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; Emily P Hyle

doi:10.1016/j.tmaid.2024.102730

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

Travel Med Infect Dis. 2024 Nov-Dec:62:102730. doi: 10.1016/j.tmaid.2024.102730. Epub 2024 Jun 1.

Authors

Affiliations

¹ Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA. Electronic address: skoiso@mgh.harvard.edu.
² Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
³ Medical 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.
⁴ Medical 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.
⁵ Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St., Suite, 1010, Boston, MA, USA.
⁶ Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, USA.
⁷ 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; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA.
⁸ 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.
⁹ Medical 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.

PMID: 38830442
PMCID: PMC11606784 (available on 2025-11-01)
DOI: 10.1016/j.tmaid.2024.102730

Abstract

Background: Travel-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.

Methods: We 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.

Results: The 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.

Conclusion: Future 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.

Keywords: COVID-19; Decision analysis; Modeling; Public health policies; Travel.

Publication types

Review

MeSH terms

COVID-19* / epidemiology
COVID-19* / prevention & control
Communicable Disease Control / methods
Health Policy
Humans
Models, Theoretical
Quarantine*
SARS-CoV-2*
Travel*

Grants and funding

U01 CK000633/CK/NCEZID CDC HHS/United States