Modeling and prediction of COVID-19 in the United States considering population behavior and vaccination

Abstract

COVID-19 has devastated the entire global community. Vaccines present an opportunity to mitigate the pandemic; however, the effect of vaccination coupled with the behavioral response of the population is not well understood. We propose a model that incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with the number of infectious cases, while an increasing sense of safety with increased vaccination lowers precautionary behaviors. To the best of our knowledge, this is the first model that can effectively reproduce the complete time history of COVID-19 infections for various regions of the United States and provides relatable measures of dynamic changes in the population behavior and disease transmission rates. We propose a parameter d_I as a direct measure of a population's caution against an infectious disease, that can be obtained from the ongoing new infectious cases. The model provides a method for quantitative measure of critical infectious disease attributes for a population including highest disease transmission rate, effective disease transmission rate, and disease related precautionary behavior. We predict future COVID-19 pandemic trends in the United States accounting for vaccine rollout and behavioral response. Although a high rate of vaccination is critical to quickly end the pandemic, we find that a return towards pre-pandemic social behavior due to increased sense of safety during vaccine deployment, can cause an alarming surge in infections. Our results indicate that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by the end of August 2021. The model can be used for predicting future epidemic and pandemic dynamics before and during vaccination.