Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella

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 For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence 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 37,223 cases from China Information System for Disease Control and Prevention's “Disease Prevention and Control Information System” and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence 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 1.17:1. 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 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. 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.

Impact of implementing a free varicella vaccination policy on incidence in Wuxi City, China: an interrupted time series analysis

“…Our findings demonstrated a decrease in varicella incidence from January to March and from June to August, which aligns with previous epidemiological studies conducted in various regions of China, including Shandong Province, Dalian City, Guangzhou City, and Hangzhou City [24][25][26][27]. Cheng et al and Lu et al suggested that this period corresponds to the winter and summer vacations of Chinese students [25,26].…”

“…Our findings demonstrated a decrease in varicella incidence from January to March and from June to August, which aligns with previous epidemiological studies conducted in various regions of China, including Shandong Province, Dalian City, Guangzhou City, and Hangzhou City [ 24 – 27 ]. Cheng et al and Lu et al suggested that this period corresponds to the winter and summer vacations of Chinese students [ 25 , 26 ]. The occurrence of summer and winter holidays in many schools across China during this timeframe led to decreased interactions among children, thereby reducing the likelihood of varicella transmission within educational environments where the virus can easily spread.…”

Association between emphysema and other pulmonary computed tomography patterns in primary varicella pneumonia: A retrospective cohort study

“…As a serious complication of chickenpox, primary varicella pneumonia is estimated to occur in 1 out of 400 infections. [4] As a highly contagious disease, many body systems can be affected such as the lungs, skin, and central nervous system. Smoking can lead to decrease the activation of alveolar macrophages; it will increase the susceptibility to varicella pneumonia.…”

“…As a serious complication of chickenpox, primary varicella pneumonia is estimated to occur in 1 out of 400 infections. [ 4 ]…”

Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence

“…[3][4][5] Outbreaks in China are prevalent in primary school students. [6,7] Varicella remains inactive in the nervous system and may later erupt in adults causing more serious consequences in the form of "shingles" (herpes zoster). [8] Varicella infections are therefore a burden on health authorities' resources.…”