Effective Analysis and Investigation in COVID-19 Prevention Policies Based on Population Dynamics Modeling and Simulation

The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.

A Feedback SAIR Model for the Spread of Infectious Disease with Application to COVID-19 Pandemic

The COVID-19 global pandemic has highlighted the importance of identifying effective ways to control the spread of an infectious disease in a population. A solid understanding of the dynamics and the underlying mechanisms that govern this spread is an important step toward such a goal. Susceptible-Asymptomatic-Infected-Recovered (SAIR) models and their variants have played an important role in providing such insight. However, these models have limited explanatory and predictive power due to policy and behavior changes over time. In this paper we introduce a feedback version of the SAIR model by introducing feedback in the disease transmission rate. We apply this model to publicly available COVID-19 infection data. We show this model better captures the dynamics of the disease spread and has much better explanatory and predictive power. Our analysis suggests that public health policies based on daily infection numbers can be more effective than policies based on estimations of infection levels. © 2022 American Automatic Control Council.