Abstract
IntroductionControl strategies for human infections are often investigated using individual‐based models (IBMs) to quantify their impact in terms of mortality, morbidity and impact on transmission. Genetic selection can be incorporated into the IBMs to track the spread of mutations whose origin and spread are driven by the intervention and which subsequently undermine the control strategy; typical examples are mutations which encode drug resistance or diagnosis‐ or vaccine‐escape phenotypes.Methods and resultsWe simulated the spread of malaria drug resistance using the IBM OpenMalaria to investigate how the finite sizes of IBMs require strategies to optimally incorporate genetic selection. We make four recommendations. Firstly, calculate and report the selection coefficients, s, of the advantageous allele as the key genetic parameter. Secondly, use these values of “s” to calculate the wait time until a mutation successfully establishes itself in the pathogen population. Thirdly, identify the inherent limits of the IBM to robustly estimate small selection coefficients. Fourthly, optimize computational efficacy: when “s” is small, fewer replicates of larger IBMs may be more efficient than a larger number of replicates of smaller size.DiscussionThe OpenMalaria IBM of malaria was an exemplar and the same principles apply to IBMs of other diseases.
Highlights
Control strategies for human infections are often investigated using individual-based models (IBMs) to quantify their impact in terms of mortality, morbidity and impact on transmission
The number of humans to be tracked in OpenMalaria is user-specified, and we can vary the user-defined entomological inoculation rate (EIR; the mean number of infective bites per adult human per year) to vary the prevalence of malaria infection; by default, we simulate a prevalence of 15% averaged over all ages based on diagnosis by microscopy
This is not usually an issue for simulating the overall epidemiology, and IBMs of malaria have been successfully used to investigate a number of control measures as described in the Introduction
Summary
Control strategies for human infections are often investigated using individual-based models (IBMs) to quantify their impact in terms of mortality, morbidity and impact on transmission. Methods and results: We simulated the spread of malaria drug resistance using the IBM OpenMalaria to investigate how the finite sizes of IBMs require strategies to optimally incorporate genetic selection. Calculate and report the selection coefficients, s, of the advantageous allele as the key genetic parameter Use these values of “s” to calculate the wait time until a mutation successfully establishes itself in the pathogen population. Advances in computational power over the last 20 years have allowed sophisticated, individual-based models (IBMs) of infectious diseases to be developed and applied to important human and animal disease. These are valuable in diseases with complex transmission through vector species or which have complex clinical aetiology. Drug resistance almost inevitably evolves in response to drug deployment, and mutations arise that change the antigenic
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