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.

Combinations of artemisinin and quinine for uncomplicated falciparum malaria were studied. A total of 268 patients were randomized to 7 days of quinine at 10 mg/kg of body weight three times a day (Q) or to artemisinin at 20 mg/kg of body weight followed by 3 (AQ3) or 5 (AQ5) days of quinine. Recrudescence rates were 16, 38, and 15% for the Q, AQ3, and AQ5 groups, respectively (P < 0.001). Recrudescence was associated with shorter parasite clearance time (PCT) and longer treatment after the blood smear had become negative (eradication time). However, classification of patients to outcome-recrudescence or radical cure-was correct in only 77% of patients. The population kinetics of the parasitemia was estimated with nonlinear mixed-effect models. Several models were tested, but the best model was a monoexponential decline of the parasitemia in which the mean parasite elimination half-life was shorter after artemisinin (5.1 h; 95% confidence interval [CI], 4.9 to 5.2 h) than after quinine (8.0 h [95% CI, 7.5 to 8.3 h]). Attempts to simulate the initial increase of the parasitemia did not result in better models with a biologically plausible interpretation. Recrudescence was associated with slower parasite clearance and a higher simulated terminal parasitemia (P term ). The classification of patients to outcome groups based on P term was correct in 78% of patients. The data suggest that parasite strains with reduced sensitivity to quinine are prevalent in Vietnam, with slower parasite clearance and consequent recrudescence. A single dose of artemisinin induces rapid parasite reduction and lowers the value of P term , but to prevent recrudescence, this should be followed by quinine for at least 3 days after parasite clearance, or 5 days in total.

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Our data show that (1) SSIs are prevalent at Cho Ray Hospital; (2) antimicrobial use among surgical patients is widespread and inconsistent with published guidelines; and (3) pathogens often are resistant to commonly used antimicrobials. SSI prevention interventions, including appropriate use of antimicrobials, are needed in this population.

Background Dementia poses a serious threat to the wellbeing of the elderly. In the context of the rapidly ageing population of Vietnam however, little is known about the prevalence of symptoms and other related factors. This study aims to detect the prevalence of cognitive symptoms of dementia in the elderly in Vietnam as well as other associated factors. Methods A cross-sectional study was conducted over a period of six communes at the Northern, Central and Southern region of Vietnam. Prevalence of cognitive symptoms of dementia was the outcome of interest and assessed by Mini Mental State Evaluation (MMSE) questionnaire and was standardized according to the age structure of Vietnam. A total of 3308 adults aged 60 and above were included. Association between having cognitive symptoms of dementia and other factors was assessed with logistic regression. Findings Cognitive symptoms of dementia were perceived in 46.4% of the sample group. The symptoms were more common among participants who were older, female, had a lower educational level, were not physically active or have previously had stroke. Conclusions Prevalence of cognitive symptoms of dementia in adults aged 60 and above was relatively high in Vietnam. Other modifiable associated factors including physical inactivity and social connectedness should also be considered in designing intervention program to prevent dementia in the future.