Health system planning and optimisation - advancements in the application of machine learning to policy decisions in global health

Global health is a domain which presents significant, pressing and timely challenges. Due to the scale of the problems, the potential impact on peoples' lives from even incremental improvement in policy decision making is truly significant. In this chapter, we explore the contributions which may be made through the application of machine learning methods - specifically in the planning and optimisation of population level healthcare interventions. To guide evidence-based policy enhanced through the insights of machine learning models. In order to achieve this, we will focus on existing work in the domain of malaria control policy while also expanding current results on the COVID-19 epidemic. Specifically demonstrating advancements possible through the sharing of data, simulations and compute. While ultimately these factors have enabled engagement in Global Health planning and optimisation, through the application of reinforcement learning methods. This approach has already attracted multiple contributions from machine learning researchers globally, on significant Global Health challenges.