Climate-driven dengue forecasting in Bangladesh: division-specific feature-set design and lag structure
- 1Department of Mathematics, University of Dhaka 1 , Dhaka, Bangladesh
- 2Department of Mathematics, Jagannath University 2 , Dhaka, Bangladesh
Abstract
Bangladesh shows marked annual variation in dengue incidence, partly driven by meteorological effects on Aedes breeding and transmission. We contrast consistently high-burden Dhaka with Barishal, where dengue incidence has recently increased, and emphasize feature-set design and predictor structure as the main methodological contributions. Using monthly dengue data from Directorate General of Health Services (DGHS, 2025 Daily dengue status report) and meteorological data from WorldWeatherOnline (World Weather Online. https://www.worldweatheronline.com/bangladesh-weather.aspx) for January 2022–October 2025, we compare four climate feature sets that vary wetness (rainy days versus rainfall) and sunshine (sun days versus sun hours), with temperature and humidity included in all sets. Correlation analysis across 0–4-month climate lags shows that rainfall has the strongest positive correlations at a 2-month lag, humidity at a 1-month lag, sunshine has the strongest negative correlations at a 2-month lag and temperature shows weak positive correlations at longer lags. For forecasting, we evaluate lagged climate covariates alone and combined with 1-month lagged dengue incidence. We compare multivariate Poisson regression (MPR), artificial neural networks (ANNs), extreme gradient boosting (XGBoost) and Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) regressors. For Dhaka, ANN-1 with SET-1 performs best (RMSE = 2176.70), whereas for Barishal, SARIMAX((0,1,1)(1,0,0,12)) with SET-2 performs best (RMSE = 817.56). Analyses use consistent monthly aggregation and division-specific tuning.
Related
- Anaemia Prevalence, Severity and Sex Disparities Among Healthcare‐Seeking Adults in Rural Chattogram Division, Bangladesh: Evidence From a Community‐Based Portable Health Clinic Programme
- Comparing the upregulations of helper T cell-specific activation-induced cell markers (AIM) among COVID-19 vaccinees in Bangladesh
- Functional O-specific polysaccharide-specific antibody responses in young children following Shigella infection in Bangladesh
- Mortality forecasting under climate risk: a stochastic approach with distributed lag nonlinear models
- Forecasting Lives Lost to Climate Change: A Precautionary Empirical Framework for Climate-Related Mortality
BibTeX
@article{khondaker2026climatedriven,
title = {{Climate-driven dengue forecasting in Bangladesh: division-specific feature-set design and lag structure}},
author = {Faizunnesa Khondaker and Md. Kamrujjaman},
journal = {Royal Society Open Science},
year = {2026},
month = sep,
doi = {10.1098/rsos.260746},
url = {https://royalsocietypublishing.org/rsos/article/13/9/260746/483449/Climate-driven-dengue-forecasting-in-Bangladesh},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Climate-driven dengue forecasting in Bangladesh: division-specific feature-set design and lag structure
AU - Faizunnesa Khondaker
AU - Md. Kamrujjaman
PY - 2026
DA - 2026/09/30
JO - Royal Society Open Science
DO - 10.1098/rsos.260746
UR - https://royalsocietypublishing.org/rsos/article/13/9/260746/483449/Climate-driven-dengue-forecasting-in-Bangladesh
AB - Bangladesh shows marked annual variation in dengue incidence, partly driven by meteorological effects on Aedes breeding and transmission. We contrast consistently high-burden Dhaka with Barishal, where dengue incidence has recently increased, and emphasize feature-set design and predictor structure as the main methodological contributions. Using monthly dengue data from Directorate General of Health Services (DGHS, 2025 Daily dengue status report) and meteorological data from WorldWeatherOnline (World Weather Online. https://www.worldweatheronline.com/bangladesh-weather.aspx) for January 2022–October 2025, we compare four climate feature sets that vary wetness (rainy days versus rainfall) and sunshine (sun days versus sun hours), with temperature and humidity included in all sets. Correlation analysis across 0–4-month climate lags shows that rainfall has the strongest positive correlations at a 2-month lag, humidity at a 1-month lag, sunshine has the strongest negative correlations at a 2-month lag and temperature shows weak positive correlations at longer lags. For forecasting, we evaluate lagged climate covariates alone and combined with 1-month lagged dengue incidence. We compare multivariate Poisson regression (MPR), artificial neural networks (ANNs), extreme gradient boosting (XGBoost) and Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) regressors. For Dhaka, ANN-1 with SET-1 performs best (RMSE = 2176.70), whereas for Barishal, SARIMAX((0,1,1)(1,0,0,12)) with SET-2 performs best (RMSE = 817.56). Analyses use consistent monthly aggregation and division-specific tuning.
SN - 2054-5703
ER -