Evidence map›Paper›PMID 38830442›Full record

ArticleTravel medicine and infectious disease

Modeling approaches to inform travel-related policies for COVID-19 containment: A scoping review and future directions.

Satoshi Koiso, Eren Gulbas, Lotanna Dike, Nora M Mulroy, Andrea L Ciaranello, Kenneth A Freedberg, Mohammad S Jalali, Allison T Walker, Edward T Ryan, Regina C LaRocque and 1 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Travel medicine and infectious disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Satoshi KoisoMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA. Electronic address: skoiso@mgh.harvard.edu.
Eren GulbasMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
Lotanna DikeMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
Nora M MulroyMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
Andrea L CiaranelloMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA.
Kenneth A FreedbergMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA.
Mohammad S JalaliHarvard Medical School, 25 Shattuck Street, Boston, MA, USA; Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St., Suite, 1010, Boston, MA, USA.
Allison T WalkerDivision of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, USA.
Edward T RyanHarvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA.
Regina C LaRocqueHarvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA.
Emily P HyleMedical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA. Electronic address: ehyle@mgh.harvard.edu.

Funding

NCEZID CDC HHS U01 CK000633
6 · The paper itself

Abstract

backgroundTravel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies.

methodsWe examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor.

resultsThe most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches.

conclusionFuture modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.

Indexed as

COVID-19QuarantineSARS-CoV-2TravelCommunicable Disease ControlHealth PolicyHumansModels, TheoreticalCOVID-19Decision analysisModelingPublic health policiesTravel

Identifiers

PMID38830442
PMCPMC11606784

What OpenQuestion holds

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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.