The Corona Virus Disease 2019 (COVID-19) epidemic is a sudden public health crisis, known as an "International Emergency of Public Health Event". This study uses the bottom-up characteristics of multi-agents to construct multi-agent simulation models for COVID-19 prevention and control. The development trend of the epidemic situation under the condition that the government adopts different prevention and control measures is studied, and on this basis, the influence of temperature on the spread of the virus is discussed. The simulation results show that the multi-agent modeling method can effectively capture the emergence of complex systems. The evaluation of the effects of single measures and multiple interventions will help determine key prevention and control strategies and provide important experience and scientific basis for future epidemic prevention and control.

The enhanced virulence and infectiousness of the Omicron variant of SARS-CoV-2 is having more significant impacts on certain socioeconomic areas, and rapidly suppressing the spread of the epidemic remains a priority for maintaining public health security throughout the world. Thus, we applied multi-agent modeling theory to create a social simulation model of Omicron variant transmission and prevention and control in order to analyze the virus transmission status in complex urban systems and its changing trends under different interventions. By considering the six municipal districts under the jurisdiction of Taiyuan City as examples, we developed state transition rules between five types of resident agents, mobility and contact behavior rules, and rules for patient admission behavior by hospital agents. We then conducted multi-scenario simulation experiments based on single measures of pharmacological and non-pharmacological interventions under non-governmental control as well as multiple interventions in combination to evaluate the effects of different measures on rapidly suppressing the spread of the epidemic. The experimental results demonstrated the utility of the model and the multi-agent modeling method effectively analyzed the transmission trends for the Omicron variant, thereby allowing a comprehensive diagnosis of the future urban epidemic situation and providing an important scientific basis for exploring more accurate normalized prevention and control measures.

The rapid suppression of SARS-CoV-2 transmission remains a priority for maintaining public health security throughout the world, and the agile adjustment of government prevention and control strategies according to the spread of the epidemic is crucial for controlling the spread of the epidemic. Thus, in this study, a multi-agent modeling approach was developed for constructing an assessment model for the rapid suppression of SARS-CoV-2 transmission under government control. Different from previous mathematical models, this model combines computer technology and geographic information system to abstract human beings in different states into micro-agents with self-control and independent decision-making ability; defines the rules of agent behavior and interaction; and describes the mobility, heterogeneity, contact behavior patterns, and dynamic interactive feedback mechanism of space environment. The real geospatial and social environment in Taiyuan was considered as a case study. In the implemented model, the government agent could adjust the response level and prevention and control policies for major public health emergencies in real time according to the development of the epidemic, and different intervention strategies were provided to improve disease control methods in the simulation experiment. The simulation results demonstrate that the proposed model is widely applicable, and it can not only judge the effectiveness of intervention measures in time but also analyze the virus transmission status in complex urban systems and its change trend under different intervention measures, thereby providing scientific guidance to support urban public health safety.

Multi-Agent System (MAS) isan importantbranch of artificial intelligence research.This study usesthe bottom-up characteristics of multi-agents to construct multi-agent simulation models for CoronaVirus Disease 2019 (COVID-19) virus prevention and control basedon different age groups. The developmentofthe epidemic and the infection of residents ofall ages under different prevention and control measuresissued by the governmentwere studied. The simulation results proved that the multi-agent modeling method could effectively capture the emergence of complex systems. Its experimental conclusions provided a basis for predicting the developmentof the epidemic andprovidescientific supportfor governmentdecision-making.