Evidence map›Paper›PMID 39187511›Full record

ArticleNature communications2024

Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles.

Yong Dam Jeong, William S Hart, Robin N Thompson, Masahiro Ishikane, Takara Nishiyama, Hyeongki Park, Noriko Iwamoto, Ayana Sakurai, Michiyo Suzuki, Kazuyuki Aihara and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  5. Review
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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

16 authors.

Yong Dam JeongInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.ORCID 0000-0002-3970-7690
William S Hart *Interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.ORCID 0000-0002-2504-6860
Robin N Thompson *Mathematical Institute, University of Oxford, Oxford, OX2 6GG, UK.ORCID 0000-0001-8545-5212
Masahiro Ishikane *Disease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan.
Takara NishiyamaInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
Hyeongki ParkInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
Noriko IwamotoDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan.
Ayana SakuraiDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan.
Michiyo SuzukiDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan.
Kazuyuki AiharaInternational Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-4602-9816
Koichi WatashiResearch Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan.ORCID 0000-0002-4536-9966
Eline Op de CoulCentre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.ORCID 0000-0002-8263-9187
Norio OhmagariDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan.ORCID 0000-0002-4622-8970
Jacco WallingaCentre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.ORCID 0000-0003-1725-5627
Shingo IwamiInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan. iwami.iblab@bio.nagoya-u.ac.jp.ORCID 0000-0002-1780-350X
Fuminari MiuraCentre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. fuminari.miura@rivm.nl.ORCID 0000-0001-8235-757X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles - specifically, when infected individuals cease to be infectious - based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7-10.5) after symptom clearance and 5.4% (95% CI: 4.1-6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.

Indexed as

Disease OutbreaksMpox, MonkeypoxHumansPatient IsolationViral LoadVirus Shedding

Identifiers

PMID39187511
PMCPMC11347573

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.