RAPID: Collaborative Research: Metapopulation Modeling to Develop Strategies to Reduce COVID-19 Transmission in Public Spaces

新冠肺炎疫情对公共和私人机构安全地重新开放包括工作场所和学校在内的公共空间提出了前所未有的挑战。然而，鉴于一种高度可传播但不可见的病原体，我们几乎没有关于如何管理共享空间的使用的指导意见。这个项目的基本目的是更好地了解SARS-CoV-2如何在建筑环境中传播。通过数学建模产生的预测将使用替代的非致病病毒进行实验测试。该项目提出了一种新的范式，其中感染个体存在的可能性、病毒传播的数量和方式、病毒在一段时间内的位置以及位置的使用需求为公共空间占用和使用情况的评估提供了重大进展。最终目标是发展能够限制病毒传播和应对当前对公共卫生的世界性挑战的做法。建议将类似于已建立的建筑和消防规范，该规范根据设计和使用要求规定如何为每位居住者分配空间；我们的分析将生成一个可以推广的“COVID代码”，以供未来疫情爆发时使用。本研究还将为学生和博士后学者提供培训机会。最近开发的应用集合种群理论解释病原体如何在医院环境中持续存在的计算模型(短暂岛屿集合种群模型(EIMM))将被修订，以解决SARS-CoV-2在建筑环境中的空间传播。EIMM将建筑环境的各个方面定义为不同的宜居居住区(“DEME”)，其方式与考虑人类宿主的方式大致相同，但这些DEME有自己的生物学参数，与SARS-CoV-2的生存和传播有关。病原菌的数量和大小被用来模拟病原体种群的大小和位置，使用与生态相关的参数，如生长速度、种群大小和携带能力，而不是仅仅使用人类宿主。一个被包裹的噬菌体phi6将被用来验证模型的预期以及在真实环境中的测试控制策略，例如教室。目标是测试EIMM建议的哪些干预措施将噬菌体phi6在共享空间中传播的机会降至最低，这些信息可以被调整以提供各种干预措施如何影响SARS-CoV-2持久性和传播的估计。这一快速奖项由环境生物学部门传染病生态和进化计划利用冠状病毒援助、救济和经济安全法案(CARE)的资金颁发。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。

The COVID-19 pandemic presents an unprecedented challenge to public and private institutions to safely reopen public spaces, including workspaces and schools. However, we have little guidance on how to manage the use of shared spaces in light of a highly transmissible, but invisible, pathogen. The fundamental aim of this project is to better understand how SARS-CoV-2 spreads in built environments. Predictions generated by mathematical modeling will be experimentally tested using a surrogate non-pathogenic virus. This project presents a new paradigm where the likelihood of infected individuals being present, the amount and manner of viral shedding, the locations of viruses over time, and the usage-needs of a location provide for a major advancement in the assessment of public space occupancy and usage. The ultimate goal is to develop practices capable of limiting virus transmission and meeting the current worldwide challenge to public health. Recommendations will resemble established building and fire codes, which regulate how space is allotted per occupant based upon design and usage requirements; our analyses will generate a “COVID Code” that can be generalized for use during future outbreaks. This research will also provide training opportunities for students and postdoctoral scholars. A recently developed computational model (the Ephemeral Island Metapopulation Model (EIMM)) that applies metapopulation theory to explain how pathogens persist in hospital environments will be revised to address the spatial spread of SARS-CoV-2 within built environments. The EIMM defines aspects of the built environment as distinct habitable zones of occupancy (“demes”) in much the same manner as human hosts are considered, but these demes have their own biological parameters relevant to the survival and transmission of SARS-CoV-2. The number and size of both living and non-living demes, instead of human hosts alone, are used to model size and location of pathogen populations using ecologically relevant parameters, such as growth rate, population size, and carrying capacity. An enveloped bacteriophage phi6 will be used to validate model expectations as well as test control strategies in real environments such as classrooms. The goal is to test which interventions suggested by the EIMM minimize opportunities for phage phi6 spread in shared spaces, and this information can be adapted to provide estimates of how various interventions would affect SARS-CoV-2 persistence and transmission.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.