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COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion
BACKGROUND: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. METHODS: We collected information...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871588/ https://www.ncbi.nlm.nih.gov/pubmed/36703835 http://dx.doi.org/10.3389/fpubh.2022.1076248 |
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author | Yi, Dali Chen, Xicheng Wang, Haojia Song, Qiuyue Zhang, Ling Li, Pengpeng Ye, Wei Chen, Jia Li, Fang Yi, Dong Wu, Yazhou |
author_facet | Yi, Dali Chen, Xicheng Wang, Haojia Song, Qiuyue Zhang, Ling Li, Pengpeng Ye, Wei Chen, Jia Li, Fang Yi, Dong Wu, Yazhou |
author_sort | Yi, Dali |
collection | PubMed |
description | BACKGROUND: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. METHODS: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C(r)). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R(t)). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. RESULTS: (1) The overall level of R(t) in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R(t) decreased rapidly, and the overall R(t) level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. CONCLUSION: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions. |
format | Online Article Text |
id | pubmed-9871588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98715882023-01-25 COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion Yi, Dali Chen, Xicheng Wang, Haojia Song, Qiuyue Zhang, Ling Li, Pengpeng Ye, Wei Chen, Jia Li, Fang Yi, Dong Wu, Yazhou Front Public Health Public Health BACKGROUND: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. METHODS: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C(r)). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R(t)). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. RESULTS: (1) The overall level of R(t) in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R(t) decreased rapidly, and the overall R(t) level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. CONCLUSION: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions. Frontiers Media S.A. 2023-01-10 /pmc/articles/PMC9871588/ /pubmed/36703835 http://dx.doi.org/10.3389/fpubh.2022.1076248 Text en Copyright © 2023 Yi, Chen, Wang, Song, Zhang, Li, Ye, Chen, Li, Yi and Wu. 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 Yi, Dali Chen, Xicheng Wang, Haojia Song, Qiuyue Zhang, Ling Li, Pengpeng Ye, Wei Chen, Jia Li, Fang Yi, Dong Wu, Yazhou COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title | COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title_full | COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title_fullStr | COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title_full_unstemmed | COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title_short | COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion |
title_sort | covid-19 epidemic and public health interventions in shanghai, china: statistical analysis of transmission, correlation and conversion |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871588/ https://www.ncbi.nlm.nih.gov/pubmed/36703835 http://dx.doi.org/10.3389/fpubh.2022.1076248 |
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