Collaborative Research: RAPID: Statistical Tools to Quantify and Mitigate the Spread of COVID-19

该项目将开发严格的统计模型和推理工具，以更好地了解COVID-19的传播以及最有可能防止进一步爆发的干预措施类型。该工作将产生量化可能结果的不确定性的具体方法，并提供可靠的工具，以协助决策者设计COVID-19大流行的缓解策略。特别是，将开发一种新的统计推断工具，以可解释的方式估计描述COVID-19传播性质的关键流行病参数。这些参数对于通过实时模拟潜在疫情提供可靠的预测至关重要。新模型将分析不断变化的社会接触结构对COVID-19传播的影响，并以系统的方式解释缺失或部分数据。该项目的结果将有助于提供有效和快速了解未来流行病或相关现象所需的科学和数学方法。现有的流行病模型在很大程度上依赖于确定性系统，无法量化对决策至关重要的不确定性。此外，COVID-19大流行的数据分析和预测建模需要统计工具来拟合仅部分可用数据的流行病模型。然而，大多数随机模型在缺失数据面前面临挑战。为了解决这一差距，项目团队将开发新的随机房室模型，扩展经典方法，以明确解释疾病传播背后的社会接触网络的变化。这些新的发展将伴随着基于可能性的方法来推断关键的流行病参数，这些参数提供了具有不确定性量化的机械解释。特别是，将通过新的数据增强技术解释缺失数据，如个体水平的感染时间。该项目的结果将为科学界如何评估COVID-19大流行提供指导，并为公众提供最有效的缓解策略。该赠款将使用冠状病毒援助，救济，经济安全（CARES）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

This project will develop rigorous statistical models and inference tools to better understand the spread of COVID-19 as well as the type of the interventions that are most likely to work to prevent further outbreak. The work will result in concrete approaches for quantifying uncertainty of possible outcomes and provide reliable tools to assist decision makers in designing mitigation strategies for the COVID-19 pandemic. In particular, a new statistical inference tool will be developed to estimate the key epidemic parameters that describe the nature of the COVID-19 spread in an interpretable manner. These parameters are critical for providing reliable forecasts through real-time simulation of potential outbreaks. The new models will analyze the effects of changing social contact structures on the spread of COVID-19, and account for missing or partial data in a systematic way. The results of this project will contribute to the scientific and mathematical approaches needed to provide effective and quick understanding of future epidemics or relevant phenomena.Existing epidemic models largely rely on deterministic systems that are unable to quantify the uncertainty critical for decision making. In addition, data analysis and predictive modeling for the COVID-19 pandemic require statistical tools for fitting epidemic models with only partially available data. However, most stochastic models face challenges in the face of missing data. To address this gap, the project team will develop new stochastic compartmental models that extend classical approaches to explicitly account for changes to the social contact network underlying the disease spread. These new developments will be accompanied by likelihood-based methods to infer key epidemic parameters that offer mechanistic interpretations with uncertainty quantification. In particular, missing data such as infection times at the individual level will be accounted for via novel data augmentation techniques. The results of this project will provide guidance both to the scientific community on how to evaluate the COVID-19 pandemic, and to the general public on which mitigation strategies would be most effective.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.