RAPID: The effect of contact network structure on the spread of COVID-19: balancing disease mitigation and socioeconomic well-being

是什么让新冠肺炎在一些地方传播得很快，而在另一些地方传播得很慢？社会应该如何在减少社会距离的同时限制感染的增加？为了回答这些问题，这个快速反应研究(RAPID)项目试图了解社会联系网络中的人际互动模式(“结构”)如何影响疾病在人群中的传播。研究人员将模拟一种通过各种社会接触网络传播的疾病，并使用机器学习将每个网络的结构与新感染的数量和时间联系起来。通过限制与疾病增加相关的结构，社会可能能够重新开放其经济的其他部分，同时仍能遏制疾病的整体传播。研究人员将为公众和决策者制作一个互动的网络应用程序，以可视化减少疾病和保持社会凝聚力之间的权衡。这项研究将为早期职业科学家的职业发展提供支持。本研究旨在基于接触网络结构确定SARS-CoV-2传播的内在风险。研究人员将使用机器学习来1)识别影响疾病传播的网络结构，2)在经验接触网络上预测疾病传播。重要的网络结构将作为模拟减轻疾病干预措施的目标(例如，减少增加疾病水平的结构或增加降低疾病水平的结构)。最后，研究人员将调查未来新冠肺炎或其他疾病的爆发是否可以通过提前优化社会联系网络来缓解。这项研究的成果将为快速、高效的干预措施提供参考和便利，以降低新冠肺炎的社会和经济成本。这项研究将开发一个将疾病与网络结构联系起来的一般框架。因此，可以将结果推广到当前的大流行之外，有助于进一步了解未来潜在的新冠肺炎浪潮以及人、牲畜和野生动物中的其他直接传播疾病。这一快速奖项由环境生物学部门的传染病生态学和进化计划颁发，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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

What makes COVID-19 spread rapidly in some places, yet slowly in others? How should society lessen social distancing while limiting an increase in infections? To answer these questions, this Rapid Response Research (RAPID) project seeks to understand how patterns of interpersonal interaction (“structure”) in social contact networks affect disease spread in a population. The researchers will simulate a disease spreading through a variety of social contact networks, and use machine learning to relate each network’s structure to the number and timing of new infections. By limiting structures related to increased disease, societies may be able to reopen other parts of their economies while still curbing overall disease spread. The researchers will produce an interactive web application for the public and decision-makers to visualize trade-offs between reducing disease and maintaining social cohesion. This research will support the professional development of an early career scientist.This research aims to determine the inherent risk of SARS-CoV-2 spread based on contact network structure. The researchers will use machine learning to 1) identify network structures that influence disease spread and 2) predict disease spread on empirical contact networks. Important network structures will serve as targets for simulated disease mitigation interventions (e.g. reducing structures that increase levels of disease or increasing structures that reduce disease levels). Finally, the researchers will investigate whether future outbreaks of COVID-19 or other diseases could be alleviated through optimizing social contact networks ahead of time. The outcomes of this research will inform and facilitate quick, efficient interventions to reduce the social and economic costs of COVID-19. This research will develop a general framework for relating disease to network structure. Thus, results can be generalized beyond the current pandemic, serving to further our understanding of potential future waves of COVID-19, as well as other directly-transmitted diseases in humans, livestock, and wildlife.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) ActThis 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.