RAPID: Networked Data-Driven Modelling of the COVID-19 Outbreak with a Performativity-Aware Calibration Learning Algorithm

该项目将开发和部署一个数据驱动的数学建模框架，用于预测COVID-19在区域层面的传播，并为潜在的缓解工作提供信息。该等模型亦将提供一种方法，以测试社交距离及流动性减少对疫情未来发展的影响。建议的建模框架依赖于一个两个组件的结构，不需要事先了解疾病的流行病学特征。这种方法在新出现的疫情的初始阶段特别有用，因为在此阶段，对传染病的了解和验证很少。此外，该项目还将为疾病传播的数学建模带来新的视角，这将补充其他正在进行的努力，并提供对不确定性下的决策至关重要的各种模型。该项目建立在数据驱动的数学建模方法之上，利用流行病学数据集和模型中检查的令人惊讶的简单行为，可以预测病例数，而无需参数估计。第一个目标是将数据驱动的建模集成到显式网络交互中，以研究COVID-19爆发传播的空间方面。该项目的第二个重点是实施一个考虑到减缓努力的校准层。这种方法的基本原理是，在一个不断变化的环境中，由于表演效应，流行病学预测很难做出，模型预测影响社会行为和缓解努力，这反过来又改变了数学模型预测的疫情传播。从概念的角度来看，这个项目将在流行病学建模的背景下解决表演性。在实践层面，它将开发一个通用校准模块，学习如何将对预测的反应纳入流行病学预测。根据设计，这一“感知表演性”的校准模块将独立于任何特定的流行病模型;因此，一旦开发，该奖项由分配给MPS的CARES补充资金资助。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进行评估，被认为值得支持审查标准。

This project will develop and deploy a data-driven mathematical modeling framework for predicting the spread of COVID-19 at regional levels and for informing potential mitigation efforts. The models will also provide a means to test the impact of social distancing and mobility reduction on the future course of the pandemic. The proposed modeling framework relies on a two-component structure that does not require prior knowledge of the epidemiological characteristics of the disease. This approach is especially useful during the initial stages of an emerging outbreak, where little is known and validated about the contagion. Moreover, this project will bring a novel perspective on the mathematical modeling of disease spread, which will complement other ongoing efforts and provide access to diverse models critical to decision-making under uncertainty.This project builds upon a data-driven mathematical modeling approach leveraging a surprisingly simple behavior examined in epidemiological data sets and models that allows forecasts for case counts with no parameter estimations. The first thrust is to integrate data-driven modeling into explicit network interactions in order to investigate spatial aspects of COVID-19 outbreak propagation. The second thrust of the project is to implement a calibration layer that takes into account mitigation efforts. The rationale for this approach is that, in a constantly evolving environment, epidemiological predictions are difficult to make due to the performativity effect, whereby model predictions affect social behavior and mitigation efforts, which in turn alters the spread of the outbreak predicted by the mathematical models. From a conceptual point of view, this project will address performativity in the context of epidemiological modeling. At the practical level, it will develop a general calibration module that will learn how to incorporate reactions to predictions into epidemiological forecasts. By design, this “performativity-aware” calibration module will be independent of any specific epidemic model; hence, once developed, it will be possible to be integrated into other existing predictive models.This award is being funded by the CARES 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.