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

2020Frontiers in Medicine42 citationsDOIOpen Access PDF

Abstract

Understanding the transmission dynamics of COVID-19 is crucial for evaluating the spread pattern of it, especially in metropolitan areas of China which may cause secondary outbreaks outside Wuhan, the center of the new coronavirus disease outbreak. We used reported data from Jan 24, 2020, to Feb 23, 2020, fitted the model of infection, and on the number of cases reported to estimate likely number of infections in four high risk metropolitan areas, as well as facilitate understanding the COVID-19's spread pattern. A group of SERI model statistical parameters were estimated using Markov Chain Monte Carlo (MCMC) methods, and our modeling integrated the effect of the official quarantine regulation and travel restriction of China. As a result, we estimated that the basic reproductive number R0 ​is 3.11 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen. In addition, we inferred the prediction results and compared the results of different level of parameters, For example, In Beijing, the predicated peak number of cases is around 466 at the peak time Feb 29, 2020; however, when the city conducts different levels (strict, mild, or weak) of travel restrictions or regulation measures, the estimation results show that transmission dynamics will change and the peek number of cases shows the changing proportion is between 56%~159%. We concluded that public health interventions would reduce the risks of COVID-19 spreading and more rigorous control and prevention measures will effectively contain its further spread, but risk increases when businesses and social activities returning back before the ending date. 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.

Topics & Concepts

BeijingMetropolitan areaChinaGeographyCoronavirus disease 2019 (COVID-19)DemographyOutbreakMarkov chain Monte CarloPopulationEstimationStatisticsQuarantineTransmission (telecommunications)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SocioeconomicsMonte Carlo methodMedicineComputer scienceMathematicsVirologyEngineeringEconomicsTelecommunicationsDiseaseSociologyArchaeologyInfectious disease (medical specialty)Systems engineeringPathologyCOVID-19 epidemiological studiesData-Driven Disease SurveillanceCOVID-19 Digital Contact Tracing