Malaria is a major global health challenge. It is caused by Plasmodium parasites transmitted from human-to-human by female Anopheles mosquitoes. The parasite species Plasmodium vivax is one of the two most important causes of malaria and also the most difficult to eliminate because of its ability to relapse: cause recurrent blood-stage infection (recurrence) following the activation of dormant liver-stage parasites. Blood-stage treatment failure (recrudescence) and new infectious mosquito bites (reinfection) also cause recurrence. Distinguishing among these three causes of recurrence is important in both epidemiological and clinical studies, especially those that aim to estimate the clinical efficacy of antimalarial treatment. However, there are no definitive biomarkers of relapse, recrudescence and reinfection (the 3Rs).
Genetic data on P. vivax parasites sampled from two or more blood-stage infections in the same person can be used to infer the cause of recurrence. Previously, we developed a statistical model capable of computing posterior 3R probabilities using data on fewer than 10 microsatellite markers. This was a breakthrough, showing that statistical genetic inference of P. vivax recurrence is feasible, but the prototypic model has two major drawbacks. Firstly, it is encoded in study specific scripts that are not easily accessible. Secondly, it does not scale to hundreds of markers typical of amplicon sequencing (AmpSeq) data, which many malaria studies are now generating.
PvRecur aims to transform P. vivax recurrence state inference by providing a open-source tool that all P. vivax researchers (epidemiologists and clinical trial analysts) can use to compute 3R probabilities from P. vivax AmpSeq data (objective 1); and by demonstrating the utility of that tool through its application to AmpSeq datasets on samples collected in the Solomon Islands, Peru, and Ethiopia (objective 2). By clarifying the causes of recurrence, PvRecur will improve our epidemiological understanding of P. vivax and enable more accurate treatment efficacy estimation, guiding the optimization of new treatment regimens. Given that nearly 2.5 billion people live at risk of P. vivax infection, efforts to improve P. vivax control and elimination have a substantial potential impact on global health.
