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

RAPID：使用性能感知校准学习算法对 COVID-19 爆发进行网络数据驱动建模

基本信息

批准号：
2028401
负责人：
Faryad Darabi Sahneh
金额：
$ 15.64万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028401&HistoricalAwards=false
关键词：
RAPID Networked Data Driven Modelling

项目摘要

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.

该项目将开发和部署一个数据驱动的数学建模框架，用于预测新冠肺炎在区域一级的传播，并为潜在的缓解努力提供信息。这些模型还将提供一种手段，以测试社会距离和流动性减少对大流行未来进程的影响。拟议的建模框架依赖于两个组成部分的结构，该结构不需要事先了解疾病的流行病学特征。在新爆发的初期阶段，这种方法尤其有用，因为人们对这种传染病的了解和验证很少。此外，这个项目将为疾病传播的数学建模带来一个新的视角，这将补充其他正在进行的努力，并提供对不确定情况下的决策至关重要的各种模型的访问。该项目建立在数据驱动的数学建模方法的基础上，该方法利用在流行病学数据集和模型中检查的令人惊讶的简单行为，允许在没有参数估计的情况下预测病例数量。第一个推力是将数据驱动的建模集成到显式的网络交互中，以调查新冠肺炎爆发传播的空间方面。该项目的第二个主旨是实施一个考虑到缓解努力的校准层。这种方法的基本原理是，在一个不断演变的环境中，由于执行性效应，流行病学预测很难做出，即模型预测影响社会行为和缓解努力，这反过来又改变数学模型预测的暴发的传播。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绩效问题。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。根据设计，这个“绩效感知”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，它将有可能整合到其他现有的预测模型中。该奖项由分配给MP的CARE补充资金提供资金。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影响审查标准进行评估，被认为值得支持。