Revisiting Plasmodium vivax molecular correction
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Background
Reliable inference of Plasmodium vivax recurrence states — relapse, recrudescence and reinfection (the “3Rs”) — improves estimates of antimalarial efficacy. The R package Pv3Rs features a Bayesian model designed for P. vivax molecular correction, i.e., using parasite genetic data to infer recurrence states. The model is an extension of a prototype built to analyse microsatellite data from the Vivax History (VHX) and Best Primaquine Dose (BPD) trials.
Methods
We re-analysed data from 212 VHX and BPD trial participants (493 recurrences) using Pv3Rs, comparing results with those from the prototype and with genetic relatedness estimated using Dcifer, a tool for estimating relatedness based on identity-by-descent. Posterior recurrence state probabilities were computed using both uniform and time-to-event priors: artificial but equal prior probabilities facilitate posterior interpretation, while time-to-event priors leverage all available information and enable re-computation of failure rates. Relatedness estimates were used to identify and correct instances of model misspecification.
Results
The Pv3Rs model generated posterior probabilities for all recurrences and was able to jointly model data on all episodes per participant for 89% of participants, compared with 73% using the prototype. Recurrence state probabilities were broadly consistent across methods, though the Pv3Rs model elevated reinfection probabilities slightly. Relatedness estimates exposed various outliers consistent with half-sibling parasites and/or genotyping errors. Outlier correction impacted some per-participant failure probabilities, but reinfection-adjusted radical-cure failure rates of high-dose primaquine remained near 3%, in line with previous findings.
Conclusion
Re-analysis of VHX and BPD P. vivax genetic data restates earlier reinfection-adjusted efficacy estimates. It demonstrates the increased computational capability and misspecification sensitivity of Pv3Rs, highlighting a need for careful analyses. Using relatedness-based diagnostics alongside model-based inference, we were able to harness the advantages of model-based inference and provide a framework for future P. vivax molecular correction.
