Novel Exploration Techniques (NETs) for Malaria Policy Interventions

Understand

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.