What is this publication about?

The article presents results of statistical modelling that estimated the distribution of Kelch 13 mutations in the Plasmodium falciparum parasite in Sub-Saharan Africa, as it varies through space and time. The models use existing surveillance data to estimate Kelch 13 mutation prevalence in regions with limited molecular surveillance. The mutations are associated with partial resistance to artemisinin-based combination therapies (ACTs), the most widely used treatment for Plasmodium falciparum malaria. The article also describes statistical modelling estimates of the prevalence of mutations associated with reduced susceptibility to ACT partner drugs.

Why is this important?

Kelch 13 mutations are being found in an increasing number of countries in Sub-Saharan Africa, threatening the efficacy of malaria treatments. Molecular surveillance is a  key piece of evidence to consider in maintaining malaria treatment guidelines, but this data is not available in all regions. The models presented in the paper help visualise observed mutation prevalence and extrapolate it to areas where surveillance data doesn’t exist. The models also provide estimates of prediction uncertainty, factoring spatial and temporal bias in the surveillance dataset.

How can this make a difference?

The distribution models described in the preprint visualise changes in resistance mutation prevalences over space and time. The models can help decision-makers to formulate their malaria policies in a time when global health funding and surveillance capacity are limited.