RAPID: Scaling, causality, and modulation of the spread of COVID19

RAPID：COVID19 传播的规模、因果关系和调节

• 批准号: 2028271
• 负责人: Michel Boufadel
• 金额: $ 20万
• 依托单位: New Jersey Institute of Technology
• 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: 2020-04-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028271&HistoricalAwards=false
• 关键词: RAPID; Scaling; causality; modulation; spread

Models of the spreading of the COVID-19 virus have yielded conflicting predictions for the value and time of the peak occurrence of cases. Most models tend to be one dimensional exponential growth, and thus do not account for spatial correlation. Other models are based on neural networks, which are capable of predicting if sufficient data are available, which is not at this instant of time the case for COVID-19. This study will use multifractals, which result from multiplicative processes with spatial correlations. Multifractals, due to their lack of a characteristic scale, may be ideal tools to analyze the spread of viruses, such as COVID-19; the spreading in a large city such as NYC could be similar to that occurring in a small city such as Newark, NJ. Multifractals have been used largely for geophysical data by various groups, and in some cases, to understand the spread of viruses, such as H1N5. This study will use data from the five boroughs of New York City and from Northern New Jersey (namely Bergen, Essex, and Union County). The team already has been collecting data from these communities in a project on community resilience. The hypothesis of this research is that multifractals can reflect both the scaling behavior and the exponential increase with time. Multifractals also account for the spatial correlation between subjects, and thus could be used to explain connectivity, be it at the individual level or at the level of cities (say NYC and Philadelphia). In addition, a look at the map of cases at the US (or the world scale) reveals spottiness, that is hot zones that are not uniformly distributed in space. The study team believes that the skeleton of the geometry is fractal (because of the lack of scale), and thus will be analyzing multifractals distributed spatially on a fractal network. This approach may open new modes of investigation in various areas, including public health and resilience.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.

COVID-19病毒传播的模型对病例发生高峰的数值和时间产生了相互矛盾的预测。大多数模型倾向于一维指数增长，因此不考虑空间相关性。其他模型基于神经网络，能够预测是否有足够的数据可用，而目前COVID-19的情况并非如此。这项研究将使用多重分形，这是由于空间相关性的乘法过程。由于缺乏特征尺度，多重分形可能是分析病毒传播的理想工具，如COVID-19;在纽约这样的大城市中的传播可能与在新泽西州纽瓦克这样的小城市中发生的传播相似。多重分形已被各种团体广泛用于地球物理数据，在某些情况下，用于了解病毒的传播，如H1 N5。本研究将使用来自纽约市五个行政区和北方新泽西（即卑尔根、埃塞克斯和联合县）的数据。该团队已经在一个关于社区复原力的项目中收集了这些社区的数据。本研究的假设是多重分形既能反映标度行为，又能反映随时间的指数增长。多重分形也解释了主体之间的空间相关性，因此可以用来解释连接性，无论是在个人层面还是在城市层面（比如纽约和费城）。此外，看看美国（或世界范围内）的病例地图，就会发现斑点，即在空间上分布不均匀的热区。研究小组认为，几何的骨架是分形的（因为缺乏尺度），因此将分析分形网络上空间分布的多重分形。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。