Classical infectious disease modeling paradigms shifted by the SARS-CoV-2 pandemic

Document Type

Presentation Abstract

Presentation Date

10-11-2021

Abstract

Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has provided an abundant amount of data that allows for thorough testing of disease modeling assumptions, as well as how we think about classical infectious disease modeling assumptions. We use simulations to demonstrate the minimal data (infected, active, quarantined, and recovered) needed for collection and reporting that are sufficient for reliable model parameter identification and prediction accuracy. Using a classical example of influenza epidemics in an England boarding school, we show that the Susceptible-Infected-Quarantined-Recovered model is more appropriate than the commonly employed Susceptible-Infected-Recovered model. We demonstrate the role of misclassification and the importance of correctly classifying reported data to the proper compartment in a COVID-19 disease model and implications of using “right” data in the “wrong” model. The role of misclassification and the importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.

Additional Details

October 11, 2021 at 3:00 p.m. in Math 305

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