An Autoregressive Integrated Moving Average Model for Predicting Varicella Outbreaks — China, 2019
Miaomiao Wang, Zhuojun Jiang, Meiying You, Tianqi Wang, Li Ma, Xudong Li, Yuehua Hu, Dapeng Yin
Abstract
<abstract abstract-type="Abstract"> <sec> <b>Introduction</b> Varicella, a prevalent respiratory infection among children, has become an escalating public health issue in China. The potential to considerably mitigate and control these outbreaks lies in surveillance-based early warning systems. This research employed an autoregressive integrated moving average (ARIMA) model with the objective of predicting future varicella outbreaks in the country. </sec><sec> <b>Methods</b> An ARIMA model was developed and fine-tuned using historical data on the monthly instances of varicella outbreaks reported in China from 2005 to 2018. To determine statistically significant models, parameter and Ljung-Box tests were employed. The coefficients of determination (R<sup>2</sup>) and the normalized Bayesian Information Criterion (BIC) were compared to selecting an optimal model. This chosen model was subsequently utilized to forecast varicella outbreak cases for the year 2019. </sec><sec> <b>Results</b> Four models passed parameter (all <i>P</i><0.05) and Ljung-Box tests (all <i>P</i>>0.05). ARIMA (1, 1, 1)×(0, 1, 1)<sub>12</sub> was determined to be the optimal model based on its coefficient of determination R<sup>2</sup> (0.271) and standardized BIC (14.970). Fitted values made by the ARIMA (1, 1, 1)×(0, 1, 1)<sub>12</sub> model closely followed the values observed in 2019, the average relative error between the actual value and the predicted value is 15.2%. </sec><sec> <b>Conclusion</b> The ARIMA model can be employed to predict impending trends in varicella outbreaks. This serves to offer a scientific benchmark for strategies concerning varicella prevention and control. </sec>