Abstract

Dengue is a viral disease spread by mosquitoes and is found primarily in tropical and subtropical areas. Currently, dengue fever (DF) remains a significant public health challenge in Bangladesh, and various meteorological factors influence its incidence. This study aims to analyze the temporal patterns of dengue cases from January 2008 to November 2024 and explore the relationships between meteorological factors and the incidence of dengue fever (DF) in Bangladesh, utilizing time series forecasting models and multivariate Poisson models based on monthly dengue case data. Seasonal decomposition was measured using LOESS seasonal, trend, and residual components. A SARIMA forecast model for dengue cases and Poisson regression assessed meteorological impacts, considering one- and two-month lags. The result indicates that the highest number of dengue cases were found in August 2019 (52,636 cases) and in September 2023 (79,598 cases) with September standing out as the peak month in Bangladesh. Autocorrelation analysis revealed strong positive correlations at 1-month and 2-month lags, indicating the selection of the SARIMA (2,1,2) (1,1,1) [6] model, which effectively captured seasonality with a Mean Absolute Error coefficient of 1649 and a Root Mean Squared Error (RMSE) coefficient of 5203.44. Forecasts from 2024-2027 predict that dengue cases will fluctuate between 10,000 and 20,000 annually. Spearman’s rank correlation indicated positive associations between dengue cases and precipitation (r = 0.37, p<0.05), temperature (r = 0.28, p<0.05), wind speed (r = 0.25, p<0.05), and humidity (r = 0.18, p<0.05). Multivariable Poisson regression revealed that temperature (°C) (IRR = 1.02, 95% CI: 1.02– 1.02, p < 0.001), Humidity (%) (IRR = 1.25, 95% CI: 1.24–1.25, p < 0.001), Wind speed (m/s) (IRR = 1.10, 95% CI: 1.09–1.10, p < 0.001) significantly increased dengue incidence. In conclusion, this study emphasizes the critical role of humidity and temperature in shaping dengue incidence in Bangladesh, highlighting the need to integrate climate data into public health strategies for improved forecasting and control.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

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AbbreviationsDFDengue FeverSARIMASeasonal Autoregressive Integrated Moving AverageRMSERoot Mean Squared ErrorIRRIncidence Rate RatioCIConfidence IntervalADEAntibody-dependent enhancementARIMAAutoregressive Integrated Moving AverageSVMSupport vector machinesANNArtificial neural networksHMMHidden Markov modelsIEDCRInstitute of Epidemiology, Disease Control and ResearchBMDBangladesh Meteorological DepartmentARAutoregressionMAMoving AverageMPRMultivariate Poisson regressionVIFVariance inflation factorsACFAutocorrelation functionPACFPartial Autocorrelation functionMAEMean Absolute ErrorSDStandard deviationMAPEMean absolute percentage error