A spatiotemporal recommendation engine for malaria control

TL;DR

This paper presents a framework that combines a hierarchical Bayesian spatiotemporal model with a policy-search algorithm to optimize resource allocation for malaria control, improving long-term outcomes in simulations and real-world data.

Contribution

It introduces an interpretable, continuous-domain resource allocation policy framework that integrates disease modeling with policy optimization for malaria interventions.

Findings

Improved long-term outcomes in simulations.

Effective resource allocation in real-world malaria data.

Framework adaptable to other infectious diseases.

Abstract

Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited resources in realtime for malaria control. We formalize a policy for the resource allocation as a sequence of decisions, one per intervention decision, that map up-to-date disease related information to a resource allocation. An optimal policy must control the spread of the disease while being interpretable and viewed as equitable to stakeholders. We construct an interpretable class of resource allocation policies that can accommodate allocation of resources residing in a continuous domain, and combine a hierarchical Bayesian spatiotemporal model for disease transmission with a policy-search algorithm to estimate an optimal policy for resource allocation within the pre-specified class. The estimated optimal policy under the proposed framework improves the cumulative long-term outcome compared with naive approaches in both simulation experiments and application to malaria interventions in the Democratic Republic of the Congo.