Parameter identifiability and uncertainty in modeling infectious disease interventions

Tuesday, May 29, 2018 - 9:00am - 9:50am
Lind 305
Marisa Eisenberg (University of Michigan)
Connecting dynamic models with data to yield insights and predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. Here, we will discuss different approaches to examining parameter identifiability and uncertainty, and examine how these issues affect parameter estimation and intervention predictions. Using examples taken from several recent epidemics of vectorborne and environmentally driven diseases, we illustrate some of the potential difficulties caused by unidentifiability, and show how alternative data collection may help reduce parameter uncertainty.