Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model
Abstract Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-023-08969-4 |
| _version_ | 1797276696737480704 |
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| author | Zhende Wang Yongbin Wang Shengkui Zhang Suzhen Wang Zhen Xu ZiJian Feng |
| author_facet | Zhende Wang Yongbin Wang Shengkui Zhang Suzhen Wang Zhen Xu ZiJian Feng |
| author_sort | Zhende Wang |
| collection | DOAJ |
| description | Abstract Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models. Results The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. Conclusions The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease. |
| first_indexed | 2024-03-07T15:32:55Z |
| format | Article |
| id | doaj.art-a686dab7ebf34200a368af90812f4621 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-03-07T15:32:55Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-a686dab7ebf34200a368af90812f46212024-03-05T16:21:23ZengBMCBMC Infectious Diseases1471-23342024-01-0124111510.1186/s12879-023-08969-4Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series modelZhende Wang0Yongbin Wang1Shengkui Zhang2Suzhen Wang3Zhen Xu4ZiJian Feng5School of Public Health, Weifang Medical UniversitySchool of Public Health, Xinxiang Medical UniversitySchool of Basic Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical CollegeZibo Hospital of Shandong Health GroupChinese Center for Disease Control and PreventionChinese Preventive Medicine AssociationAbstract Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models. Results The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. Conclusions The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.https://doi.org/10.1186/s12879-023-08969-4GonorrheaModelingSARIMALSTM |
| spellingShingle | Zhende Wang Yongbin Wang Shengkui Zhang Suzhen Wang Zhen Xu ZiJian Feng Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model BMC Infectious Diseases Gonorrhea Modeling SARIMA LSTM |
| title | Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model |
| title_full | Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model |
| title_fullStr | Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model |
| title_full_unstemmed | Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model |
| title_short | Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model |
| title_sort | trend analysis and prediction of gonorrhea in mainland china based on a hybrid time series model |
| topic | Gonorrhea Modeling SARIMA LSTM |
| url | https://doi.org/10.1186/s12879-023-08969-4 |
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