RAPID: Modeling of COVID-19 transmission in cruise ships and evaluating the impact of mitigation measures

该项目的重点是模拟新冠肺炎在邮轮上的传播，并评估社区指南中常用的干预措施的影响，如掩蔽和疏远。在新冠肺炎大流行期间，许多旅行和接待服务严重中断，因为人口稠密的聚集环境导致病毒在不同地理区域和亚人群的个人之间传播。为控制疾病传播并确保旅行者和机组人员的安全，以及他们在旅行期间和旅行结束后与之交往的民众的安全，制定和执行指导方针是一项持续的努力。了解哪些干预措施组合在预防或减缓疾病传播方面最有效，将有助于为邮轮旅行以及新冠肺炎和其他传染病的类似近距离护理环境提供数据驱动的建议。此外，该项目将支持对公共卫生职业感兴趣的研究生的培养。该项目的目标包括：(I)考虑到乘客和船员之间独特的互动动态，开发数学模型来分析新冠肺炎在邮轮上的传播。(2)通过测试各种模型情景，评估各种干预措施的有效性，包括以前从未使用过的干预措施，包括单独或组合使用的干预措施。该结果将为公共卫生政策和决策者提供见解和建议，建模框架可以为依赖类似数据流的其他建模活动提供信息。该项目是与疾病控制和预防中心合作资助的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project focuses on modeling the spread of COVID-19 in cruise ships and evaluating the impact of interventions commonly used in community guidelines, such as masking and distancing. Many travel and hospitality services have been heavily disrupted during the COVID-19 pandemic, as the densely populated congregated settings contribute to virus spread across individuals from various geographic areas and subpopulations. The development and implementation of guidelines to control the spread of disease and ensure the safety of travelers and crew members, as well as the populations they interact with during and after completing their travels, has been an ongoing effort. The understanding of what combinations of interventions are most effective in preventing or slowing down the disease spread will facilitate data-driven recommendations for cruise ship travel and for similar close-quarters settings for COVID-19 and other infectious diseases. In addition, this project will support the training of graduate students who are interested in careers in public health.The objectives of this project include: (i) Developing mathematical models to analyze the spread of COVID-19 on cruise ships, considering the unique interaction dynamics among passengers and crew. (ii) Evaluating the effectiveness of various interventions, individually or in combination, including ones not previously used, by testing a wide range of model scenarios. The results will provide insights and recommendations for public health policy and decision-makers and the modeling framework can inform other modeling activities that rely on similar data streams.This project was funded in collaboration with the Centers for Disease Control and Prevention.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.