Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models

  • Qi Chen School of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.
  • Han Zhao Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
  • Hongfang Qiu School of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.
  • Qiyin Wang School of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.
  • Dewei Zeng Nan’an district center for disease control and prevention, Chongqing, China
  • Mengliang Ye School of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.
Keywords: incidence, rubella, forecasting, SARIMA, BPNN

Abstract

Introduction: Chongqing is among the areas with the highest rubella incidence rates in China. This study aimed to analyze the temporal distribution characteristics of rubella and establish a forecasting model in Chongqing, which could provide a tool for decision-making in the early warning system for the health sector.

Methodology: The rubella monthly incidence data from 2004 to 2019 were obtained from the Chongqing Center of Disease and Control. The incidence from 2004 to June 2019 was fitted using the seasonal autoregressive integrated moving average (SARIMA) model and the back-propagation neural network (BPNN) model, and the data from July to December 2019 was used for validation.

Results: A total of 30,083 rubella cases were reported in this study, with a significantly higher average annual incidence before the nationwide introduction of rubella-containing vaccine (RCV). The peak of rubella notification was from April to June annually. Both SARIMA and BPNN models were capable of predicting the expected incidence of rubella. However, the linear SARIMA model fits and predicts better than the nonlinear BPNN model.

Conclusions: Based on the results, rubella incidence in Chongqing has an obvious seasonal trend, and SARIMA (2,1,1) × (1,1,1) 12 model can predict the incidence of rubella well. The SARIMA model is a feasible tool for producing reliable rubella forecasts in Chongqing.

Downloads

Download data is not yet available.
Published
2022-08-30
How to Cite
1.
Chen Q, Zhao H, Qiu H, Wang Q, Zeng D, Ye M (2022) Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models. J Infect Dev Ctries 16:1343-1350. doi: 10.3855/jidc.16475
Section
Original Articles