Modeling and prediction of COVID-19 in the United States considering population behavior and vaccination
Thomas Usherwood, Zachary LaJoie, Vikas Srivastava (corresponding, author)

TL;DR
This paper introduces a novel dynamic model of COVID-19 in the US that incorporates population behavior and vaccination effects, enabling accurate prediction of infection trends and behavioral impacts.
Contribution
It presents the first model effectively capturing COVID-19 infection history in the US while quantifying behavioral responses and vaccination effects on disease transmission.
Findings
The model accurately reproduces COVID-19 infection trends across US regions.
Behavioral responses significantly influence infection surges during vaccination rollout.
Predicted COVID-19 cases will approach zero by late August 2021 at current vaccination rates.
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Influenza Virus Research Studies · Viral Infections and Outbreaks Research
