KIMBERLY KROETCH

Title: Inverse Ensemble Forecasting for COVID-19 Outbreaks
Date: Friday, October 6th, 2023
Time: 1:00pm
Location: Zoom
Supervised by: Dr. Donald Estep

Abstract:We apply an inverse ensemble forecasting approach to COVID-19 outbreaks in a collection of U.S. counties containing college towns. Modelling disease progression with an SIR model, we define time-dependent maps from the infection parameters to infection levels over time and assume an unobserved probability distribution on the parameter space induces an output distribution on the infection levels. We estimate the output distribution with data and recover a distribution on the parameter space through the formulation of a Stochastic Inverse Problem solved through disintegration. This solution corresponds to a distribution over the possible infection curves from which we can forecast future infection levels in an ensemble forecasting framework. We verify the method through a simulation study, then apply the method to experimental data. Results suggest the method can provide accurate forecasts under certain population and modelling assumptions, but that the SIR model cannot adequately describe the disease dynamics in the population.

Keywords: Ensemble forecasting; Stochastic Inverse Problem; COVID-19; SIR model