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

We present a Cybernetics-based Dynamic Infection Model to simulate the epidemiologic characteristics of SARS-COV-2. Two polymorphic models for different city types are derived with considerations on their medical, immigration/emigration population and administrative conditions. Shanghai, Beijing, Wenzhou and Wuhan were studied to validate the infection models. More simulated results based on the successive data from 37 cities in China are listed in the appendix. Results demonstrate that the model can successfully simulate the dynamic infecting process of COVID-19, and uncontrollable risks in a city can be predicted before they come true. Additionally, administrative management can be verified by the model so that decisions can be made in time with more confidence according to the forecasts and warnings. Combined with big data of the intercity traffics, the research work is even more help-ful to future epidemic predictions. Uncontrollable risks in multiple cities could be monitored in parallel, emergency responses could be activated faster, and medical supplies would be produced precisely and transported to the right place at the right time.