期刊
EVOLUTIONARY APPLICATIONS
卷 13, 期 10, 页码 2723-2739出版社
WILEY
DOI: 10.1111/eva.13077
关键词
computer simulation; diagnosis; drug resistance; genetics; population; malaria; mass drug administration; mutation; vaccines
资金
- Malaria Modeling Consortium [UWSC9759]
Introduction 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. 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 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. 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. Discussion The OpenMalaria IBM of malaria was an exemplar and the same principles apply to IBMs of other diseases.
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