RAPID/Collaborative Research: Agent-based Modeling Toward Effective Testing and Contact-tracing During the COVID-19 Pandemic

这笔快速反应研究(RAPID)赠款将支持一些研究，这些研究将提高我们对新冠肺炎传播的了解，并在城市层面上制定潜在的缓解战略，促进科学进步，为国家健康和繁荣做出贡献。随着新冠肺炎的持续传播，不同测试策略和预测模型的有效性受到质疑。测试策略包括使用在其他地方取得成功但可能被证明对老年人和低收入群体不切实际的免下车设施，以及使用医院，这会进一步增加医疗体系的负担，并可能带来更高的传染风险。预测疾病传播的数学模型对于让当地和全球政策制定者了解应该采取哪些行动来缓解疫情和减轻人口压力至关重要。然而，这类模型经常被早期没有症状、复杂的活动模式和有限的测试资源所混淆。该奖项支持对数学模型的基础研究，该模型将通过动力学和控制方面的进步来克服这些混杂因素。通过明确地对社会和流动限制进行建模，这项研究将有助于提高社区的总体福祉，缩小人口之间的差距。该模型将模拟关键的假设情景，并将评估不同的测试政策和缓解行动，从而为参与遏制和根除这一流行病的决策者提供宝贵的支持。研究成果将向公众呈现，包括卫生专业人员和当局，以在持续的危机中为公共政策提供信息。该研究将通过基于精细解析代理和数据驱动的模型实时应对新冠肺炎爆发，旨在为这种病毒在城市层面的传播和潜在的缓解策略提供前所未有的洞察。这一方法将对正在实施和可能实施的缓解战略的有效性进行彻底的假设分析。基于代理的模型将包括新冠肺炎的特定功能，例如测试的类型和时间、无症状发生和住院阶段。该框架将以纽约州新罗谢尔的公开人口普查和地理参考数据为基础。与理性和非理性因素相关联的社会行为将被包括在基于代理的模型的多时空尺度上的移动模式中，以增加预测的粒度。网络理论和数据驱动的控制策略将根据在测试地点收集的现有联系数据库，向涉及主动试验的增强测试协议提供信息。该奖项反映了NSF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。

This Rapid Response Research (RAPID) grant will support research that will improve our understanding of the spread of COVID-19 and potential mitigation strategies at the city level, promoting scientific progress and contributing to national health and prosperity. As COVID-19 continues to spread, the effectiveness of different testing strategies and predictive models are brought into question. Testing strategies include the use of drive-through facilities that have found success elsewhere but may prove impractical for elderly and low-income sections of the population, and the use of hospitals, which adds further burden to the healthcare system and may carry the risk of higher contagion. Mathematical models that forecast the spread of the disease are of paramount importance to inform local and global policy makers on the course of action that should be undertaken to mitigate the outbreak and give relief to the population. However, such models are often confounded by the absence of symptoms in early stages, complex mobility patterns, and limited testing resources. This award supports fundamental research toward a mathematical model that will overcome these confounding factors, through advancements in dynamics and control. By explicitly modeling social and mobility constraints, this research will help increase the general well-being of communities and reduce disparities across the population. The model will afford the simulation of critical what-if scenarios and will include the evaluation of different testing policies and mitigation actions, thereby constituting a valuable support to policy makers involved in the containment and eradication of the epidemic. Research outcomes will be presented to the public, including health professionals and authorities to inform public policy in the ongoing crisis.The research will respond to COVID-19 outbreak in real time through a fine-resolution agent-based and data-driven model that aims at providing unprecedented insight in the spread and potential mitigation strategies of this virus at the city level. The approach will afford thorough what-if analysis on the effectiveness of ongoing and potential mitigation strategies. The agent-based model will include COVID-19 specific features, such as the type and timing of testing, asymptomatic occurrence, and hospitalization stages. The framework will be grounded in publicly available census and geo-referred data from New Rochelle, New York. Social behavior associated with rational and irrational factors will be included in the mobility patterns of the agent-based model at multiple spatial and temporal scales to increase the granularity of the predictions. Network-theoretic and data-driven control strategies will inform enhanced testing protocols involving active trials on the basis of available contact databases collected at testing sites.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.