Health science reports · 2026
Comparative Performance of Mechanistic, Statistical, and Hybrid Models of Forecasting Dengue Fever Incidence in Somalia. A Retrospective Time Series Analysis.
СтатьяНазвание и аннотация: Перевод в очереди
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Abdi Abdiftah Mohamud, Nadarajah Saralees, Muse Abdisalam Hassan
Аннотация
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 ( R 0 ) 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.
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 ( R 0 ) 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.