Data-driven Modeling And Analysis On The Transmission Dynamics Of SARS-CoV-2

According to the transmission mechanism of infectious diseases,the dynamics of infectious diseases has established a differential equation model to analyze and study the transmission trend and control measures of infectious diseases.Data-driven research on infectious disease dynamics,based on dynamics models,combined with actual epidemic data,quantitatively analyzes the key factors affecting the spread of diseases and predicts the development trend of diseases.By establishing a data-driven dynamic model of the new coronary pneumonia,this paper explores the impact of a series of prevention and control measures on epidemic control,mainly including the following contents.For the dynamic modeling and analysis of"immune escape"and"dynamic Zero-COVID policy",firstly,based on the prevention and control mechanism of COVID-19in Changchun,an improved SEIR model was established and the basic reproduction number（₀R）was calculated;Secondly,according to the strategy of"dynamic Zero-COVID policy"at different stages,the parameter values are determined in stages,and the key parameters such as the contact infection rate and the social detection rate at the initial stage of the epidemic are further estimated by using the least square method.Finally,through numerical simulation,it is found that although the improvement of the immune escape ability of COVID-19 mutant strains has reduced the effectiveness of vaccines,mass vaccination is still the key prevention and control measure to ensure people’s life safety to the greatest extent.The social detection rate at the initial stage of the epidemic determines the size of the"iceberg"of the epidemic.The more obvious the"iceberg"phenomenon is,the more difficult the prevention and control is.The"dynamic Zero-COVID policy"aims to detect,isolate and transfer infected cases as soon as possible,reduce the impact of the"iceberg"phenomenon.This policy has played an important role in curbing the Omicron epidemic.（2）Dynamic modeling and analysis of the impact of"social distance control"on the spread of COVID-19.First,according to the characteristics of Delta epidemic prevention and control in Nanjing,SEIAHR model including birth and death of population was established;the basic regeneration numberR₀ is calculated by the next generation regeneration matrix method,whenR₀≤1,the disease-free equilibrium is globally asymptotically stable,and the disease disappears,while whenR₀>1,the positive equilibrium point is globally asymptotically stable,and the disease eventually develops into an endemic disease.Finally,the key parameters such as the contact infection rate are estimated to simulate the impact of social restrictions on the development of the epidemic.In view of China’s huge population base,appropriate social restrictions can effectively prevent the rapid increase of the number of infected people in a short time,reduce the burden on the medical system.At the same time,maintain social restrictions for a period of time,win time for the establishment of immune barriers,and further prevent local epidemics from escalating into large-scale epidemics.（3）Aiming at the dynamic modeling and analysis of the occult transmission of the Omicron epidemic.First,according to the characteristics of the epidemic prevention and control in Jilin City,a dynamic model considering the latent infectivity was established.Then,the relevant parameters were taken in stages and the key parameters such as the contact infection rate of symptomatic infected persons were estimated.Finally,the numerical simulation shows that the characteristics of Omicron virus strain such as short incubation period and strong infectivity lead to the difficulty of early detection and early control,and the formation of occult transmission in the society.Once the duration of incubation infection and the infection rate of asymptomatic patients exceed the actual level,the epidemic scale will be unimaginable.With the constant variation of the virus,the characteristics of occult transmission are significant.On the one hand,vaccination should be strengthened,in particular,the establishment of immune barriers for vulnerable groups such as the elderly;on the other hand,it is necessary to deploy sufficient medical resources to reduce the impact of COVID-19 in the future.Data-driven research on infectious disease dynamics can more truly reflect the epidemic law of the disease.The research results of this paper can help the prevention and control departments to develop more practical and effective epidemic prevention and control strategies.