Author: Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang
Title: Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread Document date: 2020_4_6
ID: ff4937mj_48
Snippet: where a 1 and b 1 are the corresponding under-reporting factors for the time period of [1, 2, ..., T + 1], and M 1 represents the true cumulative number of infections by day 1 or January 18, 2020. Using the number of infected cases exported from Wuhan internationally, Imai et al. 12 estimate that the cumulative number of infections in Wuhan by January 18, 2020 is 4,000 with a 95% confidence interval [1, 800] in the baseline scenario. Additionally.....
Document: where a 1 and b 1 are the corresponding under-reporting factors for the time period of [1, 2, ..., T + 1], and M 1 represents the true cumulative number of infections by day 1 or January 18, 2020. Using the number of infected cases exported from Wuhan internationally, Imai et al. 12 estimate that the cumulative number of infections in Wuhan by January 18, 2020 is 4,000 with a 95% confidence interval [1, 800] in the baseline scenario. Additionally, to account for 2 million people leaving Wuhan due to Wuhan lockdown on January 23, 2020, we set the population size N to be 11 million (i.e., regular population size in Wuhan 35 ) before January 23, 2020 and adjust it to 9 million after January 23, 2020 36 . With the above setting and the observed official numbers [Q o i , R o i ] T +1 i=1 , we can estimate parameters Θ 1 = (β 1 , µ 1 , γ 1 , a 1 , b 1 ) and compute [S i ,
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