Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand

  1. Sipat Triukose
  2. Sirin Nitinawarat
  3. Ponlapat Satian
  4. Anupap Somboonsavatdee
  5. Ponlachart Chotikarn
  6. Thunchanok Thammasanya
  7. Nasamon Wanlapakorn
  8. Natthinee Sudhinaraset
  9. Pitakpol Boonyamalik
  10. Bancha Kakhong
  11. Yong Poovorawan

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named “coronavirus disease 2019” (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak—12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a “lockdown.” This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.09.01.20182873: (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
    Statistical analysis and data visualization: Unless specified otherwise, all the data clean up, transformation, and calculation were done using R and Python languages.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Tables and Annotations on Figures were done using Apple Keynote and Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    (Real-time results for Thailand and all available provinces can be found on Thailand COVID-19 Rt Tracker website: https://thai-covid19.live.) It is important to note that the limitations of the data used in the estimation reflected the reliability of the results. The main limitations were (i) a delay in time at which the cases were recorded and confirmed from the actual onset time of disease; (ii) a considerably small number of confirmed cases, especially at the province level. The results from Thailand (Fig 4A), Bangkok (Fig 4B), and Phuket (Fig 4C) were similar in overall trends and patterns. More specifically, the results for Thailand and Bangkok were almost identical because most of the confirmed cases in Thailand were from Bangkok, particularly in the Early and Spreading stages. On the other hand, the Rt plot for Phuket in Fig 4C seems to be at a higher level with a broader credible band compared to Thailand’s and Bangkok’s Rt plot. Due to a considerably smaller number of confirmed cases in Phuket, the estimated effective reproductive numbers and its credible band may be less reliable and more conservative than those for Thailand and Bangkok. In the Early stage, the Rt plots for Thailand, Bangkok, and Phuket showed instability (somewhat randomly up and down) with the daily confirmed cases less than 5 cases per day. These unstable or random trends with very few confirmed cases per day were due to the fact that this was the Early stage when people were starting to become i...

    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 10.1371/journal.pone.0246274
  3. Version published to 10.1101/2020.09.01.20182873v1 on medRxiv