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

Abstract

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 (R 2 ) 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 R 2 (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.Varicella, or chickenpox, is a prevalent childhood disease resulting from varicella-zoster virus infection. As the third most reported vaccine-preventable infectious disease in China, varicella imposes a substantial socio-economic burden (1). The disease is

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Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

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