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...
Autores principales: | , , |
---|---|
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 |