Analysis Of Factors Affecting Leprosy And Prediction Of Prevalence Rate

As a common infectious disease,leprosy has plagued the history of human beings for thousands of years.Because the clinical symptoms of patients are terrible,there are symptoms such as limb disability and drooping eyelids.Therefore,it has been regarded as a punishment for heaven since ancient times.This has brought about many social problems,such as the problem of social discrimination against leprosy patients.However,with the advancement of human science and technology and the development of medical standards,the treatment of leprosy has also made great progress.From the initial isolation treatment to the emergence of sulfone drugs in the later stage,leprosy has gradually become the plague and smallpox.One of the overcoming diseases,especially the emergence of a combination chemotherapy method in 1983,brought the dawn of rehabilitation for leprosy patients.Taking Liaoning Province as an example,the prevalence of leprosy has dropped from 14/100,000 in the peak period to 0.08/100,000 today,which is already in a low-popular trend.However,due to the current pathogenesis of leprosy,the development of vaccine still takes time,and the primary prevention measures are still lacking.Therefore,the prevention and treatment of leprosy still faces enormous challenges.At present,in the medical field,research on the factors affecting leprosy has not made great progress,and it is still a problem in the fields of medicine and pathology.This paper compares the influencing factors of leprosy in Liaoning Province and Yunnan Province in terms of natural environment and economic development,and the literature survey +discussion and verification method to sort out and explain some key factors.In the process of comparing the differences between the two provinces,the difference between temperature,humidity and precipitation is the most representative difference between the two provinces.In the subsequent literature research,there are also data indicating that these three factors will be related to M.leprae.Breeding has an impact and is positively correlated;secondly,differences in human factors such as the way people live in the two areas have also led to differences in leprosy epidemic trends.In the comparison of historical data of leprosy patients,it is found that patients have certain characteristics in terms of gender,disease type and heredity.In the fourth chapter,through the logistic regression analysis of existing patient data,we finally found three factors that have significant influence on leprosy,such as gender,temperature and precipitation,and also achieved very good in model and parameter test.Good results show that the three influencing factors are objective and reasonable,and provide some support for the study of the influencing factors of leprosy.How to rationally formulate the next stage of prevention and control strategies,and targeted prevention and control,this also puts new demands on medical institutions,and a series of studies in the field of disease prediction provide a good support for the solution of this problem.in accordance with.The purpose of disease prediction is to know in advance the epidemic trend of the next stage of the disease,provide early warning for the outbreak of the disease or rationally allocate the medical resources needed for the disease,neither causing waste nor meeting medical needs.At present,there are many researches on the field of disease prediction,and related models are also widely used,such as time series model,regression model,gray model,neural network model and so on.This paper analyzes the prevalence rate of leprosy in Liaoning Province from 1950 to 2017,and predicts the prevalence of leprosy in a future period through time series analysis,and strives to provide some support and help for predicting the prevention and treatment of leprosy.This paper is divided into two parts according to the forecast period,one is the annual forecast and the other is the monthly forecast.In the annual forecasting process,1966 was used as the time inflection point by the characteristics of the original sequence graph,and the model prediction was carried out in the whole period and in 1966-2017.After the smoothing process,the model setting and the parameter test,the final The annual prediction model with ARIMA(1,2,1)is determined.The ARIMA(1,2,1)model is not only better than other models in terms of fitting,but also more scientific in model interpretation,especially when compared with the full-time fitting method.The monthly forecast is ultimately selected using the ARIMA(1,2,2)model.First of all,under different parameters,the ARIMA(1,2,2)model parameters are more reliable and have a very good goodness of fit.Compared with the annual forecast,the monthly forecast is better in timeliness,and the monthly prevalence prediction can better grasp the future disease epidemic trend.The better the goodness of fit compared with the annual forecast also indicates the monthly The prediction better explains the meaning of the original sequence and is more realistic for the prevention and treatment of leprosy.In the end,we obtained the prediction of the annual prevalence of 2018-2020 and the forecast of each month of the first half of 2018 through the prediction of two time dimensions,and based on this,provided better support for the future leprosy prevention and treatment work in Liaoning Province.