A model-based adaptive sampling framework for infectious disease surveillance

Abstract

Longitudinal surveillance must choose each round's sampling design before the next observations arrive. Fixed sample-size rules treat each round as an isolated cross-sectional survey and ignore the evolving uncertainty in the epidemic process, which changes as transmission, vaccination, interventions, variants, and behavior change. We present a model-based adaptive sampling framework for choosing the next-round design. A state-space model represents the epidemic process and maps latent states to surveillance estimands. A statistical filter calibrates the model as observations arrive, propagating uncertainty in latent states, time-varying parameters, stochastic dynamics, and sampling noise. A Bayesian assurance criterion converts the one-step-ahead predictive distribution into the minimum-cost design likely to achieve a target posterior credible-interval width. We evaluated the framework in two simulations and in a retrospective case study of SARS-CoV-2 serosurveillance among Canadian blood donors in four regions, replaying the series with each week's specimens subsampled to the selected design. In simulations, the policy increased sampling effort during an anticipated high-variance period, and the share of intervals meeting the width target (0.970) agreed with validated assurance (0.974). After an unanticipated change in transmission rate, validated assurance stayed at 0.960 while the share meeting the target fell to 0.894 (95% Monte Carlo interval 0.869 to 0.919) in the transition window. Sampling effort rose while the filter relearned the transmission rate, then fell. In the case study, recommended sample sizes tracked the one-step predictive uncertainty and were largest during the Omicron wave, and the 90% filtering interval had width at most 0.10 in 94% of region-rounds with specimens. The modular framework extends to other pathogens, data streams, and surveillance objectives.