Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.

ABSTRACT:

Background

Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence.

Methods

A mathematical model of malaria transmission dynamics is used to simulate

SUBMITTER: Golumbeanu M 

PROVIDER: S-EPMC9167503 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature