Abstract
In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.
Highlights
MethodsThe epidemic data used in this paper were from the latest epidemiological data of COVID-19 reported by the Ding Xiang Yuan [31]
Accepted: December 21, 2020Published: January 8, 2021
Prediction of Wuhan infection trend based on the SEIR model
Summary
The epidemic data used in this paper were from the latest epidemiological data of COVID-19 reported by the Ding Xiang Yuan [31]. The urban migration index was derived from the Baidu migration project [32], which is based on the users of Baidu and related products to count and calculate the daily number of pedestrian movements in and out of cities by railway, air and highway. The population density, per capita GDP and other urban data of all provinces in. China were obtained from the official website of the National Bureau of Statistics [33]. The distance from each province to Wuhan was obtained from the Ovi interactive map [34]. The average temperature of each province in 2019 was from the China Meteorological Administration [35]
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