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

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Infectious Diseases of Poverty 2022年第3期11卷 37-53页

作者： Monica Golumbeanu Guo-Jing Yang Flavia Camponovo Erin M.Stuckey Nicholas Hamon Mathias Mondy Sarah Rees nakul chitnis Ewan Cameron Melissa A.Penny Swiss Tropical and Public Health Institute AllschwilSwitzerland Key Laboratory of Tropical Translational Medicine of Ministry of Education and School of Tropical Medicine and Laboratory Medicine The First and Second Affiliated Hospital of Hainan Medical UniversityHainan Medical UniversityHaikouHainanPeople’s Republic of China University of Basel BaselSwitzerland Center for Communicable Disease Dynamics Department of EpidemiologyHarvard T.H.Chan School of Public HealthBostonMA02115USA The Bill&Melinda Gates Foundation SeattleWAUSA Innovative Vector Control Consortium LiverpoolUK Malaria Atlas Project Big Data InstituteUniversity of OxfordOxfordUK Curtin University PerthAustralia Telethon Kids Institute Perth Children’s HospitalPerthAustralia

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 deployment and predict potential impact of new malaria interventions by considering operational,health-system,population,and disease characteristics.Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications.We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals.Results:We apply our approach to five malaria interventions under development.Aiming for malaria prevalence reduction,we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals.While coverage is generally identified as the largest driver of impact,higher efficacy,longer protection duration or multiple deployments per year are needed to increase prevalence reduction.We show that interventions on multiple parasite or vector targets,as well as combinations the new interventions with drug treatment,lead to significant burden reductions and lower efficacy or duration requirements.Conclusions:Our approach uses disease dynamic models and machine learning to support decision-making and resource investment,facilita

关键词： Infectious diseases Malaria Novel interventions Mathematical modelling Machine learning

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