Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model

Hao Xiong; Huili Yan

The modelling techniques that we used in this study are based on SEIR and system dynamics. And our model is parameterized with the latest official released data of 2019-nCoV. An additional strength of our study is that simulation of the baseline scenario is well fit the trajectory of the official data of epidemic spreading. Nonetheless, our study has several major limitations. First, we assumed that the transmission of 2019-nCoV mainly caused by exposed individuals (without symptoms) and the transmission ability of identified (without symptoms) is ignored. Second, our transmission model was somewhat sensitive to several key parameters: the starting date of epidemic spreading, the exposed (incubation) period and the identified (treatment) period. And our assumption regarding the exposed (incubation) period and the identified (treatment) period is keeping stable. If 2019-nCoV, similar to influenza, has strong seasonality in its transmission, our epidemic forecast might not be reliable.