Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province

Hui Wan; Jing-an Cui; Guo-Jing Yang

Recently, some papers have been released as pre-prints or undergone peer-review and published to estimate R 0 and the risk of outbreak. Li et al. ([6] ) analyzed data on the first 425 confirmed cases in Wuhan and determined the epidemiologic characteristics of COVID-19. Based on their estimates, the mean incubation period was 5.2 days, and R 0 was 2.2, which is in line with the result estimated by Riou et al. ([7] ). Zhao et al. ( [8] ) assessed the unreported number of COVID-19 cases in China in the first half of January with the estimation of R 0 2.56. Considering the impact of the variations in disease reporting rate, Zhao et al. ( [9] ) modelled the epidemic curve of COVID-19 cases, in mainland China from January 10 to January 24, 2020, through the exponential growth and concluded that the mean R 0 ranged from 2.24 to 3.58 associated with 8-fold to 2-fold increase in the reporting rate. Li et al. ([10] ) conducted a mathematical modeling study using five independent methods to assess R 0 of COVID-19. Their results illustrated that R 0 dropped from 4.38 to 3.41 after the closure of Wuhan city. Over the entire epidemic period COVID-19 had a R 0 of 3.39. Moreover, Tang et al. formulated a deterministic compartmental model. Their estimations based on likelihood and model analysis showed that R 0 with control measures might be as high as 6.47 ( [5] ). Most recently, Chen et al. ( [11] ) developed a Bats-Hosts-Reservoir-People transmission network model to simulate the potential transmission from the infectious sources to human. The estimated values of R 0 were 2.30 from reservoir to person and 3.58 from person to person. We noticed that the estimations of R 0 in varied studies are different. As mentioned in [5, 12] , variability in the estimation of the basic reproduction number is a general recognized methodological issue, and standardized methods both for calculating and reporting R 0 are still missing. Furthermore, the value of R 0 may vary with key clinical parameters inferred from data which depend on the time period, quality, accuracy, and reliability. To better quantify the evolution of the interventions, Tang et al. fitted the previously proposed model in [5] to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ( [13] ). There are also some literatures focusing on the prediction of COVID-19 development trend. Wang et al. formulated a complex network model and analyzed the possible time node and All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.