Data Analysis And Modeling Research On Sudden And Major Infectious Diseases

Sudden pandemics threaten human survival and social economic development.It is of great significance to understand its transmission law as early as possible and formulate feasible and effective prevention and control strategies in time.In this paper,statistical methods are used to analyze the reported data and to discover driving forces for an epidemic.In order to provide theoretical and quantitative basis for designing prevention and control measures,epidemiological models were constructed to simulate the dynamics of diseases transmission under different intervention measures based on the driving forces and actual environmental factors.Our study mainly analyzed the transmission and successful anti-epidemic strategies of the 2014 denguee outbreak in Guangdong Province and the 2019 COVID-19 outberak in Wuhan.Wokes below were done in this paper:1.For the first time,the qualified rate data of mosquito monitoring sites were embedded into dengue transmission model,and the Pearson correlation analysis was used to verify that it can replace the incomplete Breteau index data to reflect the overall mosquito density.A new SEAGR dengue model was proposed and simplified by transforming the extrinsic incubation period into a delay term.Based on the threshold of the effective reproduction number,the critical value of the qualified rate for dengue prevention and control was proposed.The results showed that in order to control the spread of dengue in 2014,the qualified rate should be higher than 0.4586(95%CI 0.4576-0.4596);79.97%(95%CI 78.98%-80.87%)of the infections were caused by indigenous asymptomatic infections;mosquito control and isolation measures effectively limited the transmission of indigenous and imported asymptomatic infections.2.Based on prevention and control measures and medical resources in Wuhan,a pulse COVID-19 model describing the change of diagnostic criteria was proposed for the first time,which solved the problem of data fitting caused by the sudden increase of reported cases.Then,approximate Bayesian estimation(ABC)algorithm and Markov chain Monte Carlo(MCMC)algorithm was combined to solve the unidentified parameters problem.Based on the model,the impacts of prevention and control measures and medical resources on the epidemic ware analyzed,and a stochastic simulation method for estimating the number of hospital beds in the early stage of the epidemic was proposed.The results showed that the number of infections was reduced about 80 times after seal off the city;the opening time of the shelter hospital was only three days later than the best time;our stochastic simulation method accurately estimated the number of beds needed in Wuhan.3.Considering the difference between mild and severe infections in the treatment,our study embedded mild and severe infections in the pulse COVID-19 model to explored the necessity of treating mild infections.Then,the change of the conversion rates of mild patients before and after the opening of the Fangcang shelter hospitals was estimated.Sensitivity analysis through the evaluation of Partial rank correlation coefficient(PRCC)suggests that the reduction of conversion rate is significant to reduce the death cases.The results of numerical simulations show that reducing the conversion rate has contributed to a 777(95% CI 729-829)reduction of death cases,and the reduction of deaths case is related to the time and extent of the reduction of conversion rate.