An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Tang, Biao; Bragazzi, Nicola Luigi; Li, Qian; Tang, Sanyi; Xiao, Yanni; Wu, Jianhong

There are three further models incorporating data from international travels: the models of Imai and coauthors (Imai et al., 2020) , of Kucharski et al. (Kucharski et al., 2020) and of Wu and collaborators (Wu, Leung, & Leung, 2020) . In particular, Imai and coworkers (Imai et al., 2020 ) estimated a reproduction number of 2.6 (uncertainty range: 1.5e3.5). Depending on the different scenarios and levels of zoonotic exposure, the reproduction number was found to vary from 1.7 to 2.6 to 1.9e4.2. Depending on the different estimates of generation time, the reproduction number oscillated from 1.3 to 2.7 to 1.7e4.3. Based Fig. 2 . Predictions and effect of control measures on infection based on assumption that parameters obtained from fitting the data from January 23rd to January 29th , 2020 (and hence the interventions) remain unchanged. (AeB) Decreasing the minimum contact rate after January 29th , 2020; (CeD) Decreasing/increasing the susceptible population size as of January 29th , 2020. on the level of infectiousness, the reproduction number varied in the range of 1.6e2.9. Finally, assuming that the novel virus would cause more mild-to-moderate cases than the SARS virus, the reproduction number would be 2.0 (uncertainty 1.4e2.3). Moreover, authors found that only public health interventions blocking over 60% of transmission would be really effective in controlling and containing the coronavirus outbreak. Partially based on the findings of Imai and coworkers (Imai et al., 2020) and building on a SIR model, Yu (Yu, 2020) has computed a basic reproduction number of 3.5 and has estimated that only a quarantine rate of infectious population higher than 90% would enable to effectively control the coronavirus outbreak. Kucharski and colleagues (Kucharski et al., 2020) designed a stochastic SEIR model, based on the Euler-Maruyama algorithm with a 6-h time-step and with the transmission rate following a geometric Brownian motion. Time-varying reproduction number was estimated using the sequential Monte-Carlo approach. Authors utilized several datasets to overcome the issue of unreliability of some data sources and to provide real-time estimates, relying on the Poisson probability calculation. Transmission was modeled as a random process, fluctuating and varying over the time. Similar to the model of Imai and coworkers (Imai et al., 2020) , the risk of transmission and the risk of causing a large outbreak were modeled based on a negative binomial offspring distribution, with incubation and infectious period being Erlang distributed. Median reproduction number was found to oscillate between 1.6 and 2.9 before the introduction and implementation of travel restriction. The study by Wu and collaborators (Wu et al., 2020) , based on nowcasting and forecasting approach, estimated a basic reproductive number of 2.68 (95% credible interval or CrI 2.47e2.86) with 75,815 individuals (95% CrI 37,304e130,330) being infected in Wuhan as of January 25th , 2020. The epidemics doubling time was found to be 6.4 days (95% CrI 5.8e7.1). The dynamics transmission model by Shen and coworkers (Shen, Peng, Xiao, & Zhang, 2020) predicted 8042 (95% CI 4199e11,884) infections and 898 (95% CI 368e1429) deaths, with a fatality rate of 11.02% (95% CI 9.26e12.78%). Authors computed a basic reproduction number of 4.71 (95% CI 4.50e4.92), which decreased to 2.08 (95% CI 1.99e2.18) on January 22nd , 2020. Based on these estimates, the pandemics outbreak is expected to significan