Fig 1.
Yearly DF incident cases per 100,000 population (log-scaled) for 20 different provinces in northern, central, and southern Vietnam from 1997 to 2016.
In the box and whisker plots, green dots indicate mean values.
Table 1.
Meteorological factors from the Vietnam Institute of Meteorology, Hydrology and Environment.
Fig 2.
NIHE = National Institute of Hygiene and Epidemiology. IMHEN = Vietnam Institute of Meteorology, Hydrology and Climate Change. DF = dengue fever.
Table 2.
Root mean square errors for all prediction models in 20 Vietnamese provinces.
Table 3.
Mean absolute errors for all prediction models in 20 Vietnamese provinces.
Fig 3.
Prediction performances of CNN, LSTM, and LSTM-ATT during the last 36 months in six Vietnamese provinces.
Predicted incidence rates per 100,000 population from 2014 to 2016 are shown compared to the observed incidence rates. The closer the predictions are to the observed values, the better the prediction accuracies. CNN = convolutional neural network. LSTM = long short-term memory. LSTM-ATT = attention mechanism-enhanced LSTM.
Fig 4.
RMSEs and MAEs for all models (LSTM, and LSTM-ATT, CNN, Transformer) for all 20 provinces.
The smaller the values, the better the prediction accuracies. RMSE = root mean square error. MAE = mean absolute error. CNN = convolutional neural network. LSTM = long short-term memory. LSTM-ATT = attention mechanism-enhanced LSTM.
Fig 5.
DF forecasting models with RMSE- and MAE-based rankings.
Rankings are based on the relative scores for lowest RMSE or MAE in the prediction of dengue fever one month ahead. Grey-outlined circles indicate mean values. RMSE = root mean square error. MAE = mean absolute error. LSTM = long short-term memory. LSTM-ATT = attention mechanism-enhanced LSTM. CNN = convolutional neural network. Poisson = Poisson regression. XGB = XGBoost Extreme Gradient Boosting. SVR = Support Vector Regressor with Radial Basis Kernel. SVR-L = Support Vector Regressor with Linear Kernel. SARIMA = Seasonal Autoregressive Integrated Moving Average.
Fig 6.
Outbreak detection by LSTM-ATT.
Numbers of actual outbreak months, correct outbreak month predictions (true positive) and incorrect outbreak month predictions (false positive) for each province are shown (Fig 6A). Additionally, prediction metrics (precision, accuracy, sensitivity, and specificity) for each province are displayed (Fig 6B). If a province did not have any actual epidemic months in the evaluation period, the precision and sensitivity are not available. LSTM-ATT = attention mechanism-enhanced LSTM.
Fig 7.
Performance of multi-step ahead predictions of LSTM-ATT for all provinces.
Error metrics are displayed for all 20 provinces (Fig 7A for RMSE and middle for MAE) in addition to the predicted and observed incidence rates per 100,000 population in three provinces (Fig 7B). LSTM-ATT = attention mechanism-enhanced LSTM. RMSE = root mean square error. MAE = mean absolute error.
Fig 8.
Precision, accuracy, sensitivity, and specificity for multi-step ahead epidemic prediction using LSTM-ATT.
LSTM-ATT = attention mechanism-enhanced long short-term memory.