Research On Key Parameter Estimation And Network Transmission Dynamics Of SARS-CoV-2

By February 2021,the global outbreak of SARS-CoV-2 had infected millions of people,significantly damaging the human well-being,lifestyle of people,and the global economy.However,the transmission mechanism of SARS-CoV-2 remains largely unclear.It’s urgent to build a corresponding epidemic model to predict its spreading trend,which is also beneficial for understanding the transmission mechanism of SARS-CoV-2 and controlling the epidemic effectively.Based on the SEIR model,we reconstructed the dynamics model of SARS-CoV-2 spreading.According to the characteristics of the SARS-CoV-2,we investigated the epidemic spread mechanism from both the single-node and network levels.Firstly,we established the node models to simulate the epidemic spreading of various provinces in China and some typical countries at the single-node level.Based on the models,we fit the epidemic data to obtain the key parameters of SARS-CoV-2 spreading.By comparing the key parameters of epidemic models in different regions,we found that the intensity of prevention,timeliness of prevention,and the rate of treatment are the key factors affecting the spread of the SARS-CoV-2,while the cure rate and mortality rate are mainly determined by the constitution of patients.Furthermore,we calculated the effective regeneration number of SARS-CoV-2 of each province in China and found that they all reduced to less than 1 within a short period,indicating that the effectiveness of prevention and control measures in China is robust.In addition,we obtained the key parameters of SARS-CoV-2 spreading for some representative countries by October 18,2020 and evaluated the effectiveness of epidemic prevention measures.The results show that the effectiveness of epidemic prevention measures in China is the strongest compared with other countries.Secondly,based on the node model,we introduced the scale-free network to establish the network model and further studied the spreading mechanism of SARS-CoV-2 in the network.We found that intrinsic model parameters such as the intensity of prevention,timeliness of prevention,and the rate of treatment have significant effects on the spread of SARS-CoV-2 in the network.Furthermore,we introduced the closure coefficient in the model to indicate the degree of communication between nodes in the network.Compared with intrinsic model parameters,we found that the closure coefficient is the most important factor affecting the spread of SARS-CoV-2 on the network.In addition,we found that the epidemic spreading of nodes with an intermediate degree is affected most by the change of intrinsic model parameters compared with nodes with too-small degree or too-large degree.In contrast,the closure coefficient has the greatest effect on the epidemic spreading of the nodes with a high degree when the closure coefficient increases,and when the closure coefficient decreases,the epidemic spreading of nodes with an intermediate degree is affected most.Overall,we explored the transmission mechanism of SARS-CoV-2 from the singlenode and network levels.Based on the computational model,we investigated the influence of intrinsic model parameters such as the epidemic intensity,timeliness of epidemic prevention,and the consultation rate on the transmission of SARS-CoV-2 at the single-node and network level.In particular,at the network level,we studied the effect of the closure coefficient on epidemic prevention,and the results suggested that reducing human contact is the most effective means of epidemic prevention.In conclusion,our research is useful to evaluate the effectiveness of interventions for epidemic prevention,thus providing guidance for selecting the most effective external interventions.