Study On The Control Measures Of COVID-19 Epidemic On Campus Based On Travel Simulation

When COVID-19 pandemic spread across the globe,many countries responded to varying degrees,such as social distancing restrictions and quarantines.However,the economic losses and physical and mental health effects of prolonged and widespread quarantine on people in infected areas have become increasingly severe.In order to curb the spread of the epidemic as early as possible,many studies have focused on predicting the trend of the epidemic development and using it as a basis to help formulate epidemic prevention policies,but such models are mostly homogeneous and cannot reflect the individual heterogeneity in the process of population travel.In addition,there is still room for improvement in many epidemic control measures to better control the development of the epidemic.The main work of this paper includes:First of all,a travel simulation platform is built to simulate heterogeneous individual travel and the spread of COVID-19 in the epidemic area.The activity-based individual travel model is used to heterogeneously generate daily travel schedules for different individuals based on travel demand,the small-world network stochastic model is used to generate heterogeneous information on the number of individual proximity contacts for different types of activity sites,and COVID-19 spread model is combined with the individual travel model to generate heterogeneous individual travel chains with COVID-19 spread information.Based on the above models,we propose a simulation algorithm to make the whole simulation cycle run cyclically.Secondly,this study verifies the rationality of the platform function and conducts travel simulation with Omicron and Delta as the propagation strain respectively.The reasonableness of the human flow in different activity sites was verified by counting the human flow in different hours of a single day and comparing it with the capacity of the activity sites.By counting the travel data of individuals embedded with information on the transfer of COVID-19 status,we verified that the platform can generate real-time changes in infection status,mask wearing status,and heterogeneity of sweeping codes in and out of venues.By counting the number of new confirmed cases per day verifies that considering latent infectiousness in the simulation yields an epidemic transmission trend that is more consistent with reality.The accuracy validation metric verifies that the simulated epidemic trends of Omicron and Delta strains are similar to the realistic epidemic trends.The simulation platform built in this study can simulate realistic individual travel and epidemic transmission,and provide an effective tool for the optimization of control measures.Finally,this study proposes an optimization algorithm for COVID-19 epidemic control measures,taking nucleic acid detection measures as an example,this study proposes to optimize the existing control measures with grouping as the decisionmaking variable,funding as the constraint,and taking the epidemic transmission speed and infection scale as the optimization goal,and obtain the optimization effect of reducing the epidemic transmission speed by 16.40% and the epidemic infection scale by 15.41%.In addition,by applying the optimization algorithm to the sweeping tracking control measures and obtaining the optimization effect of 51.00% reduction in the epidemic transmission speed and 53.11% reduction in the epidemic transmission scale.Later,the number of new confirmed cases per day,the cumulative number of confirmed cases and the number of intergenerational transmissions were used to measure the epidemic prevention effect after combining nucleic acid testing and scanning code registration control measures,which proved that the epidemic prevention effect of implementing combined optimization measures was better than implementing single optimization measures.