Malaria Analytics

Simulation-based Analytics for Malaria Programs


Malaria analytics in SimBA was designed for decision support for malaria programs: we need to support evaluation, scenario planning, and sub-national tailoring. To accomplish these major tasks, we need a suite of supporting algorithms. Here, we present an overview.


SimBA was developed to support robust analytics for malaria policy (RAMP). What do we mean by robust analytics?

Malaria analytics are defined herein as the systematic analysis of data for malaria decision support for malaria programs. In analyzing data to give advice, we will often need data that we don’t have. Since we will rarely have all the data that would be required to fully develop a quantitative understanding of malaria transmission in a set of management units, we will be giving advice in the face of great uncertainty. To give robust advice, we go to great lengths to characterize and quantify uncertainty, and then to fully propagate the uncertainty through analytis, up to and including development of advice. The advice we develop is designed to robust in the sense that it is highly unlikely to change if we had done the uncertainty in another reasonable way. Ideally, robust advice comes with a recommended course of action, an assessment of the alternatives, a recommendation about how to adapt malaria surveillance to reduce uncertainty in the future.

In designing the software and the core algorithms for malaria analytics, our goal has been to start with simple models, develop algorithms to fit models to data, to evaluate malaria in context as a changing baseline that has been modified by control. The retrospective analysis is translated into advice about the future through a forecast, to set expectations about what is likely to happen. Using the evaluation and forecast, we do scenario planning.

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 [13], 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:

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:

References

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Hay SI, Smith DL, Snow RW. Measuring malaria endemicity from intense to interrupted transmission. Lancet Infect Dis. 2008;8: 369–378. doi:10.1016/S1473-3099(08)70069-0
2.
Smith DL, Smith TA, Hay SI. Measuring malaria for elimination. In: Feachem RGA, Phillips AA, Targett GAT, editors. Shrinking the Malaria Map. San Francisco, CA: University of California, San Francisco; 2009. pp. 108–126. Available: http://www.worldcat.org/title/shrinking-the-malaria-map-a-prospectus-on-malaria-elimination/oclc/421361248
3.
Tusting LS, Bousema T, Smith DL, Drakeley C. Measuring changes in Plasmodium falciparum transmission: Precision, accuracy and costs of metrics. Adv Parasitol. 2014;84: 151–208. doi:10.1016/B978-0-12-800099-1.00003-X