An Autoregressive Integrated Moving Average Model for Predicting Varicella Outbreaks - China, 2019.

INTRODUCTION

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.

METHODS

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 (R2) 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.

RESULTS

Four models passed parameter (all P<0.05) and Ljung-Box tests (all P>0.05). ARIMA (1, 1, 1)×(0, 1, 1)12 was determined to be the optimal model based on its coefficient of determination R2 (0.271) and standardized BIC (14.970). Fitted values made by the ARIMA (1, 1, 1)×(0, 1, 1)12 model closely followed the values observed in 2019, the average relative error between the actual value and the predicted value is 15.2%.

CONCLUSION

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.