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2019冠状病毒病在区域层面的传播，并为可能的缓解工作提供信息。这些模型还将提供一种手段，以测试保持社交距离和减少流动性对大流行未来进程的影响。所提出的建模框架依赖于一个双组分结构，该结构不需要事先了解该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，在这个阶段，人们对传染病知之甚少，也没有得到证实。此外，该项目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，并提供对不确定情况下决策至关重要的各种模型。该项目以数据驱动的数学建模方法为基础，利用流行病学数据集和模型中检验的一种非常简单的行为，无需参数估计即可预测病例数。第一个重点是将数据驱动的建模集成到明确的网络交互中，以调查COVID-19疫情传播的空间方面。该项目的第二个重点是实施一个考虑到缓解努力的校准层。采用这种方法的理由是，在不断变化的环境中，由于性能效应，难以进行流行病学预测，即模型预测会影响社会行为和缓解努力，从而改变数学模型预测的疫情蔓延。从概念的角度来看，本项目将在流行病学建模的背景下解决表演性问题。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。通过设计，这种“性能感知”校准模块将独立于任何特定的流行病模型；因此，一旦开发出来，它将有可能集成到其他现有的预测模型中。该奖项由分配给MPS的CARES补充资金资助。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。

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.