Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread

Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang

To evaluate the effect of transportation suspension, we focused on the period between January 26, 2020 and February 4, 2020, during which the only major intervention is transportation suspension. Fig. 4 (A) plots the true cumulative number of infected cases estimated by our method during the period in a solid blue line, with the shaded area representing the 95% credible interval. Note that these numbers reflect the spread of COVID-19 in Wuhan under the intervention of transportation suspension. To simulate the hypothetical scenario that this intervention were not imposed, we used the SIQR model parameters estimated by our method for the window period between January 21, 2020 and January 26, 2020 when no intervention effect from transportation suspension was involved. We then ran the SIQR model for the evaluation period, with the estimated infective number on January 26, 2020 as the initial state, and computed the cumulative numbers of infected cases without the intervention. Fig. 4 (A) plots the computed cumulative numbers of infected cases without the intervention (dotted green line). In particular, by February 4, 2020, in the absence of the intervention, the number of infections would be expected to climb up to 117,842 [95% CI 55,098-238,212]. Using this number as the benchmark, the number of infections saved by the intervention during the evaluation period was 33,719, resulting in 29% reduction from the scenario of no intervention. Wuhan is a metropolitan area with an average of 8 million passengers using the city's public and private transportations daily 15, 16 . Shutting down the transportations reduced the average contact rate among the city residents. As a result, the adequate contact rate β was decreased 17 and the number of infections was reduced. See also the Method section for the parameter details.