Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models
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 f...
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The Journal of Infection in Developing Countries
2022-08-01
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| Series: | Journal of Infection in Developing Countries |
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| Online Access: | https://jidc.org/index.php/journal/article/view/16475 |
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| author | Qi Chen Han Zhao Hongfang Qiu Qiyin Wang Dewei Zeng Mengliang Ye |
| author_facet | Qi Chen Han Zhao Hongfang Qiu Qiyin Wang Dewei Zeng Mengliang Ye |
| author_sort | Qi Chen |
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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.
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| format | Article |
| id | doaj-art-4e642db1dfa14bddb9160621e31134f6 |
| institution | OA Journals |
| issn | 1972-2680 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-4e642db1dfa14bddb9160621e31134f62025-08-20T02:14:20ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802022-08-01160810.3855/jidc.16475Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical modelsQi Chen0Han Zhao1Hongfang Qiu2Qiyin Wang3Dewei Zeng4Mengliang Ye5School of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.Chongqing Municipal Center for Disease Control and Prevention, Chongqing, ChinaSchool of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China.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.Nan’an district center for disease control and prevention, Chongqing, ChinaSchool of Public Health, Research Center for Medicine and Social Development and Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China. 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. https://jidc.org/index.php/journal/article/view/16475incidencerubellaforecastingSARIMABPNN |
| spellingShingle | Qi Chen Han Zhao Hongfang Qiu Qiyin Wang Dewei Zeng Mengliang Ye Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models Journal of Infection in Developing Countries incidence rubella forecasting SARIMA BPNN |
| title | Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models |
| title_full | Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models |
| title_fullStr | Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models |
| title_full_unstemmed | Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models |
| title_short | Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models |
| title_sort | time series analysis of rubella incidence in chongqing china using sarima and bpnn mathematical models |
| topic | incidence rubella forecasting SARIMA BPNN |
| url | https://jidc.org/index.php/journal/article/view/16475 |
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