The Impact Of The Average Shortest Path Length Distribution On The Spread Of COVID-19

Since ancient times,infectious diseases have been threatening human life and health,and every occurrence has become a huge disaster of human society.Human research on infectious diseases has never stopped.With the development and improvement of complex network theory,researchers combine infectious diseases with complex network theory to study the transmission process and law of infectious diseases.In December 2019,COVID-19 broke out in Wuhan,Hubei province.The virus quickly spread to all parts of the country and the world.It is listed as a global epidemic by the World Health Organization(WHO).In this paper,we study the factors that influence the spread of COVID-19 by establishing complex networks,and establish complex networks with the adjacency relationship between China's provincial level administrative regions and cities.According to the laws of travel and migration of contemporary people,we propose the concept of the shortest path length distribution of complex networks.Objective to study the effect of the shortest path length distribution on the spread of COVID-19 in complex networks.The distribution law of the average shortest path length of nodes in complex networks is analyzed by the single sample K-S test(Kolmogprov-Smirnov test).The results show that the average shortest path length of nodes in the network established by 26 of the 29 provincial administrative regions follows the normal distribution.This article selects three COVID-19 transmission indicators in each provincial administrative region,the time of COVID-19 from outbreak to peak number of infections,the time when the number of infected people decreased from peak to relatively stable and the proportion of the highest number of people infected.Apriori algorithm was used to analyze the correlation between normal distribution of the mean,the standard deviation sigma and COVID-19 transmission index in different provinces,obtained association rules between factors.After normalizing the related factor data,support vector regression model is used to fit the related factors.Through research and analysis,as a result,the mean value of normal parameter was positively related to the time of COVID-19 from outbreak to peak number of infections.It was negatively correlated with the time when the number of infected people decreased from peak to relatively stable.The standard deviation sigma was positively related to the time of COVID-19 from outbreak to peak number of infections.This conclusion has important reference significance for the prediction of the development trend of COVID-19 and the formulation of epidemic prevention and control measures.