Assessing dengue forecasting methods: A comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil
Authors: Chen Xiang , Moraga Paula 
Published: June 2024

Background

Dengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue outbreaks is crucial for effective public health planning and intervention. This study aims to assess the predictive performance and computational efficiency of a number of statistical models and machine learning techniques for dengue forecasting, both with and without the inclusion of climate factors, to inform the design of dengue surveillance systems.

Methods

The study considers dengue cases in Rio de Janeiro, Brazil, as well as climate factors known to affect disease transmission. Employing a dynamic window approach, various statistical methods and machine learning techniques were used to generate weekly forecasts at several time horizons. Error measures, uncertainty intervals, and computational efficiency obtained with each method were compared. Statistical models considered were Autoregressive (AR), Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing State Space Model (ETS). Additionally, models incorporating temperature and humidity as covariates, such as Vector Autoregression (VAR) and Seasonal ARIMAX (SARIMAX), were employed. Machine learning techniques evaluated were Random Forest, XGBoost, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Prophet. Ensemble approaches that integrated the top performing methods were also considered. The evaluated methods also incorporated lagged climatic variables to account for delayed effects.

Results

Among the statistical models, ARIMA demonstrated the best performance using only historical case data, while SARIMAX significantly improved predictive accuracy by incorporating climate covariates. In general, the LSTM model, particularly when combined with climate covariates, proved to be the most accurate machine learning model, despite being slower to train and predict. For long-term forecasts, Prophet with climate covariates was the most effective. Ensemble models, such as the combination of LSTM and ARIMA, showed substantial improvements over individual models.

Conclusion

This study demonstrates the strengths and limitations of various methods for dengue forecasting across multiple timeframes. It highlights the best-performing statistical and machine learning methods, including their computational efficiency, underscoring the significance of machine learning techniques and the integration of climate covariates to improve forecasts. These findings offer valuable insights for public health officials, facilitating the development of dengue surveillance systems for more accurate forecasting and timely allocation of resources to mitigate dengue outbreaks.

Author summary

Dengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue can significantly aid in public health planning and response. In this study, we compared the performance of various statistical models and machine learning techniques to predict dengue cases across several timeframes. In the evaluation, we used historical dengue case data in Rio de Janeiro, Brazil, as well as climate factors such as temperature and humidity known to affect transmission. Methods considered included traditional statistical models like ARIMA and SARIMAX, and advanced machine learning approaches like Random Forest, XGBoost, SVM, LSTM, and Prophet. We found that integrating climate data significantly improved the accuracy of forecasts. Specifically, the LSTM model combined with climate covariates provided the most accurate predictions overall, while Prophet was particularly effective for long-term forecasts. Additionally, ensemble approaches that combined multiple models outperformed individual models. This work demonstrates the potential of machine learning techniques to provide timely and accurate predictions, and emphasizes the importance of climate data in dengue forecasting. The study aims to support public health officials in developing dengue surveillance systems to enable informed decision-making for mitigating the impact of dengue outbreaks.