Evidence map›Paper›PMID 32777227›Full record

ArticleMathematical biosciences2020

Minimizing disease spread on a quarantined cruise ship: A model of COVID-19 with asymptomatic infections.

Berlinda Batista, Drew Dickenson, Katharine Gurski, Malick Kebe, Naomi Rankin

Abstract read
In one paragraph

Article in Mathematical biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. A close contact identification algorithm using kernel density estimation for the ship passenger health.Journal of King Saud University. Computer and information sciences · 2023
    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

5 authors.

Berlinda BatistaHoward University, Washington, DC, USA.
Drew DickensonHoward University, Washington, DC, USA.
Katharine GurskiHoward University, Washington, DC, USA. Electronic address: kgurski@howard.edu.
Malick KebeHoward University, Washington, DC, USA.
Naomi RankinHoward University, Washington, DC, USA.

Funding

National Science Foundation DMS-1814659
6 · The paper itself

Abstract

On February 5 the Japanese government ordered the passengers and crew on the Diamond Princess to start a two week quarantine after a former passenger tested positive for COVID-19. During the quarantine the virus spread rapidly throughout the ship. By February 20, there were 651 cases. We model this quarantine with a SEIR model including asymptomatic infections with differentiated shipboard roles for crew and passengers. The study includes the derivation of the basic reproduction number and simulation studies showing the effect of quarantine with COVID-19 or influenza on the total infection numbers. We show that quarantine on a ship with COVID-19 will lead to significant disease spread if asymptomatic infections are not identified. However, if the majority of the crew and passengers are immune or vaccinated to COVID-19, then quarantine would slow the spread. We also show that a disease similar to influenza, even with a ship with a fully susceptible crew and passengers, could be contained through quarantine measures.

Indexed as

Asymptomatic InfectionsBetacoronavirusQuarantineShipsTravelBasic Reproduction NumberClinical Laboratory TechniquesCommunicable Diseases, ImportedComputer SimulationCoronavirus InfectionsCOVID-19COVID-19 TestingHumansInfluenza, HumanJapanMathematical ComputingAsymptomaticCOVID-19Cruise shipFluIsolationQuarantineReproduction numberSEIRSensitivity

Identifiers

PMID32777227
PMCPMC7413099

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.