Comparative Performance of Mechanistic, Statistical, and Hybrid Models of Forecasting Dengue Fever Incidence in Somalia. A Retrospective Time Series Analysis
ABSTRACT 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, N = 32) obtained from Our World in Data. Due to the lack of standardized case definitions and high under‐reporting inherent to Somalia's surveillance system, forecasts represent the projected reported burden rather than true infection rates. The data was divided into a training and a testing set (1990–2016 and 2017–2021, respectively). We tested a mechanistic Susceptible‐Infected‐Recovered (SIR) model, 8 single time series models (ARIMA, ETS, TBATS, and NNAR), and 12 hybrids using an averaging ensemble strategy. 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 (R0) calculated by the SIR model was 1.015 which shows that the disease is endemic. 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.