Estimation of the epidemic properties of the 2019 novel coronavirus: A mathematical modeling study

Jinghua Li; Yijing Wang; Stuart Gilmour; Mengying Wang; Daisuke Yoneoka; Ying Wang; Xinyi You; Jing Gu; Chun Hao; Liping Peng; Zhicheng Du; Dong Roman Xu; Yuantao Hao

• Exponential growth (EG), which assumes an exponential growth curve to the virus and estimates the basic reproduction number from the Lotka-Euler equation 14 • Maximum likelihood method (ML), in which the likelihood of the cases is expressed directly in terms of " on the assumption of a simple SIR model structure 15 • Sequential Bayesian Method (SB), in which the posterior probability distribution of the basic reproduction number is estimated sequentially using the posterior at the previous time point as the new prior 16 • Time-dependent reproduction numbers (TD), in which the basic reproduction number at any time point is estimated as an average of accumulated estimates at previous time points 17 These methods were implemented using the R0 package in R. 18 All these models require no assumption about recovery time, but in some cases require an assumption about the generation time of the epidemic. All methods were applied to the data for the whole period (January 10 th to February 8 th ), to the period only before the closure (January 10 th to January 23 rd ), and to only the period after the closure of Wuhan city (January 23 rd to February 8 th )