This study aimed to describe knowledge, attitudes, and practices (KAP) in controlling COVID-19 and some related factors among the Vietnamese population in 2020. A cross-sectional study was conducted involving 1999 participants aged 18-59 years old, through an online questionnaire. The results showed that 92.2% of the participants had a high knowledge level regarding COVID-19 prevention measures, 68.6% had a positive attitude toward COVID-19 prevention measures, and 75.8% practiced all six measures for preventing the spread of the virus. Age, sex, marital status, knowledge, and fear were significantly associated with the practices aimed at COVID-19 prevention. Married people and participants with high levels of knowledge were more likely to practice all preventive measures. In contrast, young people, men, and those who fear COVID-19 were less likely to practice all preventative measures. Good KAP among Vietnamese people could be an important factor in helping authorities gain initial success in containing the coronavirus and COVID-19. In addition to continuously raising and maintaining the community's awareness, attitude, and practices in disease prevention, the introduction and strict implementation of sanctions and regulations were also important in ensuring good practices were implemented and sustained over time. Groups with lower KAP levels should be provided with more information and support to promote appropriate disease prevention practices.

Introduction: Healthcare workers (HCWs) are at the frontline of COVID-19 control and prevention but also are high-risk groups for COVID-19 infection. The low level of knowledge and negative attitudes toward COVID-19 among HCWs can lead to inappropriate responding, wrong diagnoses, and poor practices for prevention. This research aims to examine the knowledge, attitudes, and practices regarding COVID-19 prevention and factors influencing the practices among HCWs in Daklak province, Vietnam. Method: A cross-sectional study was conducted among 963 HCWs working at district health centers and commune health stations through an online survey. Results: Overall, HCWs have good knowledge (91.3%), a positive attitude (71.5%), and appropriate practice (83.1%) regarding COVID-19 prevention. There was 89.6% of HCWs facing difficulties in practicing preventive measures such as felt difficult to change their habits (56.4%), insufficient personal protective equipment (PPE) (40.0%), and inconvenience to practice preventive measures (14.4%). The factors associated with implementing good practices are age group, residence, and knowledge about COVID-19. Recommendation: The Daklak Department of Health should provide additional training programs and guidelines about COVID-19 prevention and PPE for HCWs. More studies on risk and protective factors, and assessment about KAP regarding COVID-19 prevention at the post of the pandemic are needed.

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.

Purpose This paper aims to analyze the current responses applied in Vietnam to the Coronavirus disease (COVID-19) pandemic and link these measures to priority actions highlighted in the Sendai Framework for Disaster Risk Reduction (SFDRR). From there, strengths, limitations and recommendations on applying the SFDRR to build the pandemic resilience in the future are discussed. Design/methodology/approach The authors synthesize literature on response measures to the COVID-19 pandemic in Vietnam from January to June 2020 and compare to four priority actions of the SFDRR including understanding risk, strengthening governance, investing in risk reduction for resilience and enhancing preparedness for effective response and resilient recovery. Findings Vietnam has effectively controlled the pandemic with 401 infected cases and no death so far. Well preparation, timely policies’ implementation, risk communication and comprehensive approaches are key strategies. These measures are same as the four priority actions in the SFDRR. Originality/value To the best of the authors’ knowledge, this is the first study in Vietnam to link the COVID-19 response and the SFDRR, which can serve as an important example for other countries in responding to the pandemic. Some measures have surpassed SFDRR’s guidance, especially preventive responses applied nationwide with strong political will and the community’s commitment accompanied by sanctions. Cultural factors such as the habit of using masks to prevent air pollution have contributed to the good observance of wearing mask regulations during the pandemic. However, some areas that need more attention include specific solutions for vulnerable groups, limiting fake news and ensuring patient privacy.

Dengue fever/dengue hemorrhagic fever (DF/DHF) has been an important public health challenge in Viet Nam and worldwide. This study was implemented in 2016-2017 using retrospective secondary data to explore associations between monthly DF/DHF cases and climate variables during 2008 to 2015. There were 48 175 DF/DHF cases reported, and the highest number of cases occurred in November. There were significant correlations between monthly DF/DHF cases with monthly mean of evaporation ( r = 0.236, P < .05), monthly relative humidity ( r = -0.358, P < .05), and monthly total hours of sunshine ( r = 0.389, P < .05). The results showed significant correlation in lag models but did not find direct correlations between monthly DF/DHF cases and monthly average rainfall and temperature. The study recommended that health staff in Hanoi should monitor DF/DHF cases at the beginning of epidemic period, starting from May, and apply timely prevention and intervention measures to avoid the spreading of the disease in the following months. A larger scale study for a longer period of time and adjusting for other potential influencing factors could better describe the correlations, modelling/projection, and developing an early warning system for the disease, which is important under the impacts of climate change and climate variability.