Reachable Outcomes of Combining Multiple Interventions for Control of Malaria and Drug Resistance: Models, Analysis, and a Framework for Adaptive Programs

Abstract

How might we take advantage of the currently available anti-malarial interventions to control malaria and parasite drug resistance in endemic areas? Interventions include antimalarial drugs, insecticide-treated bednets, housing features, indoor residual spraying of insecticides, and reduction of larval habitats. Are there possible advantages to combining interventions with respect to reducing malaria burden and drug resistance rate? How might we intentionally design an adaptive intervention program which efficiently improves over time? We develop new models and frameworks to propose some possible answers. We propose a framework for multi-intervention adaptive programs for malaria and drug resistance control. This framework incorporates sound components from statistical and engineering methods, mechanistic epidemiological models like we develop, and intervention-focused operational research. All our models are structured to take as input baseline transmission setting and coverage of interventions and return as output malaria burden and drug resistance over time. Model forms include simple extensions of early compartmental models suitable for low endemic settings, analytical approximations, a novel framework of modular compartmental models for low and high endemic settings, and a microsimulation which tracks an interacting group of individual humans and mosquitoes exchanging mutating parasites for various transmission settings. Details simulated include malaria transmission, superinfection, immunity dynamics, drug resistance emergence and/or spread, drug pharmacodynamics/pharmacokinetics, and multiple interventions. We use published data and measurements for model parameterization and validation. Our model analysis systematically explores the large space of possible multi-intervention programs and their possible effects on malaria burden and drug resistance. Our models predict that interventions can combine constructively to reduce malaria burden. Interestingly, except for the simple analytical approximations, our models also predict that drug resistance depends not only on the fraction of clinical infections drug-treated but also on vector-related interventions in a possibly complex manner depending on transmission settings, overall coverage of interventions, and whether the focus is spread of resistance or also emergence of resistance. Our modular compartmental models predict theoretically – along with some possible caveats to avoid – how vector-related interventions, in a program with drug treatment, might be used to generate possible advantages with respect to both reducing malaria burden and drug resistance spread.