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Large-scale Nonlinear Approaches for Inference of Reporting Dynamics and Unobserved SARS-CoV-2 Infections

Hart, William E.; Bynum, Michael L.; Laird, Carl L.; Siirola, John D.; Staid, Andrea S.

This work focuses on estimation of unknown states and parameters in a discrete-time, stochastic, SEIR model using reported case counts and mortality data. An SEIR model is based on classifying individuals with respect to their status in regards to the progression of the disease, where S is the number individuals who remain susceptible to the disease, E is the number of individuals who have been exposed to the disease but not yet infectious, I is the number of individuals who are currently infectious, and R is the number of recovered individuals. For convenience, we include in our notation the number of infections or transmissions, T, that represents the number of individuals transitioning from compartment S to compartment E over a particular interval. Similarly, we use C to represent the number of reported cases.