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

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.

Figure 1.. Flow chart of the study selection.

This figure summarizes the search and selection process for the selected 43 modeling articles. We first identified 720 articles from PubMed. After removing 207 duplications, we assessed the titles and abstracts of the remaining 513 articles and excluded 349 articles based on the inclusion criteria (1) the title or abstract included the term, “model(s/ing)”. We closely reviewed the abstracts of the remaining 164 articles and excluded 97 articles based on the inclusion/exclusion criteria. Next, we reviewed the full text of the remaining 67 articles and excluded 29 articles with reasons. We selected 43 articles for inclusion with additional five articles based on recommendations and review of the bibliographies of the other articles.

Figure 2.. Critical evaluation of the 43 modeling studies using standardized 26 criteria.

The critical evaluation of 43 modeling studies used prespecified 26 criteria in four areas to assess the transparency and rigor of modeling approaches [9]: (1) Modeling Development (D): problem definition, modeling objective, model scope, stakeholder engagement, modeling method, model conceptualization, high-level model visualization, model equations, parameter values and data sources, model assumptions, modeling code availability, and software used; (2) Modeling Testing (T): evaluation and testing, model calibration, and quality of calibration fit; (3) Modeling Analysis (A): discussion about strategies and policies, report of quantitative results, structural insights, input sensitivity analysis, and output sensitivity analysis; (4) Other Qualifications (O): comparison with other results, generalizability discussion, limitations discussion, reproducibility discussion, sources of funding, conflicts of interest.