Addressing Assumption Uncertainty in Models of Vector-Borne Disease Epidemiology

Alex Perkins, PhD - Associate Professor of Biological Sciences and Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN

Twitter: @TAlexPerkins

Abstract: Assumptions are central to the formulation of models of pathogen transmission and control. Many of the assumptions made in models have a sound basis in first principles or are well-informed by empirical knowledge, yet others have a weaker basis. In this talk, I will present two different applications of models to problems in vector-borne disease epidemiology in which the sensitivity of model predictions to assumptions was evaluated. These applications—one to forecasting the Zika epidemic and the other to estimating the burden of yellow fever—illustrate the value, and limitations, of ensemble approaches to account for assumption uncertainty in models.

March 24, 2022