Cargando…

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to s...

Descripción completa

Detalles Bibliográficos
Autores principales: Su, Longxiang, Hong, Na, Zhou, Xiang, He, Jie, Ma, Yingying, Jiang, Huizhen, Han, Lin, Chang, Fengxiang, Shan, Guangliang, Zhu, Weiguo, Long, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7221060/
https://www.ncbi.nlm.nih.gov/pubmed/32574319
http://dx.doi.org/10.3389/fmed.2020.00171
_version_ 1783533289704783872
author Su, Longxiang
Hong, Na
Zhou, Xiang
He, Jie
Ma, Yingying
Jiang, Huizhen
Han, Lin
Chang, Fengxiang
Shan, Guangliang
Zhu, Weiguo
Long, Yun
author_facet Su, Longxiang
Hong, Na
Zhou, Xiang
He, Jie
Ma, Yingying
Jiang, Huizhen
Han, Lin
Chang, Fengxiang
Shan, Guangliang
Zhu, Weiguo
Long, Yun
author_sort Su, Longxiang
collection PubMed
description Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R(0), was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.
format Online
Article
Text
id pubmed-7221060
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-72210602020-05-25 Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China Su, Longxiang Hong, Na Zhou, Xiang He, Jie Ma, Yingying Jiang, Huizhen Han, Lin Chang, Fengxiang Shan, Guangliang Zhu, Weiguo Long, Yun Front Med (Lausanne) Medicine Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R(0), was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China. Frontiers Media S.A. 2020-05-07 /pmc/articles/PMC7221060/ /pubmed/32574319 http://dx.doi.org/10.3389/fmed.2020.00171 Text en Copyright © 2020 Su, Hong, Zhou, He, Ma, Jiang, Han, Chang, Shan, Zhu and Long. http://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 Medicine
Su, Longxiang
Hong, Na
Zhou, Xiang
He, Jie
Ma, Yingying
Jiang, Huizhen
Han, Lin
Chang, Fengxiang
Shan, Guangliang
Zhu, Weiguo
Long, Yun
Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title_full Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title_fullStr Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title_full_unstemmed Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title_short Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China
title_sort evaluation of the secondary transmission pattern and epidemic prediction of covid-19 in the four metropolitan areas of china
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7221060/
https://www.ncbi.nlm.nih.gov/pubmed/32574319
http://dx.doi.org/10.3389/fmed.2020.00171
work_keys_str_mv AT sulongxiang evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT hongna evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT zhouxiang evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT hejie evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT mayingying evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT jianghuizhen evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT hanlin evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT changfengxiang evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT shanguangliang evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT zhuweiguo evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina
AT longyun evaluationofthesecondarytransmissionpatternandepidemicpredictionofcovid19inthefourmetropolitanareasofchina