The Epidemiology and Control of Human Influenza in Vietnam

Understanding the epidemiology of human influenza in Viet Nam is important for developing local policies and also for understanding the dynamics of influenza in tropical and subtropical southeast Asia. I have analysed an 18 year time-series of influenza-like-illness (ILI) surveillance data, and assessed the relationship of this time-series with climate variables and with sentinel influenza virus surveillance data. I also conducted a study of influenza A/H1N1 transmission within households. ILI notifications in Viet Nam show a latitudinal gradient, with seasonality in the north but no seasonal pattern observed in low lying areas of central and southern Viet Nam. Seasonality is however observed in the elevated provinces of central Viet Nam, suggesting that the seasonal patterns are driven by climate. Principal component analysis finds that temperature and absolute humidity (AH) are positively correlated and together explain around 59% of total climatic variance, and that there is a strong latitudinal gradient in these variables. Regression tree analysis shows that provinces with strong seasonality of AH have strong ILI seasonality. Although virological surveillance data are limited, increases in ILI notifications are associated with an increase in the proportion of upper respiratory tract swabs that are influenza positive. In a prospective study of H1N1/2009 transmission in a household-based cohort, 11 of 59 household contacts were infected, giving a household secondary infection risk of 18.6% (95%CI 10.7-30.4%), but 5 (45%) did not develop symptoms. Virus genetic sequencing indicated that 10 of the 11 secondary cases (91%) were probably infected within the household rather than from the community. This research provides new insights into the seasonality and climatic determinants of ILI and influenza epidemiology in Viet Nam, and on the transmission of influenza within households. The findings are valuable for national influenza control policies and also add to the current state of knowledge of influenza epidemiology

Would greater household wealth make young children smarter?

Drawing on the Young Lives data obtained from three cycles of surveys from 2006 to 2016, our study examines factors affecting children’s cognitive ability in Vietnam. Controlling for the conditional wealth, which is the residual of the regression equation of the household wealth index in 2006 and 2013, our study provides evidence that conditional wealth has an effect of increasing the cognitive capacity of 15-year-old children, manifested in all three methods of measurement: by vocabulary points, math scores and reading comprehension scores in Vietnamese. This finding once again confirms that late intervention after the first 1,000 days has a positive impact on children's cognitive ability. Notably, our finding suggests that using the conditional wealth enables to capture the impact of economic shocks, which in turn have a significant effect on the cognitive ability of children in Vietnam

Observations on multiple mating flights of Apis dorsata queens

This observation is aimed at providing information for a reasonable comparative study on reproductive biology among the honeybee species. The research was carried out in 1996 in the submerged Melaleuca forest of southern Vietnam, where low-nesting colonies on man-made supports, rafters, allowed us to make detailed observations on the queens. Flights of six newly emerged queens were observed and after their final mating flights, queens were dissected to count the sperm number. The five investigated queens took their first flights 6 ± 1 (mean ± SD) days after emergence. Four queens took orientation flights of less than 3 min. One queen flew to mate without any orientation flight. Mating flights happened around sunset and lasted 15.4 ± 4.3 (n = 14) min. A queen undertook two to four mating flights and after fully mating, she had 5.5 ± 0.9 (n = 5) million sperm in her spermatheca. This study indicated the extreme polyandry in A. dorsata. © Inra/DIB/AGIB/Elsevier, Pari

Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods

Combating the COVID-19 Epidemic: Experiences from Vietnam.

The COVID-19 pandemic is spreading fast globally. Vietnam's strict containment measures have significantly reduced the spread of the epidemic in the country. This was achieved through the use of emergency control measures in the epidemic areas and integration of resources from multiple sectors including health, mass media, transportation, education, public affairs, and defense. This paper reviews and shares specific measures for successful prevention and control of COVID-19 in Vietnam, which could provide useful learning for other countries

Cardiovascular Disease Risk Factor Patterns and Their Implications for Intervention Strategies in Vietnam

Background. Data on cardiovascular disease risk factors (CVDRFs) in Vietnam are limited. This study explores the prevalence of each CVDRF and how they cluster to evaluate CVDRF burdens and potential prevention strategies. Methods. A cross-sectional survey in 2009 (2,130 adults) was done to collect data on behavioural CVDRF, anthropometry and blood pressure, lipidaemia profiles, and oral glucose tolerance tests. Four metabolic CVDRFs (hypertension, dyslipidaemia, diabetes, and obesity) and five behavioural CVDRFs (smoking, excessive alcohol intake, unhealthy diet, physical inactivity, and stress) were analysed to identify their prevalence, cluster patterns, and social predictors. Framingham scores were applied to estimate the global 10-year CVD risks and potential benefits of CVD prevention strategies. Results. The age-standardised prevalence of having at least 2/4 metabolic, 2/5 behavioural, or 4/9 major CVDRF was 28%, 27%, 13% in women and 32%, 62%, 34% in men. Within-individual clustering of metabolic factors was more common among older women and in urban areas. High overall CVD risk (≥20% over 10 years) identified 20% of men and 5% of women—especially at higher ages—who had coexisting CVDRF. Conclusion. Multiple CVDRFs were common in Vietnamese adults with different clustering patterns across sex/age groups. Tackling any single risk factor would not be efficient