Background Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. Objective This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. Methods Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997–2013 were used to train models, which were then evaluated using data from 2014–2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results and discussion LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. Conclusion This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.

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The purpose of this study is to examine the effects of energy consumption, economic growth, and trade openness on environmental pollution in a developing country, especially in the case of Vietnam. The study was conducted on the basis of time-series data collected in the period of time between the years 1990 and 2014. By a method of autoregressive distributed lag and testing the hypothesis of the environmental Kuznets curve, our result demonstrated that environmental Kuznets curve could be found in both the long run and short run. There existed an inverted U-shaped relationship between different pollutants and per capita income. Further, energy consumption could positively affect carbon dioxide (CO2) emissions in the short run, but negatively could affect CO2 emissions in the long run because of transformation from non-renewable energy sources to renewable energy sources. In addition, environmental pollution converged on its long-run equilibrium by at least 29.4% with the speed adjustment via the channel of income, energy consumption, and trade openness. In terms of trade openness, the country has a positive and significant effect on CO2 emissions in both the long run and short run.