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Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China

BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level...

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Autores principales: Ma, Yifei, Xu, Shujun, Luo, Yuxin, Li, Jiantao, Lei, Lijian, He, Lu, Wang, Tong, Yu, Hongmei, Xie, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693062/
https://www.ncbi.nlm.nih.gov/pubmed/38042794
http://dx.doi.org/10.1186/s12889-023-17327-7
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author Ma, Yifei
Xu, Shujun
Luo, Yuxin
Li, Jiantao
Lei, Lijian
He, Lu
Wang, Tong
Yu, Hongmei
Xie, Jun
author_facet Ma, Yifei
Xu, Shujun
Luo, Yuxin
Li, Jiantao
Lei, Lijian
He, Lu
Wang, Tong
Yu, Hongmei
Xie, Jun
author_sort Ma, Yifei
collection PubMed
description BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R(t) curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R(t) below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-17327-7.
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spelling pubmed-106930622023-12-03 Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China Ma, Yifei Xu, Shujun Luo, Yuxin Li, Jiantao Lei, Lijian He, Lu Wang, Tong Yu, Hongmei Xie, Jun BMC Public Health Research BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R(t) curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R(t) below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-17327-7. BioMed Central 2023-12-02 /pmc/articles/PMC10693062/ /pubmed/38042794 http://dx.doi.org/10.1186/s12889-023-17327-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ma, Yifei
Xu, Shujun
Luo, Yuxin
Li, Jiantao
Lei, Lijian
He, Lu
Wang, Tong
Yu, Hongmei
Xie, Jun
Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title_full Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title_fullStr Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title_full_unstemmed Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title_short Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
title_sort model-based analysis of the incidence trends and transmission dynamics of covid-19 associated with the omicron variant in representative cities in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693062/
https://www.ncbi.nlm.nih.gov/pubmed/38042794
http://dx.doi.org/10.1186/s12889-023-17327-7
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