ArticleHealth science reports2024
Dengue in Tomorrow: Predictive Insights From ARIMA and SARIMA Models in Bangladesh: A Time Series Analysis.
Article in Health science reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A novel time-series modeling framework for predicting tuberculosis incidence in Sichuan, China.BMC infectious diseases · 2026Article
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction.Scientific reports · 2026Article
- Time-series decomposition and modeling of dengue cases in Malaysia, 2022-2024: a nationwide observational study.Osong public health and research perspectives · 2026Article
- Urban Hyperendemicity and Emerging Coastal Per-Capita Risk of Dengue in Bangladesh, 2021-2025: A Nationwide Spatiotemporal Ecological Analysis.Journal of tropical medicine · 2026Article
- The emergence of hyperendemic dengue in Bangladesh: An ecological study of structural breaks and transmission regime shifts, 2008-2025.PloS one · 2026Article
- Article
- Dengue incidence and its relationship with El Niño oceanic index, as a sensitive variable to anticipate outbreaks in the Colombian Caribbean regionBiomedica : revista del Instituto Nacional de Salud · 2025Article
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4 authors.
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Abstract
Backgrounds and Aims: Dengue fever has been a continued public health problem in Bangladesh, with a recent surge in cases. The aim of this study was to train ARIMA and SARIMA models for time series analysis on the monthly prevalence of dengue in Bangladesh and to use these models to forecast the dengue prevalence for the next 12 months. Methods: This secondary data-based study utilizes AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models to forecast dengue prevalence in Bangladesh. Data was sourced from the Institute of Epidemiology Disease Control and Research (IEDCR) and the Directorate General of Health Services (DGHS). STROBE Guideline for observational studies was followed for reporting this study. Results: The ARIMA (1,1,1) and SARIMA (1,1,2) models were identified as the best-performing models. The forecasts indicate a steady dengue prevalence for 2024 according to ARIMA, while SARIMA predicts significant fluctuations. It was observed that ARIMA (1,1,1) and SARIMA (1,2,2) (1,1,2) Conclusion: These models offer valuable insights for healthcare planning and resource allocation, although external factors and complex interactions must be considered. Dengue prevalence is expected to rise in future in Bangladesh.
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