An epidemiologic profile of COVID-19 patients in Vietnam

  1. Huy G. Nguyen
  2. Tuan V. Nguyen

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background and Aim

There is a paucity of data on the COVID-19 pandemic in Vietnam. In this paper, we sought to provide an epidemiologic description of patients who were infected with SARS-Cov-2 in Vietnam.

Methods

Data were abstracted from the wikipedia’s COVID-19 information resource and Johns Hopkins University Dashboard. Demographic data and treatment status were obtained for each patient in each day. The coverage period was from 23/1/2020 to 10/4/2020. Descriptive analyses of incident cases were stratified by gender and age group. The estimation of the reproduction ratio was done with a bootstrap method using the R statistical environment.

Results

During the coverage period, Vietnam has recorded 257 cases of COVID-19. Approximately 54% of the cases were women. The median age of patients was 30 years (range: 3 months to 88 years), with 78% of patients aged 49 or younger. About 66% (n = 171) of patients were overseas tourists (20%) and Vietnamese students or workers returning from overseas (46%). Approximately 57% (n = 144) of patients have been recovered and discharged from hospitals. There have been no mortality. The reproduction ratio was estimated to range between 0.95 and 1.24.

Conclusion

These data indicate that a majority of COVID-19 patients in Vietnam was imported cases in overseas tourists and young students and workers who had returned from overseas.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.10.20061226: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Case identification: The data used in the present analysis were collated from two publicly available datasets: the Wikipedia [4] and the Johns Hopkins Dashboard system [3, 5].
    Wikipedia
    suggested: (Wikipedia, RRID:SCR_004897)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present results should be interpreted in the context of limitations. It can be argued that the reliance on wikipedia data is a weakness, because we could not directly ascertain the infection. Because there were no clinical data, we could not evaluate the severity and treatment outcome of patients. It is important to note that the epidemic is still going on, and data are continuously evolved by the epidemic course; thus all estimates should be treated as temporary and time-dependent. In Vietnam, the government has been proactive since the first case identified. A series of public health measures have been introduced to control the epidemic. These measures included stringent exit screening at airports, limited travel ban, application of information technology in the identification of potential cases, social distancing, and limited lockdown in major cities. These measures have been supported and folowed by the public at large. In summary, this analysis has shown that COVID-19 patients in Vietnam were largely imported cases in overseas tourists and young students and workers who had returned from overseas. The low estimated reproduction ratio and force of infection show that the epidemic is on the downward trend, suggesting that the government’s strategy of containment has been effective.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.10.20061226v1 on medRxiv