Analytics
Simulation-based Analytics for Malaria Programs
In malaria analytics, defined herein as the systematic analysis of data for the purpose of giving advice, we use models as inferential tools with the goal of giving policy advice. Most of the algorithms developed herein are for malaria programs.
After 145 years of malaria epidemiology (counting from Laveran to the present day, 2025), it’s possible to set reasonable expectations about the sorts of data that we will be able to use for malaria analytics. We will use the metrics that are collected as part of malaria research and routine surveillance. While there are many metrics available [1–3], malaria programs need timely data from every management unit to give a quantitative assessment of malaria in a form that can be used to support malaria policies – we call this malaria intelligence. We have thus based our algorithms around the following expectations:
Most malaria program decisions will need to use facility data to manage malaria from routine health management information systems (HMIS); specifically, data from health facilities will be the primary source for malaria policies;
Since facility data are collected passively and with large concerns about data quality, there is a need to validate the data through some algorithm that can relates HMIS data to research data. The metric most commonly used in malaria research is the PfPR, so we have developed algorithms that predict PfPR from facility data.
The output of HMIS data processing and analysis pipelines will thus be a PfPR time series.
The PfPR in any population in an area varies by age, sex, and time of year. To predict PfPR by location, age, sex, and time of year, we have developed simulation-based algorithms that estimate average population expsure as a PfEIR time series.
The algorithms that we present in the following sections are thus designed to estimate malaria exposure and transmission from PfPR time series. With these data, we will want to do the following:
First, we discuss model fitting for malaria analytics.
Next, we fit models to get a history. We do it two ways: first, we fit the average, seasonal patterns, and inter-annual variability in the PfEIR to get PfPR; next, we fit the average, seasonal patterns, and inter-annual variability in the mosquito emergence rate, \(\Lambda,\) to fit the PfPR.
The models that we have fitted to the data are purely descriptive, but we would like to evaluate malaria control and do scenario planning. We thus assemble a history of vector control, and do a set of linked activities:
From a reconstructed history of exposure, we Evaluate Vector Control from the PfPR time series and the timing of vector control;
We describe how to Estimate Effect Sizes
Next, we reconstruct a history of malaria as a Changing Baseline that has been modified by vector control.
We need to Forecast the baseline as a basis for Scenario Planning;