Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burdens at the national sub-national scale in Colombia

Abstract

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed for the national and departmental levels in Colombia to predict weekly dengue cases at 12-weeks ahead. A national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, population counts, income inequality, and education. Our RF model trained on the national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the departments data. Additionally, the forecast errors of the national RF model were smaller to those of the national ANN model and were increased with the forecast horizon increasing from one-week ahead (mean absolute error, MAE: 5.80; root mean squared error, RMSE: 11.10) to 12-weeks ahead (MAE: 13.38; RMSE: 26.82). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study showed the potential of RF in dengue forecasting with also demonstrating the feasibility of using a national model to forecast at finer spatial scales. Furthermore, sociodemographic predictors are important to include to capture longer-term trends in dengue. Author summaryDengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and neural networks (NN). National and local (departmental-level) models were compared and used to predict dengue cases in the future. The results showed that the counts of future dengue cases were more accurately estimated by RF than by NN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national model applied to local data was more accurate in dengue forecasting compared to the local model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.