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

• Published in: Travel Medicine and Infectious Disease
• Main Authors: 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
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2024
• Subjects: COVID-19; Modeling; Travel; Public health policies; Decision analysis; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1016/j.tmaid.2024.102730; https://doaj.org/article/f1330bd5633946fd817c6e74e0c3de22

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.