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RAPID:NSF-BSF: Analysis of the spreading patterns and the efficiency of quarantine measures for COVID-19, based on available world-wide spatio-temporal data

RAPID：NSF-BSF：根据可用的全球时空数据，分析 COVID-19 的传播模式和检疫措施的效率

基本信息

批准号：
2035297
负责人：
Lazaros Gallos
金额：
$ 19.71万
依托单位：
Rutgers University New Brunswick
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：
2020-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035297&HistoricalAwards=false
关键词：
RAPID NSF BSF Analysis spreading

项目摘要

The recent COVID-19 pandemic has created an urgent need to understand the spread of coronavirus (SARS-CoV-2). The special features of this virus and the unprecedented global response present potentially novel paths of disease transmission that have not been observed in modern times. These paths create continuously evolving spatial and temporal patterns of observable properties, such as number of infections or virus-related deaths. Using tools from statistical physics and network science, this project seeks to understand the special features of this complex behavior and to compare these patterns in different geographical areas. The availability of high-resolution data will enable the construction of computational models which will attempt to explain these patterns in terms of quarantine measures, timing of such measures, and environmental conditions. Such an understanding could assess the efficiency of quarantine measures implemented around the world and also identify the effects of temperature and humidity. This project aims to make these mathematical and computational tools be readily available at a next possible outbreak wave and to allow immediate analysis of epidemiological data. The outcome of this research has the potential of providing early detection of geographical areas in high risk, informing thus the decisions of policy makers. This project also provides significant opportunities for the training of STEM researchers.The research will focus on the scaling and correlation behavior of epidemic characteristics between different areas. The scaling analysis will study how the observed quantities change for different scales of time and space, characterized by power-law exponents. Different exponents indicate different propagation patterns which can provide information about the effectiveness of location and timing of tests and enforcement of quarantine measures. Similarly, the extent of spatial correlations and their time evolution are key indicators of spreading patterns. The analysis of the correlation lengths and correlation times can indicate how close the virus spreading process is to criticality. This is particularly important because critical systems are in general much more vulnerable to rapid spreading. The data analysis will be complemented by appropriate mathematical and simulation epidemic models to test the influence of population and environmental factors on the empirical data.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.

最近的COVID-19大流行迫切需要了解冠状病毒（SARS-CoV-2）的传播。这种病毒的特殊性和前所未有的全球反应可能提供现代尚未观察到的新的疾病传播途径。这些路径创造了可观察属性的不断演变的空间和时间模式，例如感染或病毒相关死亡的数量。利用统计物理学和网络科学的工具，该项目试图了解这种复杂行为的特殊特征，并在不同的地理区域比较这些模式。高分辨率数据的可用性将使计算模型的建设，将试图解释这些模式的检疫措施，这些措施的时间，和环境条件。这种了解可以评估世界各地实施的检疫措施的效率，并确定温度和湿度的影响。该项目旨在使这些数学和计算工具在下一次可能爆发时随时可用，并允许立即分析流行病学数据。这项研究的结果有可能提供早期发现的地理区域的高风险，从而告知决策者的决定。该项目还为STEM研究人员的培训提供了重要机会。该研究将侧重于不同地区之间流行特征的标度和相关行为。标度分析将研究观测量在不同的时间和空间尺度上如何变化，其特征是幂律指数。不同的指数表明不同的传播模式，可以提供有关检测地点和时间以及检疫措施执行情况的信息。同样，空间相关性的程度及其时间演变是传播模式的关键指标。相关长度和相关时间的分析可以指示病毒传播过程接近临界的程度。这一点特别重要，因为关键系统通常更容易受到快速传播的影响。数据分析将辅以适当的数学和模拟流行病模型，以测试人口和环境因素对经验数据的影响。这个RAPID奖是由环境生物学部传染病生态学和进化计划颁发的，使用的资金来自冠状病毒援助，救济，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。