Summary of the COVID-19 outbreak in Vietnam – Lessons and suggestions

Nguyen, Trang Ha; Vu, Danh C.

doi:10.1016/j.tmaid.2020.101651

Cited by 60 publications

(49 citation statements)

References 2 publications

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“…The estimated mean of incubation period for 19 Vietnamese confirmed COVID-19 cases using Weibull distribution model is higher than that of SARS in Hong Kong and Beijing [ 21 ] and in MERS [ 15 , 16 , 22 , 23 ]. Hence, mathematical models of COVID-19 should not be fitted with incubation period of MERS or SARS.…”

Section: Discussionmentioning

confidence: 99%

“…Any discrepancies in data extraction were resolved by discussion between two researchers and facilitated by a third researcher to reach consensus. Previous studies also used the same approach to generate data sources for analysis [ 15 , 16 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, the country has limited resources, hence understanding the time of intensive monitoring will be beneficial to plan for effective measures to minimize the risk for hidden SARS-CoV-2 infection. While estimations of incubation period of SARS-CoV-2 have been conducted elsewhere in the world [4][5][6][7][8][9][10][11][12][13], there is no estimation of this parameter using data from Vietnam, instead, efforts were only focusing on describing pattern of COVID-19 pandemic in Vietnam [14][15][16]. This study aims to estimate the incubation period of Vietnamese confirmed COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the incubation period of COVID-19 in Vietnam

Bui¹,

Nguyen

Levine

et al. 2020

PLoS ONE

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Objective To estimate the incubation period of Vietnamese confirmed COVID-19 cases. Methods Only confirmed COVID-19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS-CoV-2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID-19 cases. Leave-one-out Information Criterion was used to assess the performance of three models. Results A total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CrI): 4.89–8.5), standard deviation (SD) was 3.05 (95%CrI 3.05–5.30), median was 5.6, ranges from 1.35 to 13.04 days (2.5th to 97.5th percentiles). Extreme estimation of incubation periods is within 14 days from possible infection. Conclusion This analysis provides evidence for an average incubation period for COVID-19 of approximately 6.4 days. Our findings support existing guidelines for 14 days of quarantine of persons potentially exposed to SARS-CoV-2. Although for extreme cases, the quarantine period should be extended up to three weeks.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of the incubation period of COVID-19 in Vietnam

Bui¹,

Nguyen

Levine

et al. 2020

PLoS ONE

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“…Although these countries have successfully reduced case numbers, no causal relationship between use of app technology and epidemic control has yet been shown. 9 , 10 , 11 , 12 , 13 , 14 Many uncertainties remain on the optimal process of contact tracing with conventional methods or mobile apps, on the timing of testing for current or past infection, and on the required coverage of contact tracing needed.…”

Section: Introductionmentioning

confidence: 99%

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

Kretzschmar¹,

Rozhnova²,

Bootsma³

et al. 2020

The Lancet Public Health

717

762

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Summary Background In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. Methods We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy ( R CTS ) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. Findings For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7–0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% ( R CTS 0·8, 95% CI 0·7–1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep R CTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach R CTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. Interpretation In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, furth...

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“…The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in widespread infection and severe consequences [1] , leading to extreme hardships for many countries [2] , [3] . Through the rapid global spread of COVID-19, the pandemic has had a profound impact on low- and middle-income countries [4] .…”

Section: Introductionmentioning

confidence: 99%

Effects of Tanreqing Capsule on the negative conversion time of nucleic acid in patients with COVID-19: A retrospective cohort study

Xing

Xue

Chen

et al. 2021

Journal of Integrative Medicine

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Objective Traditional Chinese medicine plays a significant role in the treatment of the pandemic of coronavirus disease 2019 (COVID-19). Tanreqing Capsule (TRQC) was used in the treatment of COVID-19 patients in the Shanghai Public Health Clinical Center. This study aimed to investigate the clinical efficacy of TRQC in the treatment of COVID-19. Methods A retrospective cohort study was conducted on 82 patients who had laboratory-confirmed mild and moderate COVID-19; patients were treated with TRQC in one designated hospital. The treatment and control groups consisted of 25 and 57 cases, respectively. The treatment group was given TRQC orally three times a day, three pills each time, in addition to conventional Western medicine treatments which were also administered to the control group. The clinical efficacy indicators, such as the negative conversion time of pharyngeal swab nucleic acid, the negative conversion time of fecal nucleic acid, the duration of negative conversion of pharyngeal-fecal nucleic acid, and the improvement in the level of immune indicators such as T-cell subsets (CD3, CD4 and CD45) were monitored. Results COVID-19 patients in the treatment group, compared to the control group, had a shorter negative conversion time of fecal nucleic acid (4 vs. 9 days, P = 0.047) and a shorter interval of negative conversion of pharyngeal-fecal nucleic acid (0 vs. 2 days, P = 0.042). The level of CD3 + T cells increased in the treatment group compared to the control group ([317.09 ± 274.39] vs. [175.02 ± 239.95] counts/μL, P = 0.030). No statistically significant differences were detected in the median improvement in levels of CD4 + T cells (173 vs. 107 counts/μL, P = 0.208) and CD45 + T cells (366 vs. 141 counts/μL, P = 0.117) between the treatment and control groups. Conclusion Significant reductions in the negative conversion time of fecal nucleic acid and the duration of negative conversion of pharyngeal-fecal nucleic acid were identified in the treatment group as compared to the control group, illustrating the potential therapeutic benefits of using TRQC as a complement to conventional medicine in patients with mild and moderate COVID-19. The underlying mechanism may be related to the improved levels of the immune indicator CD3 + T cells.

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Summary of the COVID-19 outbreak in Vietnam – Lessons and suggestions

Cited by 60 publications

References 2 publications

Estimation of the incubation period of COVID-19 in Vietnam

Estimation of the incubation period of COVID-19 in Vietnam

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

Effects of Tanreqing Capsule on the negative conversion time of nucleic acid in patients with COVID-19: A retrospective cohort study

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