Authors
Debbie Shackleton, Neil Ferguson, Lucy Okell, Tom Churcher, Pete Winskill
Published in
PLoS computational biology. Volume 22. Issue 9. Pages e1013687. Sep 01, 2026. Epub Sep 01, 2026.
Abstract
Process-based malaria transmission models are important tools for evaluating intervention strategies, quantifying uncertainty, and informing malaria control policy. Individual-based models such as malariasimulation are computationally demanding, which limits their practicality for applications that require large numbers of simulation runs. In this paper we present malariasimple, a simplified, compartmental model implemented as an R package which approximates the epidemiological structure and parameter definitions of malariasimulation while operating at a fraction of the computational cost. Across a range of transmission intensities and intervention scenarios, malariasimple closely reproduces key outputs of malariasimulation while reducing runtimes by up to 99.6%. Its computational efficiency enables full Bayesian parameter inference, allowing estimation of complete posterior distributions. malariasimple provides a fast, flexible, and mechanistically consistent addition to the Imperial College London Malaria Model framework, bridging the gap between computational efficiency and epidemiological realism. The malariasimple R package is freely available for download at https://github.com/mrc-ide/malariasimple.
PMID:
42678996
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.
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