malariasimple: An R package for fast simulations of malaria transmission

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.Author summary: Malaria remains a major global health challenge, and choosing how best to use limited resources for control is difficult. Mathematical models can help by testing possible intervention strategies before they are used in the real world. However, detailed malaria models that track individuals are computationally slow, making it impractical to run the thousands of simulations needed to rigorously quantify uncertainty in model predictions. This is a significant limitation, because data on malaria transmission are often sparse or imprecise, and understanding the range of plausible outcomes is essential for sound decision-making. We developed malariasimple, a new R package that provides a faster, simplified version of malariasimulation, an established individual-based malaria transmission model. Our model retains the essential biology of how malaria spreads between humans and mosquitoes, as well as the effects of widely used control interventions such as insecticide-treated bed nets and preventive drug treatments. We show that malariasimple produces outputs that are very similar to the more detailed model while reducing runtimes by up to 99.6%. This speed makes it practical to fit the model to data using Bayesian methods and to quantify uncertainty in predictions. malariasimple therefore offers a useful tool for researchers and policy analysts who need fast, interpretable malaria simulations.