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

The medical regulation functions as a negative channel to control the incremental numbers of infection in the main loop. The basic model in Fig. 1 is based on a theoretical assumption, that all the patients are isolated at the moment of symptom onset (after the incubation period). When people start to wear masks and voluntarily stay indoors, R 0 (namely C 0 ) decreases accordingly. In the responsive condition, when a patient is confirmed, certain close contacts that are still in the asymptomatic incubation period would also be isolated. We introduce C 1 , named the contact tracing rate, to reflect this effect. When C 1 ≈ 0, that means no contact tracing is imposed, the system could easily fall into an instable state if R 0 ≥ 1.0. The infection model is discretized with a cyclic period of one day. In summary, T 0 and T 1 are mostly determined by the epidemic, C 0 is determined by both epidemic and the social stress, and C 1 is determined majorly by administrative activities. Eq. 2 gives the difference equations of the basic CDIM.