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.

当国际社会权衡必要程度的隔离和社交距离以对抗 COVID-19 的传播时，关键问题是如何通过这些措施减轻疾病传播。最近的预测表明，如果不采取认真的干预措施，世界很大一部分人口将受到感染，导致数百万人死亡。为了缓解这种最坏的情况，关键的政策决策正在以数学模型为指导。然而，一些著名的模型对人类接触做出了不切实际的假设，即一个人感染亲密家庭成员的可能性与感染国家另一边的陌生人的可能性相同。这种假设对于计算很有用，但未能考虑到疫情爆发的全部地理复杂性。此外，许多模型没有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描述隔离网络的更现实的结构以及疾病如何在其中传播，从而解决这些缺点。拟议的研究将使用有关接触网络的真实世界数据来为控制 COVID-19 爆发做出预测和建议，从而提高我们对如何最好地遏制当前和未来流行病的理解。该项目将涉及对本科生的培训。这项研究将描述隔离对连接和疾病传播的影响，该影响比之前考虑的更现实。特别重要的是确定关键阈值，一旦超过该阈值，就会导致大规模流行病的发生。最近有关于这些阈值的研究，但针对的是模拟数字基础设施和社交网络的网络。研究的第一个目标是确定有偏差的位点渗滤对图结构的影响，特别是不同的渗滤规则如何影响给定图的最大组件的大小。第二部分将重点关注 SIR 模型的流行病临界阈值和规模在渗透后如何变化。这将在从配置模型生成的图以及吉尔伯特图等随机空间网络上进行严格的探索。此外，这些问题将使用当前 COVID-19 大流行的特定数据在现实世界的面对面网络上进行调查。回答这些问题将有助于测试以前模型的稳健性，同时还探索更强有力的先发制人的距离的有效性。这笔赠款是使用分配给 MPS 的冠状病毒援助、救济和经济安全 (CARES) 法案补充资金提供的资金来授予的。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。