Novel Exploration Techniques (NETs) for Malaria Policy Interventions
This work addresses healthcare policy-making challenges for malaria interventions, but it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of finding optimal malaria policies under uncertainty by framing it as a stochastic multi-armed bandit problem and implementing three agent-based strategies, resulting in a platform that provides a direct reference to human expert decisions for simulated populations.
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.