Comparative Performance of Mechanistic, Statistical, and Hybrid Models of Forecasting Dengue Fever Incidence in Somalia. A Retrospective Time Series Analysis.

Background and Aims: Dengue fever is a growing menace in Somalia, a climate change prone region with a weak healthcare system. An imperative of public health is effective forecasting models. This paper will provide a detailed comparative analysis comparing mechanistic, statistical, and hybrid models in order to find the best forecasting model to use in this data-sparse situation of dengue fever. Methods: We utilized raw reported annual incidence data of dengue (1990-2021, Findings: Although the time series models (individually) offered the basics of predictive power, the hybrid ARIMA-TBATS model was the most successful and it surpassed the other models. It performed the highest accuracy statistics on the testing data with a Mean Absolute Percentage Error (MAPE) of 6.49% and a Root Mean Squared Error (RMSE) of 663.81 and outperformed the best single model (TBATS) which had a MAPE of 7.14%. The basic reproduction number ( Conclusion: This paper concludes that the ARIMA-TBATS hybrid should be the most empirically effective tool out of the ones considered when planning to forecast dengue in Somalia. Real-World Application: This ARIMA-TBATS framework is designed to serve as the mathematical engine for a National Dengue Early Warning System (DEWS) in Somalia, allowing public health officials to physically preempt outbreaks by routing medical supplies, mobilizing fumigation teams, and distributing bed nets to high-risk zones months before peak incidence occurs.