Background With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Background One hundred days after SARS-CoV-2 was first reported in Vietnam on January 23rd, 270 cases were confirmed, with no deaths. We describe the control measures used by the Government and their relationship with imported and domestically-acquired case numbers, with the aim of identifying the measures associated with successful SARS-CoV-2 control. Methods Clinical and demographic data on the first 270 SARS-CoV-2 infected cases and the timing and nature of Government control measures, including numbers of tests and quarantined individuals, were analysed. Apple and Google mobility data provided proxies for population movement. Serial intervals were calculated from 33 infector-infectee pairs and used to estimate the proportion of pre-symptomatic transmission events and time-varying reproduction numbers. Results A national lockdown was implemented between April 1st and 22nd. Around 200 000 people were quarantined and 266 122 RT-PCR tests conducted. Population mobility decreased progressively before lockdown. 60% (163/270) of cases were imported; 43% (89/208) of resolved infections remained asymptomatic for the duration of infection. The serial interval was 3·24 days, and 27·5% (95% confidence interval, 15·7%-40·0%) of transmissions occurred pre-symptomatically. Limited transmission amounted to a maximum reproduction number of 1·15 (95% confidence interval, 0·37-2·36). No community transmission has been detected since April 15th. Conclusions Vietnam has controlled SARS-CoV-2 spread through the early introduction of mass communication, meticulous contact-tracing with strict quarantine, and international travel restrictions. The value of these interventions is supported by the high proportion of asymptomatic and imported cases, and evidence for substantial pre-symptomatic transmission.

Background:One hundred days after SARS-CoV-2 was first reported in Vietnam on January 23 rd , 270 cases have been confirmed, with no deaths. We describe the control measures used by the Government and their relationship with imported and domestically-acquired case numbers, with the aim of identifying the measures associated with successful SARS-CoV-2 control. Methods:Clinical and demographic data on the first 270 SARS-CoV-2 infected cases and the timing and nature of Government control measures, including numbers of tests and quarantined individuals, were captured by Vietnam's National Steering Committee for COVID-19 response. Apple and Google mobility data provided proxies for population movement. Serial intervals were calculated from 33 infector-infectee pairs and used to estimate the proportion of pre-symptomatic transmission events and time-varying reproduction numbers. Findings:After the first confirmed case on January 23 rd , the Vietnamese Government initiated mass communications measures, case-contact tracing, mandatory 14-day quarantine, school and university closures, and progressive flight restrictions. A national lockdown was implemented between April 1 st and 22 nd . Around 200 000 people were quarantined and 266 122 RT-PCR tests conducted. Population mobility decreased progressively before lockdown. 60% (163/270) of cases were imported; 43% (89/208) of resolved infections remained asymptomatic for the duration of infection. 21 developed severe disease, with no deaths. The serial interval was 3·24 days, and 27·5% (95% confidence interval, 15·7%-40·0%) of transmissions occurred pre-symptomatically. Limited transmission amounted to a maximum reproduction number of 1·15 (95% confidence interval, 0·37-2·36). No community transmission has been detected since April 15 th .

BackgroundDengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001–2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.MethodsThe data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001–2009) and validation (2010–2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil’s coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010–2012 were also used to generate risk maps.ResultsThe mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil’s coefficient of inequality of 0.22 was generated.ConclusionsThe study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil’s coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.

Cholera remains a major public health problem in many countries. Poor sanitation and inappropriate clean water supply, insufficient health literacy and community mobilization, absence of national plans and cross-border collaborations are major factors impeding optimal control of cholera in endemic countries.In March 2017, a group of experts from 10 Asian cholera-prone countries that belong to the Initiative against Diarrheal and Enteric Diseases in Africa and Asia (IDEA), together with representatives from the World Health Organization, the US National Institutes of Health, International Vaccine Institute, Agence de médecine préventive, NGOs (Save the Children) and UNICEF, met in Hanoi (Vietnam) to share progress in terms of prevention and control interventions on water, sanitation and hygiene (WASH), surveillance and oral cholera vaccine use.This paper reports on the country situation, gaps identified in terms of cholera prevention and control and strategic interventions to bridge these gaps.

Dengue is a serious infectious disease threat in Vietnam, but its spatiotemporal and socioeconomic risk factors are not currently well understood at the province level across the country and on a multiannual scale. We explore spatial trends, clusters and outliers in dengue case counts at the province level from 2011–2015 and use this to extract spatiotemporal variables for regression analysis of the association between dengue case counts and selected spatiotemporal and socioeconomic variables from 2013–2015. Dengue in Vietnam follows anticipated spatial trends, with a potential two-year cycle of high-high clusters in some southern provinces. Small but significant associations are observed between dengue case counts and mobility, population density, a province’s dengue rates the previous year, and average dengue rates two years previous in first and second order contiguous neighbours. Significant associations were not found between dengue case counts and housing pressure, access to electricity, clinician density, province-adjusted poverty rate, percentage of children below one vaccinated, or percentage of population in urban settings. These findings challenge assumptions about socioeconomic and spatiotemporal risk factors for dengue, and support national prevention targeting in Vietnam at the province level. They may also be of wider relevance for the study of other arboviruses, including Japanese encephalitis, Zika, and Chikungunya.

Since facing outbreaks of severe acute respiratory syndrome and avian influenza A in 2003, Vietnam has increasingly applied a One Health approach to address emerging infectious diseases of animal origin. Here, we reflect on the challenges and opportunities of One Health in the context of zoonoses, food safety, and antimicrobial resistance, drawing on a stocktake of One Health training, policy, and research in Vietnam. We also report on the results of a virtual consultation workshop held on July 2021 with representatives from 32 institutions in Vietnam. As Vietnam approaches nearly two decades of disease preparedness and response, we hope our experiences can provide practical insights to support countries in developing coordination mechanisms and moving the One Health agenda forward toward better public health outcomes.

Timely information is key for decision-making. The ability to predict dengue transmission ahead of time would significantly benefit planners and decision-makers. Dengue is climate-sensitive. Monitoring climate variability could provide advance warning about dengue risk. Multiple dengue early warning systems have been proposed. Often, these systems are based on deterministic models that have limitations for quantifying the probability that a public health event may occur. We introduce an operational seasonal dengue forecasting system where Earth observations and seasonal climate forecasts are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to six months ahead. We demonstrate that the system has skill and relative economic value at multiple forecast horizons, seasons, and locations. The superensemble generated, on average, more accurate forecasts than those obtained from the models used to create it. We argue our system provides a useful tool for the development and deployment of targeted vector control interventions, and a more efficient allocation of resources in Vietnam.