Evidence map›Paper›PMID 26908522›Full record

ArticleBMJ open2016

Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis.

Hiroshi Nishiura, Akira Endo, Masaya Saitoh, Ryo Kinoshita, Ryo Ueno, Shinji Nakaoka, Yuichiro Miyamatsu, Yueping Dong, Gerardo Chowell, Kenji Mizumoto

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
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  5. Article
  6. Highly pathogenic coronaviruses and the kidney.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022
    Review
  7. Article
  8. Article
  9. Article
  10. Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models.International journal of environmental research and public health · 2020
    Article
  11. Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. MERS coronavirus outbreak: Implications for emerging viral infections.Diagnostic microbiology and infectious disease · 2019
    Review
  17. Review
  18. Article
  19. Article
  20. 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

10 authors.

Hiroshi NishiuraInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan.
Akira EndoInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masaya SaitohInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan The Institute of Statistical Mathematics, Tokyo, Japan.
Ryo KinoshitaInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan.
Ryo UenoInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Shinji NakaokaInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan.
Yuichiro MiyamatsuInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan.
Yueping DongInfectious Disease Epidemiology team, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan CREST, Japan Science and Technology Agency, Saitama, Japan.
Gerardo ChowellSchool of Public Health, Georgia State University, Atlanta, Georgia, USA Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA.
Kenji MizumotoCREST, Japan Science and Technology Agency, Saitama, Japan Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.

Funding

Biotechnology and Biological Sciences Research Council BB/M008894/1
6 · The paper itself

Abstract

objectivesTo investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.

designRetrospective epidemiological analysis.

settingMultiple healthcare facilities of secondary and tertiary care centres in an urban setting.

participantsA total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection. PRIMARY AND SECONDARY OUTCOME MEASURES: Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.

resultsA total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0-8.6) and 28.6 (0.0-63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.

conclusionsSuperspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.

Indexed as

Contact TracingCoronavirus InfectionsCross InfectionDisease OutbreaksHumansRepublic of KoreaRetrospective StudiesEPIDEMIOLOGY

Identifiers

PMID26908522
PMCPMC4769415

What OpenQuestion holds

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LicenceCC BY-NC
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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.