Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784862711780212736 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9807409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | AIMS Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT owusugabriel temporaldynamicsforarealunitbasedcooccurrencecovid19trajectories AT yuhan temporaldynamicsforarealunitbasedcooccurrencecovid19trajectories AT huanghong temporaldynamicsforarealunitbasedcooccurrencecovid19trajectories |