The math of malaria: Drug resistance 'a numbers game' of competing parasites

By Carol Clark | eScienceCommons | Sept. 4, 2018

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"Computer models can sometimes give you insights that would be too difficult to get in a real-world setting," says Mary Bushman. She developed a malaria model for her PhD thesis, advised by Emory evolutionary biologist Jaap de Roode. Emory Photo/Video

A new mathematical model for malaria shows how competition between parasite strains within a human host reduces the odds of drug resistance developing in a high-transmission setting. But if a drug-resistant strain does become established, that same competition drives the spread of resistance faster, under strong selection from antimalarial drug use.

“It’s basically a numbers game,” says Mary Bushman, who developed the model for her PhD thesis in Emory University’s Population Biology, Ecology and Evolution Graduate Program. “When you already have multiple strains of malaria within a population, and a drug-resistant strain comes along, it will usually go extinct simply because it’s a late-comer. Whichever strain is there first has the advantage.”

PLOS Biology published the findings, a computational framework that modeled a malaria epidemic across multiple scales: Transmission of parasites from mosquitos to humans, and the dynamics of parasites competing to infect blood cells while they also battle the immune system of a human host.

After creating the model, Bushman ran simulations tracking malaria in a population for roughly 14 years. The simulations included 400 theoretical people who were randomly bitten by 12,000 mosquitos that were infected with malaria parasites classified as either drug resistant or drug susceptible. Various levels of treatment with antimalarial drugs were also part of the simulations.

“Our model holds strong relevance for infectious diseases beyond malaria,” says Jaap de Roode, an evolutionary biologist at Emory and senior author of the paper. “We hope this research gives others a method to look at disease dynamics across scales of biological organisms to learn how drug resistance develops in a range of pathogens.”

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