Malaria is thought to have had the greatest disease burden throughout human history, while it continues to pose a significant but disproportionate global health burden. With 50% of the world’s population at risk of malaria infection. Sub Saharan Africa is most affected, with 90% of all cases.
Over the course of this live competition we are looking for participants to apply machine learning tools to determine novel solutions which could impact malaria policy in Sub Saharan Africa. Specifically, how should combinations of interventions which control the transmission, prevalence and health outcomes of malaria infection, be distributed in a simulated human population.
This challenge has been framed as a Reinforcement Learning problem, participants are expected to submit high performing solutions to the sequential decision making task. For this competition, actions receive stochastic and delayed rewards, which are resource constrained by both the monetary and computational costs associated with implementing and observing the impact of an action.
Submissions are encouraged from participants who may not have previous experience in reinforcement learning problems, reading through the materials we hope you may see the necessity for contributions to inform decision making for this complex real-world problem.