Inferring the cause of recurrent Plasmodium vivax malaria with statistical genetics

One of the difficulties in eliminating Plasmodium vivax malaria lies in its ability to cause recurrent infections following the activation of dormant parasites (relapse). However, this can be confused with recurrent infections due to treatment failure (recrudescence), or a new infectious mosquito bite (reinfection). Distinguishing the cause of recurrent Plasmodium vivax malaria in each patient is critical for malaria control efforts, such as efficacy studies of drug treatments. We address this need by developing a statistical tool to infer the cause of Plasmodium vivax recurrent malaria from genetic data, implemented through the R package Pv3Rs. Each mode of malaria recurrence – relapse, recrudescence, and relapse – feature different levels of genetic relatedness between parasites. We use Bayesian hierarchical modelling to translate genetic relatedness in observed data to interpretable probabilities for each mode of malaria recurrence. We illustrate the utility of our model by applying it to Plasmodium vivax microsatellite marker data of acute malaria patients treated with high-dose primaquine. The ability to probabilistically resolve the cause of recurrent malaria helps provide more accurate failure rates of drug treatments.

Inferring the cause of recurrent Plasmodium vivax malaria with statistical genetics

Monday, July 14 at 6:00pm

Yong See Foo

"Inferring the cause of recurrent Plasmodium vivax malaria with statistical genetics"

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