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Evaluating the secondary transmission pattern and epidemic prediction of the COVID-19 in metropolitan areas of China
Longxiang Su; Na Hong; Xiang Zhou; Jie He; Yingying Ma; Huizhen Jiang; Lin Han; Fengxiang Chang; Guangliang Shan; Weiguo Zhu; Yun Long.
Affiliation
  • Longxiang Su; Peking Union Medical College Hospital
  • Na Hong; Digital China Health Technologies Co. Ltd., Beijing, China
  • Xiang Zhou; Peking Union Medical College Hospital
  • Jie He; Digital China Health Technologies Co. Ltd., Beijing, China
  • Yingying Ma; Digital China Health Technologies Co. Ltd., Beijing, China
  • Huizhen Jiang; Peking Union Medical College Hospital
  • Lin Han; Digital China Health Technologies Co. Ltd., Beijing, China
  • Fengxiang Chang; Digital China Health Technologies Co. Ltd.
  • Guangliang Shan; Institute of Basic Medicine Sciences, Peking Union Medical College & Chinese Academy of Medical Sciences
  • Weiguo Zhu; Peking Union Medical College Hospital
  • Yun Long; Peking Union Medical College Hospital
Preprint in English | medRxiv | ID: ppmedrxiv-20032177
Journal article
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ABSTRACT
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.
License
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study / Qualitative research Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study / Qualitative research Language: English Year: 2020 Document type: Preprint
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