Cargando…

A model and predictions for COVID-19 considering population behavior and vaccination

The effect of vaccination coupled with the behavioral response of the population is not well understood. Our model incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with infectious cases, while an increasing sense of...

Descripción completa

Detalles Bibliográficos
Autores principales: Usherwood, Thomas, LaJoie, Zachary, Srivastava, Vikas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187461/
https://www.ncbi.nlm.nih.gov/pubmed/34103618
http://dx.doi.org/10.1038/s41598-021-91514-7
_version_ 1783705135571009536
author Usherwood, Thomas
LaJoie, Zachary
Srivastava, Vikas
author_facet Usherwood, Thomas
LaJoie, Zachary
Srivastava, Vikas
author_sort Usherwood, Thomas
collection PubMed
description The effect of vaccination coupled with the behavioral response of the population is not well understood. Our model incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with infectious cases, while an increasing sense of safety with increased vaccination lowers precautions. Our model accurately reproduces the complete time history of COVID-19 infections for various regions of the United States. We propose a parameter [Formula: see text] as a direct measure of a population’s caution against an infectious disease that can be obtained from the infectious cases. The model provides quantitative measures of highest disease transmission rate, effective transmission rate, and cautionary behavior. We predict future COVID-19 trends in the United States accounting for vaccine rollout and behavior. Although a high rate of vaccination is critical to quickly ending the pandemic, 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 predict that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by August 2021. This model can be used for other regions and for future epidemics and pandemics.
format Online
Article
Text
id pubmed-8187461
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-81874612021-06-09 A model and predictions for COVID-19 considering population behavior and vaccination Usherwood, Thomas LaJoie, Zachary Srivastava, Vikas Sci Rep Article The effect of vaccination coupled with the behavioral response of the population is not well understood. Our model incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with infectious cases, while an increasing sense of safety with increased vaccination lowers precautions. Our model accurately reproduces the complete time history of COVID-19 infections for various regions of the United States. We propose a parameter [Formula: see text] as a direct measure of a population’s caution against an infectious disease that can be obtained from the infectious cases. The model provides quantitative measures of highest disease transmission rate, effective transmission rate, and cautionary behavior. We predict future COVID-19 trends in the United States accounting for vaccine rollout and behavior. Although a high rate of vaccination is critical to quickly ending the pandemic, 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 predict that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by August 2021. This model can be used for other regions and for future epidemics and pandemics. Nature Publishing Group UK 2021-06-08 /pmc/articles/PMC8187461/ /pubmed/34103618 http://dx.doi.org/10.1038/s41598-021-91514-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Usherwood, Thomas
LaJoie, Zachary
Srivastava, Vikas
A model and predictions for COVID-19 considering population behavior and vaccination
title A model and predictions for COVID-19 considering population behavior and vaccination
title_full A model and predictions for COVID-19 considering population behavior and vaccination
title_fullStr A model and predictions for COVID-19 considering population behavior and vaccination
title_full_unstemmed A model and predictions for COVID-19 considering population behavior and vaccination
title_short A model and predictions for COVID-19 considering population behavior and vaccination
title_sort model and predictions for covid-19 considering population behavior and vaccination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187461/
https://www.ncbi.nlm.nih.gov/pubmed/34103618
http://dx.doi.org/10.1038/s41598-021-91514-7
work_keys_str_mv AT usherwoodthomas amodelandpredictionsforcovid19consideringpopulationbehaviorandvaccination
AT lajoiezachary amodelandpredictionsforcovid19consideringpopulationbehaviorandvaccination
AT srivastavavikas amodelandpredictionsforcovid19consideringpopulationbehaviorandvaccination
AT usherwoodthomas modelandpredictionsforcovid19consideringpopulationbehaviorandvaccination
AT lajoiezachary modelandpredictionsforcovid19consideringpopulationbehaviorandvaccination
AT srivastavavikas modelandpredictionsforcovid19consideringpopulationbehaviorandvaccination