Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis
Introduction: This study proposes a combined Seasonal Autoregressive Integrated Moving Average and Long Short-Term Memory (SARIMA-LSTM) model to enhance the accuracy of evaluating the effectiveness of visceral leishmaniasis prevention and control efforts in Yangquan, China. Methodology: Data were...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Journal of Infection in Developing Countries
2025-07-01
|
| Series: | Journal of Infection in Developing Countries |
| Subjects: | |
| Online Access: | https://www.jidc.org/index.php/journal/article/view/20739 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849232761409241088 |
|---|---|
| author | Mengchen Han Chongqi Hao Zhiyang Zhao Peijun Zhang Bin Wu Lixia Qiu |
| author_facet | Mengchen Han Chongqi Hao Zhiyang Zhao Peijun Zhang Bin Wu Lixia Qiu |
| author_sort | Mengchen Han |
| collection | DOAJ |
| description |
Introduction: This study proposes a combined Seasonal Autoregressive Integrated Moving Average and Long Short-Term Memory (SARIMA-LSTM) model to enhance the accuracy of evaluating the effectiveness of visceral leishmaniasis prevention and control efforts in Yangquan, China.
Methodology: Data were obtained from the Yangquan Centre for Disease Control and Prevention. The hybrid model integrates a SARIMA component with a residual-based LSTM neural network.
Results: In the SARIMA-LSTM model, the LSTM component included seven hidden layer nodes, a learning rate of 0.001, 500 training epochs, a batch size of 256, and utilized the Adam optimization algorithm. The SARIMA-LSTM model demonstrated superior performance (MSE = 2.824, MAE = 1.279, RMSE = 1.681). A paired samples t-test revealed a statistically significant difference between predicted and actual case counts (t = -4.058, p < 0.001), indicating that the actual number of cases was lower than predicted.
Conclusions: The combined SARIMA-LSTM model outperformed the individual SARIMA and LSTM models, suggesting that the implemented interventions were generally effective.
|
| format | Article |
| id | doaj-art-5bed532d4b2843bfb9e4963e3f256a8b |
| institution | Kabale University |
| issn | 1972-2680 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-5bed532d4b2843bfb9e4963e3f256a8b2025-08-21T00:26:06ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802025-07-01190710.3855/jidc.20739Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral LeishmaniasisMengchen Han0Chongqi Hao1Zhiyang Zhao2Peijun Zhang3Bin Wu4Lixia Qiu5School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, ChinaYangquan Centre for Disease Control and Prevention, Yangquan, Shanxi, ChinaYangquan Centre for Disease Control and Prevention, Yangquan, Shanxi, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China Introduction: This study proposes a combined Seasonal Autoregressive Integrated Moving Average and Long Short-Term Memory (SARIMA-LSTM) model to enhance the accuracy of evaluating the effectiveness of visceral leishmaniasis prevention and control efforts in Yangquan, China. Methodology: Data were obtained from the Yangquan Centre for Disease Control and Prevention. The hybrid model integrates a SARIMA component with a residual-based LSTM neural network. Results: In the SARIMA-LSTM model, the LSTM component included seven hidden layer nodes, a learning rate of 0.001, 500 training epochs, a batch size of 256, and utilized the Adam optimization algorithm. The SARIMA-LSTM model demonstrated superior performance (MSE = 2.824, MAE = 1.279, RMSE = 1.681). A paired samples t-test revealed a statistically significant difference between predicted and actual case counts (t = -4.058, p < 0.001), indicating that the actual number of cases was lower than predicted. Conclusions: The combined SARIMA-LSTM model outperformed the individual SARIMA and LSTM models, suggesting that the implemented interventions were generally effective. https://www.jidc.org/index.php/journal/article/view/20739Visceral leishmaniasisSARIMA-LSTM modeleffectiveness evaluationYangquan |
| spellingShingle | Mengchen Han Chongqi Hao Zhiyang Zhao Peijun Zhang Bin Wu Lixia Qiu Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis Journal of Infection in Developing Countries Visceral leishmaniasis SARIMA-LSTM model effectiveness evaluation Yangquan |
| title | Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis |
| title_full | Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis |
| title_fullStr | Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis |
| title_full_unstemmed | Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis |
| title_short | Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis |
| title_sort | application of the sarima lstm model to evaluate the effectiveness of interventions for visceral leishmaniasis |
| topic | Visceral leishmaniasis SARIMA-LSTM model effectiveness evaluation Yangquan |
| url | https://www.jidc.org/index.php/journal/article/view/20739 |
| work_keys_str_mv | AT mengchenhan applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis AT chongqihao applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis AT zhiyangzhao applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis AT peijunzhang applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis AT binwu applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis AT lixiaqiu applicationofthesarimalstmmodeltoevaluatetheeffectivenessofinterventionsforvisceralleishmaniasis |