Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling

tianyi qiu; Han Xiao

Based on the fitted parameters, the simulation showed that it would take 14 days for people to transfer from status ‫ܯ‬ to status ܱ . For those out of the system before been hospitalized, it would take 28 days to transfer from status ) raised up to 8,509 on Jan 26 th . With the default parameters, the epidemic in Wuhan was described by local status dynamic SEIO (MH) modeling. Illustrated in Figure 2a , patient zero was inferred to appear on Nov. 29 th , 2019. From that day to Jan 23 th , 2020, when less intervention was applied to prevent the epidemic, the number of infected people increased exponentially. After Jan 23 th , the circulation intensity of people in Wuhan decreased sharply after the city lockdown 19 . However, the people infected with symptoms, exposed and in hospitalization were estimated to be 1,682, 6,536 and 2,471 respectively on Jan 22 nd , 2020 and increased to 1,999, 7,676 and 2,877 respectively on Jan 23 rd , 2020 (Figure 2b) , which was a large population with infected people and would lead to the outbreaks of the epidemic in the early period even after the lockdown.