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Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function...

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Detalles Bibliográficos
Autores principales: Owusu, Gabriel, Yu, Han, Huang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIMS Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807409/
https://www.ncbi.nlm.nih.gov/pubmed/36636154
http://dx.doi.org/10.3934/publichealth.2022049
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author Owusu, Gabriel
Yu, Han
Huang, Hong
author_facet Owusu, Gabriel
Yu, Han
Huang, Hong
author_sort Owusu, Gabriel
collection PubMed
description The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.
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spelling pubmed-98074092023-01-11 Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories Owusu, Gabriel Yu, Han Huang, Hong AIMS Public Health Research Article The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students. AIMS Press 2022-10-14 /pmc/articles/PMC9807409/ /pubmed/36636154 http://dx.doi.org/10.3934/publichealth.2022049 Text en © 2022 the Author(s), licensee AIMS Press https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0 (https://creativecommons.org/licenses/by/4.0/) )
spellingShingle Research Article
Owusu, Gabriel
Yu, Han
Huang, Hong
Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title_full Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title_fullStr Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title_full_unstemmed Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title_short Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories
title_sort temporal dynamics for areal unit-based co-occurrence covid-19 trajectories
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807409/
https://www.ncbi.nlm.nih.gov/pubmed/36636154
http://dx.doi.org/10.3934/publichealth.2022049
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