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An Autoregressive Integrated Moving Average Model for Predicting Varicella Outbreaks - China, 2019
Miaomiao Wang1, Zhuojun Jiang2, Meiying You1
1Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing, China.
China CDC Weekly
|August 18, 2023
Summary
This study developed an autoregressive integrated moving average (ARIMA) model to predict varicella outbreaks in China. The model accurately forecasts future trends, aiding in prevention and control strategies for this escalating public health issue.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- Varicella (chickenpox) is a growing public health concern in China, particularly among children.
- Effective surveillance and early warning systems are crucial for mitigating and controlling varicella outbreaks.
- Predictive modeling offers a scientific basis for public health interventions.
Purpose of the Study:
- To develop and validate an autoregressive integrated moving average (ARIMA) model for predicting varicella outbreaks in China.
- To forecast monthly varicella cases for the year 2019 based on historical data.
Main Methods:
- Utilized monthly varicella outbreak data in China from 2005 to 2018 for ARIMA model development.
- Applied parameter and Ljung-Box tests to ensure statistical significance of models.
- Selected the optimal ARIMA model based on R-squared and Bayesian Information Criterion (BIC) values.
Main Results:
- The ARIMA (1, 1, 1)×(0, 1, 1)12 model was identified as the optimal predictive model.
- This model demonstrated a good fit for observed 2019 varicella cases, with an average relative error of 15.2%.
Conclusions:
- The developed ARIMA model effectively predicts future varicella outbreak trends in China.
- This predictive capability provides a scientific benchmark for enhancing varicella prevention and control strategies.
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