III: Small: Data-Driven Control of Epidemic Processes over Complex Dynamic Networks

III：小：复杂动态网络上数据驱动的流行病过程控制

基本信息

批准号：
2008456
负责人：
VICTOR PRECIADO
金额：
$ 43.99万
依托单位：
University of Pennsylvania
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：
2020-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008456&HistoricalAwards=false
关键词：
III Small Data Driven Control

项目摘要

Despite notable advances in medicine over the last century, recent pandemics such as COVID-19 remind us that the threat of infectious diseases to human populations is very real. While continuing advances in medicine are essential, information technologies can greatly improve our ability to detect and contain the devastating effects of infectious diseases. In this direction, public health agencies collect, periodically update, and publicly report field data containing geolocated information about the tested, infected, recovered, hospitalized, and deceased individuals in those areas affected by the disease. However, this data is unreliable, incomplete, and coarse-grained; therefore, health agencies can greatly benefit from information technologies to filter and analyze field data in order to make reliable predictions about the future spread of the disease. Moreover, the final objective of a health agency is to use this information to design efficient strategies to contain the spread of infectious diseases. To achieve this objective, health agencies have at their disposal epidemic-control resources, such as social distancing, traffic restrictions, and the distribution of pharmaceutical resources (whenever available). Due to the heterogeneity and high cost of these resources, finding the cost-optimal allocation of each type of resource throughout the population is a very challenging problem of utmost societal impact. In this project, we propose to develop an integrated framework for modeling, prediction, and cost-optimal control of epidemic outbreaks using finite resources and unreliable data.In order to implement practical epidemic-control tools, it is necessary to first develop mathematical models able to replicate salient geo-temporal features of disease transmission. These patterns are strongly influenced by the geography of the area over which the disease is spreading, as well as the mobility patterns of the population. In this direction, we will use complex contact graphs to model both realistic geographical constraints and mobility patterns. In particular, the vertices of this graph correspond to towns/districts and its links represent interactions between them. On top of this contact graph, we will build a dynamical model aiming to replicate the complex geo-temporal spread of the disease. In this direction, we will consider a system of stochastic processes, coupled through the edges of the contact graph, to model the evolution of the disease. Once the model of the spread is tuned, we will then proceed to the design of a coordinated strategy to contain the spread of the infection by distributing resources throughout the population. In this direction, we will design and implement an optimization program to find the cost-optimal allocation of heterogeneous resources given a finite budget. In this research task, we must deal with the inherent uncertainty of field data, as well as the presence of sampling biases that can have a dramatic impact on the fairness of the cost-optimal allocation of resources. The success of the proposed research program would greatly improve our ability to efficiently detect and appropriately react to epidemic outbreaks, whereupon a rapid control response can be deployed.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.

尽管上个世纪在医学方面取得了显着进步，但诸如Covid-19之类的近期大流行者提醒我们，传染病对人类人群的威胁非常真实。尽管医学的持续进展是必不可少的，但信息技术可以大大提高我们检测并包含传染病的毁灭性影响的能力。在这个方向上，公共卫生机构收集，定期更新和公开报告包含有关该疾病影响的地区的测试，感染，恢复，住院和已故个人的地理分配信息的现场数据。但是，这些数据是不可靠的，不完整的和粗粒的。因此，卫生机构可以从信息技术中受益匪浅，以过滤和分析现场数据，以便对疾病的未来传播做出可靠的预测。此外，卫生机构的最终目标是使用此信息来设计有效的策略，以控制传染病的传播。为了实现这一目标，卫生机构拥有可支配的流行病资源，例如社会疏远，交通限制和药品资源的分配（随时可用）。由于这些资源的异质性和高成本，在整个人群中找到每种类型的资源的成本优势分配是一个非常具有挑战性的社会影响的问题。在这个项目中，我们建议使用有限资源和不可靠的数据来开发一个集成的框架，用于建模，预测和成本优势控制流行病爆发。在实施实用的流行病控制工具中，必须首先开发数学模型，以复制能够复制疾病传播的明显地理位置特征。这些模式受疾病传播区域的地理以及人口的流动性模式的强烈影响。在这个方向上，我们将使用复杂的接触图来建模现实的地理约束和移动性模式。特别是，该图的顶点对应于城镇/地区，其链接代表了它们之间的相互作用。在此触点图上，我们将建立一个动力学模型，旨在复制该疾病的复杂地球传播。在这个方向上，我们将考虑通过接触图的边缘耦合的随机过程系统，以对疾病的演变进行建模。一旦调整了点差的模型，我们将继续设计协调的策略，以通过在整个人群中分配资源来遏制感染的传播。在这个方向上，我们将设计和实施优化计划，以找到有限预算的异质资源的成本优势分配。在这项研究任务中，我们必须处理现场数据的固有不确定性，以及对采样偏见的存在，这些偏见可能会对资源的成本优势分配的公平性产生巨大影响。拟议的研究计划的成功将大大提高我们有效检测和适当反应流行病暴发的能力，因此可以部署快速控制的响应。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。