A Cybernetics-based Dynamic Infection Model for Analyzing SARS-COV-2 Infection Stability and Predicting Uncontrollable Risks

Wenlei Xiao; Qiang Liu; J Huan; Pengpeng Sun; Liuquan Wang; Chenxin Zang; Sanying Zhu; Liansheng Gao

2. The models are usually written as ordinary differential equations(ODE). It is not concise enough to help human comprehensively model the nonlinear dynamics in detail. 3. The models are relatively too simple to introduce the time-variant and probabilistic variables into the models. 4. All the parameters should be identified for estimation. In the early stage, the shortage of data makes the models infeasible to exactly forecast the trend of spread. 5. Emergency responses and medical supplies are not considered in the model, so the prediction of status has no direct recommending value to the administrative activities. 6. The parameter identification in the models strongly relies on the fidelity of real data. When the reported data have a severe deviation (like in Wuhan), it will easily lead to a forecasting failure.