Data-Driven Near-Optimal Regional Epidemic Surveillance
Year: 2026
Type: conference-paper
Abstract: In the early stages of an epidemic outbreak, when the incidence data is limited, policy makers actively try to establish the scale and scope of the outbreak. Two of the most pertinent questions they seek to answer are i) has the outbreak started in a particular region (e.g. a country) and ii) how many sub-regions (e.g. a state or a county) have been affected thus far? We study these problems as a data-driven knowledge extraction task, i.e., given mobility and epidemiological data, we aim to extract actionable knowledge about which regions should receive additional surveillance resources so that an outbreak is detected early and its spatial footprint is accurately characterized. To this end, we formulate two discrete-lattice optimization objectives for epidemic surveillance, 1) maximizing the probability of detecting at least one infected sub-region and 2) maximizing the expected number of infected sub-regions detected. Both objectives turn out to be NP-hard. However, we prove that they are both submodular, which paves the way for efficient epidemic surveillance from limited data. We use this structure to derive curvature bounds for the objective. We also relate the bound with the parameters of the metapopulation disease dynamics. We propose a scalable algorithm, Lattice-Adapted Modified Greedy (LAMGreedy), that uses the curvature, and marginal-gain machinery to establish constant factor approximation guarantee. Extensive experiments on real-world mobility networks for COVID-19 and Influenza across three U.S. states show that LAMGreedy consistently outperforms natural and the state-of-the-art baselines under multiple cost models. The empirical results validate the prevalence and curvature estimates, and demonstrate that the proposed tools extract high-quality surveillance knowledge from mobility and epidemic data under realistic budget constraints.
Institution: University of Iowa
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7225100552