Reframing risk assessment for malaria elimination in a changing climate

Abstract

Current malaria risk stratification approaches that support the global malaria eradication agenda are limited in capturing rapidly shifting pathogen transmission dynamics, unprecedented climate variability, extreme weather events, land use changes (such as urbanization) and population mobility. We propose reframing malaria burden-based metrics into anticipatory risk intelligence systems that leverage artificial intelligence tools. Current malaria risk stratification approaches that support the global malaria eradication agenda are limited in capturing rapidly shifting pathogen transmission dynamics, unprecedented climate variability, extreme weather events, land use changes and population mobility. Omumbo, Hassell and Siwo propose reframing malaria burden-based metrics into anticipatory risk intelligence systems that leverage artificial intelligence tools.