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Evaluation of the secondary transmission pattern and epidemic prediction of COVID-19 in the four metropolitan areas of China
Xue Wang
Qun Xu
Huizhen Jiang
Lin Han
Fengxiang Chang
Yingying Ma
Jie He
Yun Long
Weiguo Zhu
Na Hong
Longxiang Su
Novel Coronavirus
Acceso Abierto
Atribución-NoComercial-SinDerivadas
10.1101/2020.03.06.20032177
Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread can lead to secondary outbreaks outside Wuhan, the center of the new coronavirus disease outbreak. 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 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, R0, 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 approximately 466 with a peak time of February 29, 2020; however, if the city were to implement different levels (strict, mild, 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 56% and ~159%. 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 but that the risk will increase when businesses and social activities return to normal before the end of the epidemic. Besides, the experiences gained and lessons learned from China are potential to provide evidences supporting for other metropolitan areas and big cities with emerging cases outside China. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NA ### Funding Statement None ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data used to support the findings of this study were provided by Dr. Longxiang Su and Dr. Na Hong under license and thus cannot be made freely available. Access to these data will be considered by the author upon request.
Cold Spring Harbor Laboratory Press
2020
Preimpreso
https://www.medrxiv.org/content/10.1101/2020.03.06.20032177v2
Inglés
VIRUS RESPIRATORIOS
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