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Strategic test allocation plays a major role in the control of both emergingand existing pandemics (e.g., COVID-19, HIV). Widespread testing supportseffective epidemic control by (1) reducing transmission via identifying cases,and (2) tracking outbreak dynamics to inform targeted interventions. However,infectious disease surveillance presents unique statistical challenges. Forinstance, the true outcome of interest - one's positive infectious status, isoften a latent variable. In addition, presence of both network and temporaldependence reduces the data to a single observation. As testing entirepopulations regularly is neither efficient nor feasible, standard approaches totesting recommend simple rule-based testing strategies (e.g., symptom based,contact tracing), without taking into account individual risk. In this work, westudy an adaptive sequential design involving n individuals over a period of{\tau} time-steps, which allows for unspecified dependence among individualsand across time. Our causal target parameter is the mean latent outcome wewould have obtained after one time-step, if, starting at time t given theobserved past, we had carried out a stochastic intervention that maximizes theoutcome under a resource constraint. We propose an Online Super Learner foradaptive sequential surveillance that learns the optimal choice of testsstrategies over time while adapting to the current state of the outbreak.Relying on a series of working models, the proposed method learns acrosssamples, through time, or both: based on the underlying (unknown) structure inthe data. We present an identification result for the latent outcome in termsof the observed data, and demonstrate the superior performance of the proposedstrategy in a simulation modeling a residential university environment duringthe COVID-19 pandemic.

Adaptive Sequential Surveillance with Network and Temporal Dependence