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

Recently, control engineering has been maturely applied in industry. It has successfully proved its ability in analyzing the complex mechanism in a physical system. However, the control engineering has seldom been used in modeling the transmission pattern of the epidemic. Comparing its basic mechanism with an instable system, we found the dynamic infection process of COVID-19 in a city could be depicted as a cybernetic model with a positive feedback and multiple delays, and the system instability is the most issue that people and the government concern. In contrast to other classic transmission models like SIR/-SEIR (the susceptible, [exposed] , infectious and recovered model), which are mostly described by a set of ordinary differential equations [2] , the feedback system is constituted by a chain of discrete function blocks, such as proportional, time delayed, integral(or accumulative) blocks, and positive/negative feedback or feed-forward loop, etc. Dynamic manipulations, such as quarantining activities, medical supplies, etc., are also considered in the model. In those consideration, the model is named as Cybernetics-based Dynamic Infection Model (CDIM).