RAPID: Collaborative Research: Quarantined Networks and the Spread of COVID-19

RAPID：协作研究：隔离网络和 COVID-19 的传播

• 批准号: 2028880
• 负责人: Nicole Eikmeier
• 金额: $ 2.26万
• 依托单位: Grinnell College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028880&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Quarantined; Networks

As the global community weighs the necessary extent of quarantine and social distancing to fight the spread of COVID-19, the critical question is how disease transmission is mitigated by these measures. Recent predictions suggest that without serious interventions, a large portion of the world population will become infected, resulting in millions of deaths. To mitigate this worst-case scenario, key policy decisions are being guided by mathematical models. However, several prominent models make unrealistic assumptions about human contacts i.e., that an individual is equally likely to infect a close family member as a complete stranger on the other side of the country. Such assumptions are useful for calculations, but fail to take into account the full geographic complexity of the outbreak. Furthermore, many models do not consider the consequences of the quarantine of healthy individuals. This project will use rigorous analysis and simulation to address these shortcomings by describing a more realistic structure of quarantined networks and how disease spreads in them. The proposed research will use real-world data about contact networks to make predictions and recommendations for controlling the COVID-19 outbreak, improving our understanding of how best to contain the current as well as future pandemics. The project will involve the training of undergraduate students.This research will describe the effect of quarantine on connectivity and disease transmission on more realistic networks than have previously been considered. Of particular importance will be locating critical thresholds which, when exceeded, allow large epidemics to occur. There is recent study of these thresholds, but for networks that model digital infrastructure and social networks. The first objective of the research will be to determine the effect of biased site percolation on graph structure, especially how different percolation rules influence the size of the largest component of a given graph. The second part will then focus on how the critical threshold and size of the epidemic for an SIR model change after percolation. This will be explored rigorously on graphs generated from the configuration model as well as random spatial networks such as Gilbert graphs. Additionally, these questions will be investigated on real world face-to-face networks using data specific to the current COVID-19 pandemic. Answering them will help test robustness of previous models, while also exploring the effectiveness of stronger preemptive distancing.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.

随着全球社区权衡隔离和社会距离的必要范围，以与19covid-19的蔓延打击，关键的问题是这些措施如何减轻疾病的传播。最近的预测表明，如果没有严重的干预措施，世界上很大一部分人口将被感染，导致数百万死亡。为了减轻这种最坏的情况，关键政策决策以数学模型为指导。但是，几种突出的模型对人类的接触做出了不切实际的假设，即个人同样有可能将亲密的家庭成员感染为该国另一端的完全陌生人。此类假设对于计算很有用，但没有考虑到爆发的全部地理复杂性。此外，许多模型不考虑健康个体隔离的后果。该项目将使用严格的分析和模拟来解决这些缺点，以描述隔离网络的更现实结构以及疾病如何传播。拟议的研究将使用有关联系网络的现实世界数据，以做出控制Covid-19爆发的预测和建议，从而提高我们对如何最好地控制当前和未来大流行的理解。该项目将涉及本科生的培训。这项研究将描述隔离对连通性和疾病传播对更现实的网络的影响。特别重要的是，将定位关键阈值，该阈值超过，允许发生大型流行病。最近对这些阈值进行了研究，但对于对数字基础架构和社交网络进行建模的网络。研究的第一个目标是确定偏置位点渗透对图结构的影响，尤其是不同的渗透规则如何影响给定图的最大组成部分的大小。然后，第二部分将重点关注SIR模型渗透后SIR模型的临界阈值和大小如何变化。这将在配置模型以及随机空间网络（例如Gilbert图形）生成的图上进行严格探讨。此外，这些问题将在现实世界的面对面网络上使用特定于当前的Covid-19大流行的数据进行研究。回答它们将有助于测试以前模型的鲁棒性，同时还可以使用冠军，救济和经济安全和经济安全（CareS）ACT ACT补充资金授予该授予的资金。该奖项分配给了MPS.NSF的法定任务和审查局面，该奖项通过评估了Infection and Infectia and Infectia and Infectia and Infectia and Infectia and Infectia and Infectia and and Infectia。