Cargando…
Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China
Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668860/ https://www.ncbi.nlm.nih.gov/pubmed/36405530 http://dx.doi.org/10.3389/ijph.2022.1605177 |
_version_ | 1784832005168431104 |
---|---|
author | Zheng, Yangcheng Wang, Yunpeng |
author_facet | Zheng, Yangcheng Wang, Yunpeng |
author_sort | Zheng, Yangcheng |
collection | PubMed |
description | Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities. Results: An infection period of 5 days and basic reproduction number R(0) between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R(0), and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49 days. Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently. |
format | Online Article Text |
id | pubmed-9668860 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96688602022-11-18 Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China Zheng, Yangcheng Wang, Yunpeng Int J Public Health Public Health Archive Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities. Results: An infection period of 5 days and basic reproduction number R(0) between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R(0), and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49 days. Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently. Frontiers Media S.A. 2022-11-03 /pmc/articles/PMC9668860/ /pubmed/36405530 http://dx.doi.org/10.3389/ijph.2022.1605177 Text en Copyright © 2022 Zheng and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Archive Zheng, Yangcheng Wang, Yunpeng Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title | Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title_full | Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title_fullStr | Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title_full_unstemmed | Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title_short | Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China |
title_sort | transmission characteristics and predictive model for recent epidemic waves of covid-19 associated with omicron variant in major cities in china |
topic | Public Health Archive |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668860/ https://www.ncbi.nlm.nih.gov/pubmed/36405530 http://dx.doi.org/10.3389/ijph.2022.1605177 |
work_keys_str_mv | AT zhengyangcheng transmissioncharacteristicsandpredictivemodelforrecentepidemicwavesofcovid19associatedwithomicronvariantinmajorcitiesinchina AT wangyunpeng transmissioncharacteristicsandpredictivemodelforrecentepidemicwavesofcovid19associatedwithomicronvariantinmajorcitiesinchina |