Evidence map›Paper›PMID 34429188›Full record

ReviewEmerging infectious diseases2021

Policy Review and Modeling Analysis of Mitigation Measures for Coronavirus Disease Epidemic Control, Health System, and Disease Burden, South Korea.

Hae-Young Kim, In-Hwan Oh, Jacob Lee, Jeong-Yeon Seon, Woo-Hwi Jeon, Jae Seok Park, Sung-Il Nam, Niket Thakkar, Prashanth Selvaraj, Jessica McGillen and 4 more

Open access · diamondAbstract readReview
In one paragraph

Review in Emerging infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

14 authors at 4 institutions in 2 countries.

Hae-Young Kim
In-Hwan Oh
Jacob Lee
Jeong-Yeon Seon
Woo-Hwi Jeon
Jae Seok Park
Sung-Il Nam
Niket Thakkar
Prashanth Selvaraj
Jessica McGillen
Daniel Klein
Scott Braithwaite
Anna Bershteyn
Seung Heon Lee
Institute for Disease Modeling · USNew York University · USAnsan University · KRKeimyung University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We reviewed the timeline of key policies for control of the coronavirus disease epidemic and determined their impact on the epidemic and hospital burden in South Korea. Using a discrete stochastic transmission model, we estimated that multilevel policies, including extensive testing, contact tracing, and quarantine, reduced contact rates by 90% and rapidly decreased the epidemic in Daegu and nationwide during February‒March 2020. Absence of these prompt responses could have resulted in a >10-fold increase in infections, hospitalizations, and deaths by May 15, 2020, relative to the status quo. The model suggests that reallocation of persons who have mild or asymptomatic cases to community treatment centers helped avoid overwhelming hospital capacity and enabled healthcare workers to provide care for more severely and critically ill patients in hospital beds and negative-pressure intensive care units. As small outbreaks continue to occur, contact tracing and maintenance of hospital capacity are needed.

Indexed as

COVID-19EpidemicsCost of IllnessHumansPolicyRepublic of KoreaSARS-CoV-2coronavirus diseasecoronavirusesCOVID-19epidemic modelinfection controlmathematical modellingrespiratory infectionsSARS-CoV-2severe acute respiratory syndrome coronavirus 2South Koreaviruseszoonoses

Identifiers

PMID34429188
PMCPMC8544960
OpenAlexW3193818097

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.