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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
背景：减少COVID-19传播的旅行相关策略随着对SARS-CoV-2和新的预防工具的理解的变化而迅速发展，诊断,和治疗。建模是研究不同疾病遏制策略可能发生的结果范围的重要方法。
方法：我们研究了2019年12月至2022年9月发表的43篇文章，这些文章使用建模来评估与旅行相关的COVID-19遏制策略。我们提取并综合了有关研究目标的数据，方法,结果，人口,设置，战略,和成本。我们使用标准化方法根据建模质量和严格性的26项标准来评估每个分析。
结果：最常用的方法包括检查检疫的隔室建模，隔离,或测试。在大流行的早期，目标是预防与旅行相关的COVID-19病例，重点关注个人层面的结果，并评估旅行限制等策略，未经测试的检疫，社交距离，和抵达PCR检测。在诊断测试和疫苗的发展之后，建模研究预测了人群水平的结局，并研究了这些工具来限制COVID-19的传播。很少有发表的研究包括快速抗原筛选策略，成本,显式模型校准，或对建模方法的批判性评估。
结论：未来的建模分析应利用开源数据，提高建模方法的透明度，纳入新的预防措施，诊断,和治疗，并包括成本和成本效益，以便建模分析可以提供信息，以解决未来值得关注的SARS-CoV-2变体和其他新兴传染病（例如，水痘和埃博拉病毒)用于与旅行相关的卫生政策。
方法：我们研究了2019年12月至2022年9月发表的43篇文章，这些文章使用建模来评估与旅行相关的COVID-19遏制策略。我们提取并综合了有关研究目标的数据，方法,结果，人口,设置，战略,和成本。我们使用标准化方法根据建模质量和严格性的26项标准来评估每个分析。
结果：最常用的方法包括检查检疫的隔室建模，隔离,或测试。在大流行的早期，目标是预防与旅行相关的COVID-19病例，重点关注个人层面的结果，并评估旅行限制等策略，未经测试的检疫，社交距离，和抵达PCR检测。在诊断测试和疫苗的发展之后，建模研究预测了人群水平的结局，并研究了这些工具来限制COVID-19的传播。很少有发表的研究包括快速抗原筛选策略，成本,显式模型校准，或对建模方法的批判性评估。
结论：未来的建模分析应利用开源数据，提高建模方法的透明度，纳入新的预防措施，诊断,和治疗，并包括成本和成本效益，以便建模分析可以提供信息，以解决未来值得关注的SARS-CoV-2变体和其他新兴传染病（例如，水痘和埃博拉病毒)用于与旅行相关的卫生政策。
