Temporal Association of Climatic Factors with Dengue Incidence and Forecasting Using SARIMAX Model: A Time Series Study in Karnataka, India

Abstract Background: Dengue are Aedes-transmitted arboviral diseases whose transmission dynamics are strongly modulated by climatic conditions. Karnataka in India, experiences recurrent, seasonally variable dengue but region-specific forecasting models that incorporate climatic predictors remain limited.

Objectives

(1) To describe the temporal distribution of monthly dengue cases and climatic variables in Karnataka, India; (2) To develop and evaluate a SARIMAX model incorporating climatic variables for forecasting monthly dengue cases in Karnataka, India Methods: In this retrospective time-series study, monthly dengue case counts from September 2021 to December 2025 were obtained from the Commissionerate of Health & Family Welfare Services, Government of Karnataka and matched with monthly climatic data like minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and solar radiation. Series were log-transformed and rendered stationary by first-order non-seasonal and seasonal differencing. Cross-correlation function (CCF) analysis identified biologically plausible lagged climatic predictors, which were incorporated as exogenous variables in candidate SARIMAX models compared using stationary R², RMSE, MAPE, normalized BIC, and the Ljung-Box test, in IBM SPSS Statistics 2025, with a significance level of p < 0.05.