To assess the role of in-flight transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we investigated a cluster of cases among passengers on a 10-hour commercial flight. Affected persons were passengers, crew, and their close contacts. We traced 217 passengers and crew to their final destinations and interviewed, tested, and quarantined them. Among the 16 persons in whom SARS-CoV-2 infection was detected, 12 (75%) were passengers seated in business class along with the only symptomatic person (attack rate 62%). Seating proximity was strongly associated with increased infection risk (risk ratio 7.3, 95% CI 1.2–46.2). We found no strong evidence supporting alternative transmission scenarios. In-flight transmission that probably originated from 1 symptomatic passenger caused a large cluster of cases during a long flight. Guidelines for preventing SARS-CoV-2 infection among air passengers should consider individual passengers’ risk for infection, the number of passengers traveling, and flight duration.

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 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.

Outbreaks are spatiotemporally associated with litchi harvest, but the causative agent remains unknown.

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 .