The University of Arizona
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Epidemiological Forecasting with ICC curves and data assimilation

Modeling, Computation, Nonlinearity, Randomness and Waves Seminar

Epidemiological Forecasting with ICC curves and data assimilation
Series: Modeling, Computation, Nonlinearity, Randomness and Waves Seminar
Location: Online
Presenter: Joceline Lega, Department of Mathematics, University of Arizona

Every week, the CDC posts COVID-19 death forecasts for the US and its states and territories. These estimates are created with an ensemble model that combines probabilistic predictions made by a variety of groups in the US and abroad. Our model, EpiCovDA, which is developed by mathematics graduate student Hannah Biegel, is one of these contributions. In this talk, I will present a novel paradigm for epidemiological modeling that centers around ICC curves, which relate incidence to the cumulative cases of an outbreak. I will then explain how this approach may be used for parameter estimation, and how it is combined with data assimilation in EpiCovDA.

Zoom:  https://arizona.zoom.us/j/94534134312   
Password:  “arizona” (all lower case)