ArticlePloS one2025
Time series analysis of dengue incidence and its association with meteorological risk factors in Bangladesh.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Article
- Climate-Driven Advanced Machine Learning Approach for Dengue Incidence Forecasting in Bangladesh.Health science reports · 2026Article
- DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction.Scientific reports · 2026Article
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
- Time-series decomposition and modeling of dengue cases in Malaysia, 2022-2024: a nationwide observational study.Osong public health and research perspectives · 2026Article
- Housing structure shapes dengue transmission dynamics in a rapidly urbanizing Malaysian district.Scientific reports · 2026Article
- Article
- Spatial distribution and risk assessment of dengue incidence at district level across major climatic zones in India.PloS one · 2026Article
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Abstract
Dengue is a mosquito-borne viral disease affecting tropical and subtropical regions. In Bangladesh, dengue fever remains a rising public health threat driven by meteorological factors. This study aimed to assess the temporal trends and how meteorological factors influence dengue incidence in Bangladesh from 2008 to 2024. Monthly reported dengue cases were analyzed using time series forecasting techniques and multivariate Poisson regression models. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting (XGBoost) models were used for forecasting. Correlation analysis and Poisson regression assessed meteorological effects with one- and two-month lags. The result indicates that the highest number of dengue cases was found in September 2023 (79,598 cases). Autocorrelation revealed a strong positive correlation at 1-month and 2-month lags. Forecasts from 2024-2027 predict that dengue cases will fluctuate between 10,000 and 20,000 annually from the predictive models. Spearman's rank correlation indicated significant positive associations between dengue cases and precipitation, temperature, wind speed, and humidity. Multivariable Poisson regression revealed that temperature (°C) (IRR = 1.02), Humidity (%) (IRR = 1.25), and Wind speed (m/s) (IRR = 1.10) significantly increased dengue incidence. Between multivariate SARIMA, XGBoost, and Poisson regression, the best-performing model was ARIMA (RMSE: 5058.066). In conclusion, the study highlights the substantial influence of climatic factors on dengue dynamics in Bangladesh, emphasizing the need to integrate meteorological data into early warning systems and develop adaptive, climate-informed control and surveillance strategies.
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