Evidence map›Paper›PMID 33671746›Full record

ArticleInternational journal of environmental research and public health2021

Statistical Estimation of Effects of Implemented Government Policies on COVID-19 Situation in South Korea.

Gyujin Heo, Catherine Apio, Kyulhee Han, Taewan Goo, Hye Won Chung, Taehyun Kim, Hakyong Kim, Yeonghyeon Ko, Doeun Lee, Jisun Lim and 1 more

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Gyujin HeoInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.ORCID 0000-0003-3499-2848
Catherine ApioInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.ORCID 0000-0003-0240-794X
Kyulhee HanInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Taewan GooInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.ORCID 0000-0001-9427-2290
Hye Won ChungDepartment of Chemistry, Seoul National University, Seoul 08826, Korea.
Taehyun KimDepartment of Statistics, Seoul National University, Seoul 08826, Korea.ORCID 0000-0002-9257-5914
Hakyong KimDepartment of Industrial Engineering, Seoul National University, Seoul 08826, Korea.ORCID 0000-0002-6021-5121
Yeonghyeon KoDepartment of Statistics, Seoul National University, Seoul 08826, Korea.
Doeun LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Jisun LimThe Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Korea.ORCID 0000-0002-1058-3292
Taesung ParkInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.ORCID 0000-0002-8294-590X

Funding

Science & Technology Policy Institute of Korea 0409-20200222
6 · The paper itself

Abstract

Since the outbreak of novel SARS-COV-2, each country has implemented diverse policies to mitigate and suppress the spread of the virus. However, no systematic evaluation of these policies in their alleviation of the pandemic has been done. We investigate the impact of five indices derived from 12 policies in the Oxford COVID-19 Government Response Tracker dataset and the Korean government's index, which is the social distancing level implemented by the Korean government in response to the changing pandemic situation. We employed segmented Poisson model for this analysis. In conclusion, health and the Korean government indices are most consistently effective (with negative coefficients), while the restriction and stringency indexes are mainly effective with lagging (1~10 days), as intuitively daily confirmed cases of a given day is affected by the policies implemented days before, which shows that a period of time is required before the impact of some policies can be observed. The health index demonstrates the importance of public information campaign, testing policy and contact tracing, while the government index shows the importance of social distancing guidelines in mitigating the spread of the virus. These results imply the important roles of these polices in mitigation of the spread of COVID-19 disease.

Indexed as

GovernmentHealth PolicyCOVID-19HumansPandemicsRepublic of KoreaCOVID-19indiceslaggingpandemicpoliciesSARS-COV-2segmented Poisson modelstringency index

Identifiers

PMID33671746
PMCPMC7926904

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.