A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China.
Scrub typhus is a deadly infectious disease that is frequently underdiagnosed. Forecasting the emergence of infectious diseases using epidemiological models has emerged as a crucial instrument for comprehending the dynamics of their occurrence. This research aimed to investigate epidemic traits and...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0325905 |
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| author | Pei-Ying Peng Hui-Ying Duan Lei Xu Ji-Qin Sun Li-Juan Ma Ya Zu Ting-Liang Yan |
| author_facet | Pei-Ying Peng Hui-Ying Duan Lei Xu Ji-Qin Sun Li-Juan Ma Ya Zu Ting-Liang Yan |
| author_sort | Pei-Ying Peng |
| collection | DOAJ |
| description | Scrub typhus is a deadly infectious disease that is frequently underdiagnosed. Forecasting the emergence of infectious diseases using epidemiological models has emerged as a crucial instrument for comprehending the dynamics of their occurrence. This research aimed to investigate epidemic traits and create a predictive model for scrub typhus in mainland China, employing the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series method. Monthly records of scrub typhus cases were gathered from the China Center for Disease Control and Prevention, covering the timeframe from 2006 to 2019. From 2006 to 2018, a total of 142849 scrub typhus cases were reported in China, the females' morbidity was higher than the males' one (P < 0.001). The ideal model was SARIMA (1, 0, 2) (1, 1, 1) 12 with its residual being white noise (P > 0.05). This method forecasted scrub typhus cases between January and December 2019, with the predicted values for 2019 falling within the 95% confidence range. The research indicates that the SARIMA model accurately simulated the epidemiological patterns of scrub typhus across mainland China. Utilizing the SARIMA model is a practical approach for tracking scrub typhus cases in mainland China. |
| format | Article |
| id | doaj-art-a4a2d8f2aca54c24a3b73679b3c23307 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a4a2d8f2aca54c24a3b73679b3c233072025-08-20T03:30:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032590510.1371/journal.pone.0325905A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China.Pei-Ying PengHui-Ying DuanLei XuJi-Qin SunLi-Juan MaYa ZuTing-Liang YanScrub typhus is a deadly infectious disease that is frequently underdiagnosed. Forecasting the emergence of infectious diseases using epidemiological models has emerged as a crucial instrument for comprehending the dynamics of their occurrence. This research aimed to investigate epidemic traits and create a predictive model for scrub typhus in mainland China, employing the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series method. Monthly records of scrub typhus cases were gathered from the China Center for Disease Control and Prevention, covering the timeframe from 2006 to 2019. From 2006 to 2018, a total of 142849 scrub typhus cases were reported in China, the females' morbidity was higher than the males' one (P < 0.001). The ideal model was SARIMA (1, 0, 2) (1, 1, 1) 12 with its residual being white noise (P > 0.05). This method forecasted scrub typhus cases between January and December 2019, with the predicted values for 2019 falling within the 95% confidence range. The research indicates that the SARIMA model accurately simulated the epidemiological patterns of scrub typhus across mainland China. Utilizing the SARIMA model is a practical approach for tracking scrub typhus cases in mainland China.https://doi.org/10.1371/journal.pone.0325905 |
| spellingShingle | Pei-Ying Peng Hui-Ying Duan Lei Xu Ji-Qin Sun Li-Juan Ma Ya Zu Ting-Liang Yan A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. PLoS ONE |
| title | A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. |
| title_full | A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. |
| title_fullStr | A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. |
| title_full_unstemmed | A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. |
| title_short | A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. |
| title_sort | seasonal autoregressive integrated moving average sarima forecasting model to predict the epidemic trends of scrub typhus in china |
| url | https://doi.org/10.1371/journal.pone.0325905 |
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