Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼10⁷ km². We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997–1998, which was followed by a period of extremely low incidence in 2001–2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997–1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2–5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia. Significance Persons living in the tropics and subtropics are at risk for dengue fever and dengue hemorrhagic fever, and large epidemics occur unexpectedly that can overburden healthcare systems. The spatial and temporal dynamics of dengue transmission are poorly understood, limiting disease control efforts. We compiled a large-scale dataset and analyzed continental-scale patterns of dengue in Southeast Asia. Our analysis shows that periods of elevated temperatures can drive the occurrence of synchronous dengue epidemics across the region. This multicountry collaborative study improved insight that may lead to improved prediction of dengue transmission patterns and more effective disease surveillance and control efforts.

The spread of infectious diseases from person to person is determined by the frequency and nature of contacts between infected and susceptible members of the population. Although there is a long history of using mathematical models to understand these transmission dynamics, there are still remarkably little empirical data on contact behaviors with which to parameterize these models. Even starker is the almost complete absence of data from developing countries. We sought to address this knowledge gap by conducting a household based social contact diary in rural Vietnam. A diary based survey of social contact patterns was conducted in a household-structured community cohort in North Vietnam in 2007. We used generalized estimating equations to model the number of contacts while taking into account the household sampling design, and used weighting to balance the household size and age distribution towards the Vietnamese population. We recorded 6675 contacts from 865 participants in 264 different households and found that mixing patterns were assortative by age but were more homogenous than observed in a recent European study. We also observed that physical contacts were more concentrated in the home setting in Vietnam than in Europe but the overall level of physical contact was lower. A model of individual versus household vaccination strategies revealed no difference between strategies in the impact on R₀. This work is the first to estimate contact patterns relevant to the spread of infections transmitted from person to person by non-sexual routes in a developing country setting. The results show interesting similarities and differences from European data and demonstrate the importance of context specific data.

Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI: 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI: 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs. Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a “signature” in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza – and the nature of infection.

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.

Prospective community-based studies have provided fundamental insights into the epidemiology of influenza in temperate regions, but few comparable studies have been undertaken in the tropics. The authors conducted prospective influenza surveillance and intermittent seroprevalence surveys in a household-based cohort in Vietnam between December 2007 and April 2010, resulting in 1,793 person-seasons of influenza surveillance. Age- and sex-standardized estimates of the risk of acquiring any influenza infection per season in persons 5 years of age or older were 21.1% (95% confidence interval: 17.4, 24.7) in season 1, 26.4% (95% confidence interval: 22.6, 30.2) in season 2, and 17.0% (95% confidence interval: 13.6, 20.4) in season 3. Some individuals experienced multiple episodes of infection with different influenza types/subtypes in the same season (n = 27) or reinfection with the same subtype in different seasons (n = 22). The highest risk of influenza infection was in persons 5–9 years old, in whom the risk of influenza infection per season was 41.8%. Although the highest infection risk was in school-aged children, there were important heterogeneities in the age of infection by subtype and season. These heterogeneities could influence the impact of school closure and childhood vaccination on influenza transmission in tropical areas, such as Vietnam.