Work

Probabilistic classification of late treatment failure in uncomplicated falciparum malaria

Year: 2025

Type: article

Abstract: Distinguishing treatment failure (recrudescence) from reinfection in uncomplicated falciparum malaria is essential for characterising antimalarial treatment efficacy in malaria endemic areas. Classification of recrudescence versus reinfection is usually based on a comparison of parasite allelic calls derived from PCR amplification and electrophoresis of individual polymorphic markers in the acute and recurrent blood samples. Match-counting methods (e.g., 3/3 or 2/3 matching alleles) have usually been applied, but these do not account for multiple comparisons per-marker when infections are polyclonal. We show that when infections are polyclonal, as is common in high transmission settings, currently used match-counting and model-based methods may have unacceptably high false-discovery rates leading to overestimation of treatment failure. We develop the software PfRecur which provides analytical Bayesian posterior probabilities of treatment failure in recurrent falciparum malaria. We use data from a recent study in Angola to demonstrate the potential utility of our model in resolving complex polyclonal P. falciparum infections, thereby providing more accurate estimation of treatment failure rates.

Source: Nature Communications

DOI: 10.1038/s41467-025-64830-z

Authors: Somya Mehra, Aimee R. Taylor, Mallika Imwong, Nicholas J. White, James A. Watson