Evidence map›Paper›PMID 39511963›Full record

ArticleOsong public health and research perspectives2024

Performance of indicators used in regular risk assessments for COVID-19 in association with contextual factors.

Sujin Hong, Jiyoung Oh, Jia Lee, Yongmoon Kim, Bryan Inho Kim, Min Jei Lee, Hyunjung Kim, Sangwoo Tak

Abstract read
In one paragraph

Article in Osong public health and research perspectives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Sujin HongDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Jiyoung OhDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Jia LeeDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Yongmoon KimDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Bryan Inho KimDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Min Jei LeeDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Hyunjung KimDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.
Sangwoo TakDivision of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to summarize the results of coronavirus disease 2019 (COVID-19) risk assessments and to examine the associations between risk levels and various indicators, including COVID-19 incidence, risk perception, community mobility, and government policy.

methodsThe results of the risk assessment and the indicators utilized were summarized. From November 2021 to May 2022, the COVID-19 risk level was evaluated on a weekly basis, and its correlation with these indicators was analyzed. Data were obtained from press releases by the Korea Disease Control and Prevention Agency, regular surveys conducted by Hankook Research, and information available on the Google and Oxford websites.

resultsWeekly risk assessments were conducted for 30 weeks, using different indices depending on the phases. Correlation analysis revealed the strongest positive correlation between risk level and risk perception (r=0.841). The risk level from "1-week lead" demonstrated a strong positive correlation with the time-varying reproduction number (Rt). Similarly, the risk level from "week lagged value" showed a strong positive correlation with the number of severe cases in the hospital.

conclusionAt the time of risk assessment, the Rt precedes the risk level, while severe cases in hospitals follow. Therefore, the assessed risk level functioned as an early warning system. Risk perception demonstrated the strongest correlation with the risk level, suggesting consistency throughout the assessment period. Contextual indicators (e.g., risk perception) that consider time lags and implementation scales, could improve the evaluation of future risk assessment results, particularly when there are challenges in reflecting specific situations in coordinated emergency response.

Indexed as

COVID-19EmergencyRisk assessment

Identifiers

PMID39511963
PMCPMC11563726

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LicenceCC BY-NC-ND
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Registered trials

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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.