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

最近的COVID-19大流行使人们迫切需要了解冠状病毒（SARS-CoV-2）的传播。这一病毒的特点和前所未有的全球应对提出了现代尚未观察到的疾病传播的潜在新途径。这些路径创造了不断演变的可观察属性的空间和时间模式，例如感染人数或与病毒相关的死亡人数。利用统计物理和网络科学的工具，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有助于构建计算模型，这些模型将试图从隔离措施、此类措施的时间和环境条件方面解释这些模式。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。这个项目的目的是使这些数学和计算工具在下一个可能爆发的浪潮中随时可用，并能够立即分析流行病学数据。这项研究的结果有可能提供高风险地理区域的早期发现，从而为决策者的决策提供信息。该项目也为STEM研究人员的培训提供了重要的机会。重点研究疫情特征在不同地区间的尺度和相关行为。尺度分析将研究观察到的量在不同的时间和空间尺度下是如何变化的，以幂律指数为特征。不同的指数表明不同的传播模式，这可以提供有关检测地点和时间以及检疫措施执行的有效性的信息。同样，空间相关程度及其时间演变是扩展模式的关键指标。对相关长度和相关时间的分析可以表明病毒传播过程离临界状态有多近。这一点尤其重要，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，以检验人口和环境因素对经验数据的影响。该RAPID奖由环境生物学部传染病生态和进化项目颁发，资金来自《冠状病毒援助、救济和经济安全法案》。该奖项反映了美国国家科学基金会的法定使命，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。

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.