Novel Exploration Techniques (NETs) for Malaria Policy Interventions

TL;DR

This paper introduces a novel framework using stochastic multi-armed bandit strategies and Gaussian Process regression to optimize malaria policy interventions under uncertainty, aiding decision-makers with data-driven tools.

Contribution

It presents a new approach framing malaria policy optimization as a stochastic bandit problem, integrating Gaussian Process regression for improved exploration and comparison with expert decisions.

Findings

Effective exploration of policy space using bandit strategies

Gaussian Process regression improves decision accuracy

Comparable results to human expert policies

Abstract

The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.