(Epidemiological Analysis of Varicella in Dalian from 2009 to 2019 and Application of Three Kinds of Model in Prediction Prevalence of Varicella）Study Design: Cross Sectional Study

Abstract Objective：This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend.Methods: In this study, the epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella incidence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM(1,1), made GM(1,1)-Markov prediction. Results: This study collected 37223 cases from 2009 to 2019. The average annual incidence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.167:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.6425, the relative error would be 14.42%, the Markov prediction result would be 56.2075, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.508. The relative error would be 5.06%.Conclusions: Varicella data had its unique development characteristics. The accuracy of GM (1,1) - Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.