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, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

Infectious diseases;Malaria;Novel interventions;Mathematical modelling;Machine learning

Swiss Tropical and Public Health Institute, Allschwil, Switzerland;University of Basel, Basel, Switzerland;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 University, Hainan Medical University, Haikou, Hainan, People’s Republic of China;Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA;The Bill & Melinda Gates Foundation, Seattle, WA, USA;Innovative Vector Control Consortium, Liverpool, UK;Malaria Atlas Project, Big Data Institute, University of Oxford, Oxford, UK;Curtin University, Perth, Australia;Telethon Kids Institute, Perth Children’s Hospital, Perth, Australia

[1]Golumbeanu Monica;Yang Guo-Jing;Camponovo Flavia;Stuckey Erin M.;Hamon Nicholas;Mondy Mathias;Rees Sarah;Chitnis Nakul;Cameron Ewan;Penny Melissa A.-.Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions)[J].贫困所致传染病（英文）,2022(03):37-53

predefine,channeled

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