Evidence map›Paper›PMID 34438256›Full record

ArticleEpidemics2021

Dynamic network strategies for SARS-CoV-2 control on a cruise ship.

Samuel M Jenness, Kathryn S Willebrand, Amyn A Malik, Benjamin A Lopman, Saad B Omer

Abstract read
In one paragraph

Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. A review of COVID-19 transmission dynamics and clinical outcomes on cruise ships worldwide, January to October 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022
    Review
  7. Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Samuel M JennessEmory University Rollins School of Public Health, Atlanta, GA, 30322, USA. Electronic address: samuel.m.jenness@emory.edu.
Kathryn S WillebrandYale Institute of Global Health, New Haven, CT, 06510, USA; Yale School of Public Health, New Haven, CT, 06510, USA.
Amyn A MalikYale Institute of Global Health, New Haven, CT, 06510, USA; Yale School of Medicine, New Haven, CT, 06510, USA.
Benjamin A LopmanEmory University Rollins School of Public Health, Atlanta, GA, 30322, USA.
Saad B OmerYale Institute of Global Health, New Haven, CT, 06510, USA; Yale School of Public Health, New Haven, CT, 06510, USA; Yale School of Medicine, New Haven, CT, 06510, USA; Yale School of Nursing, Orange, CT, 06477, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
EpiModel 2.0: Integrated Network Models for HIV/STI Prevention ScienceR01AI138783 · NIAID · EMORY UNIVERSITY · PI JENNESS, SAMUEL · 2018 to 2021
$3.0M
Comprehensive Profiling of Social Mixing Patterns in Resource Poor CountriesR01HD097175 · NICHD · EMORY UNIVERSITY · PI LOPMAN, BENJAMIN A, OMER, SAAD B. · 2019 to 2023
$2.6M
Preparing for the SARS-CoV2 vaccine: Modeling of transmission pathways, viral evolution and vaccination strategiesR01GM124280 · NIGMS · EMORY UNIVERSITY · PI LOPMAN, BENJAMIN A · 2018 to 2022
$2.2M
NCATS NIH HHS UL1 TR001863NIAID NIH HHS R01 AI138783NICHD NIH HHS R01 HD097175NIGMS NIH HHS R01 GM124280
6 · The paper itself

Abstract

SARS-CoV-2 outbreaks have occurred on several nautical vessels, driven by the high-density contact networks on these ships. Optimal strategies for prevention and control that account for realistic contact networks are needed. We developed a network-based transmission model for SARS-CoV-2 on the Diamond Princess outbreak to characterize transmission dynamics and to estimate the epidemiological impact of outbreak control and prevention measures. This model represented the dynamic multi-layer network structure of passenger-passenger, passenger-crew, and crew-crew contacts, both before and after the large-scale network lockdown imposed on the ship in response to the disease outbreak. Model scenarios evaluated variations in the timing of the network lockdown, reduction in contact intensity within the sub-networks, and diagnosis-based case isolation on outbreak prevention. We found that only extreme restrictions in contact patterns during network lockdown and idealistic clinical response scenarios could avert a major COVID-19 outbreak. Contact network changes associated with adequate outbreak prevention were the restriction of passengers to their cabins, with limited passenger-crew contacts. Clinical response strategies required for outbreak prevention may be infeasible in many cruise settings: early mass screening with an ideal PCR test (100 % sensitivity) and immediate case isolation upon diagnosis. Personal protective equipment (e.g., facemasks) had limited impact in this environment because the majority of transmissions after the ship lockdown occurred between passengers in cabins where masks were not consistently used. Public health restrictions on optional leisure activities like these should be considered until longer-term effective solutions such as a COVID-19 vaccine become widely available.

Indexed as

COVID-19ShipsCommunicable Disease ControlCOVID-19 VaccinesHumansSARS-CoV-2COVID-19 VaccinesMathematical modelNetworkNetwork-based modelPrevention strategiesSARS-CoV-2

Identifiers

PMID34438256
PMCPMC8372454

What OpenQuestion holds

Textmetadata
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.