  Strategic test allocation plays a major role in the control of both emerging
and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports
effective 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. For
instance, the true outcome of interest - one's positive infectious status, is
often a latent variable. In addition, presence of both network and temporal
dependence reduces the data to a single observation. As testing entire
populations regularly is neither efficient nor feasible, standard approaches to
testing recommend simple rule-based testing strategies (e.g., symptom based,
contact tracing), without taking into account individual risk. In this work, we
study an adaptive sequential design involving n individuals over a period of
{\tau} time-steps, which allows for unspecified dependence among individuals
and across time. Our causal target parameter is the mean latent outcome we
would have obtained after one time-step, if, starting at time t given the
observed past, we had carried out a stochastic intervention that maximizes the
outcome under a resource constraint. We propose an Online Super Learner for
adaptive sequential surveillance that learns the optimal choice of tests
strategies over time while adapting to the current state of the outbreak.
Relying on a series of working models, the proposed method learns across
samples, through time, or both: based on the underlying (unknown) structure in
the data. We present an identification result for the latent outcome in terms
of the observed data, and demonstrate the superior performance of the proposed
strategy in a simulation modeling a residential university environment during
the COVID-19 pandemic.
