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In many public health settings, there is a perceived tension betweenallocating resources to known vulnerable areas and learning about the overallprevalence of the problem. Inspired by a door-to-door Covid-19 testing programwe helped design, we combine multi-armed bandit strategies and insights fromsampling theory to demonstrate how to recover accurate prevalence estimateswhile continuing to allocate resources to at-risk areas. We use the outbreak ofan infectious disease as our running example. The public health setting hasseveral characteristics distinguishing it from typical bandit settings, such asdistribution shift (the true disease prevalence is changing with time) andbatched sampling (multiple decisions must be made simultaneously).Nevertheless, we demonstrate that several bandit algorithms are capableout-performing greedy resource allocation strategies, which often perform worsethan random allocation as they fail to notice outbreaks in new areas.

Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits