SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration

Pengpeng Shi; Shengli Cao; Peihua Feng

The epidemic data used in this paper comes from the raw epidemic notification data published on the official website of the National Health Commission of the people's Republic of China (http://www.nhc.gov.cn/). In this paper, the governing Eqs. 2 and 3 is solved using Euler's numerical method, and the integration step is 0.01 (days). The initial value of the dynamic system refers to the epidemic data officially released by the Chinese government on January 23, 2020, and some parameters have been reasonably estimated. The specific parameter values are shown in Table 1 . The other model parameters are the results of fitting optimization based on the original data.  ), the corresponding model parameters are obtained by fitting and optimization based on the raw data, and predict the epidemic situation in future. As can be seen from the Fig. 2 , the corresponding optimal model parameters are found with different infectious abilities. Under the optimal parameters, the number of infected persons predicted by the theoretical model is in good agreement with the raw data from January 23, 2020 to February 10, 2020. Next, we can analyze the effect of different infectious ability of patients in latent period on model estimation. Note that without considering the infectious capacity of patients in the incubation period, that is, =0  , the present model can degenerate to obtain the recent dynamic model of infectious disease transmission established in Ref. [8] . The results shown in Figure 2 show that the peak estimate of the number of infected people using the theory established in Ref. [8] is much higher than the estimate made by the present theoretical model. This is due to the neglect of the infectious ability of patient in latent period in previous models [6] [7] [8] . Because only the infected are transmissible, the actual probability of infection needs to be overestimated to adequately fit the raw data, which ultimately leads to overestimation of the number of infected people. Figure. 2.Effect of infectious ability of patient in latent period on the theoretical estimation. Fig.3 . Impact of variation of incubation period length on the theoretical estimation.