Modeling And Analysis Of Infectious Disease Transmission Dynamics Based On Genetic Algorithm

Nowadays,a various of infectious diseases have occured frequently.The infectious diseases widely spread not only be bad for human health,but also be threaten to the social stability,and the economy development will be prevented from a high speed.Therefore,it has a great practical significance to analyze and simulate the spread of infectious diseases and which provide effective preventive measures and recommendations.One of the important methods for studying infectious diseases is the transmission dynamics.Analyzing the epidemic characteristic and pathogenesis of infectious diseases.The mathematical model were constructed to describe the spread process of infectious diseases.By mathematical analysis of the model to predict the epidemic trend and put forward prevention recommendations.The research content of this paper focus on infectious diseases.Combining genetic algorithms and infectious disease analysis theory,to analyze the transmission dynamics of the Zika virus and Ebola virus.The main research works and results list as follows:(1)Transmission model of Zika virus on the network was proposed,and a new transmission mechanism was established based on two transmission routes which are sexual and mosquito transmission.A network transmission model was established based on it.And the basic regeneration number was derived that determine whether the virus would spread widely based on themodel.On account of that the parameters of model are not easy to estimate,an improved genetic algorithm based on adaptive gene site mutation was proposed to estimate the parameters in the Zika virus model.And R0=2.17 is calculated based on the estimation result.The experimental results show that the improved genetic algorithm could estimate all the parameters in the model,and compared with the traditional methods the experimental results are more closely to the actual infection data,the convergence speed is faster and higher accuracy.(2)A corresponding mathematical model is obtained based on the Ebola transmission mechanism in Sierra Leone.In this paper,we proposed an improved genetic algorithm to estimate the parameters of the Ebola virus model.The applicability of the genetic algorithm for the non-linear model is studied.The adaptive genetic mutation operator and the number of cross and mutations are added into the traditional genetic algorithm,the experiments have proved that the improved genetic algorithm converges faster than the traditional algorithm,furthermore it is feasible and effective in parameter estimation.Ebola’s basic regeneration number R0=1.895 was calculated.(3)An infectious disease transmission analysis system based on genetic algorithms was realized.The transmission coefficients for the two types of infectious diseases were estimated in this system,and the transmission time of the two types of infectious diseases were predicted.Simulating the dynamic propagation process of Zika virus on the network.