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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.

尽管在上个世纪医学取得了显著的进步，但最近COVID-19等大流行病提醒我们，传染病对人类的威胁是非常真实的。虽然医学的不断进步至关重要，但信息技术可以大大提高我们发现和控制传染病破坏性影响的能力。在这个方向上，公共卫生机构收集，定期更新，并公开报告现场数据，其中包含有关受疾病影响的地区的测试，感染，康复，住院和死亡个体的地理定位信息。然而，这些数据是不可靠的，不完整的，粗粒度的;因此，卫生机构可以大大受益于信息技术来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利用这些信息来设计有效的战略，以遏制传染病的传播。为了实现这一目标，卫生机构拥有可供支配的药物控制资源，如社交距离、交通限制和药物资源分配（只要有）。由于这些资源的异质性和高成本，在整个人口中找到每种资源的成本最优分配是一个非常具有挑战性的问题，具有最大的社会影响。在本项目中，我们建议开发一个集成框架，用于使用有限资源和不可靠数据对流行病爆发进行建模、预测和成本最优控制。为了实现实用的流行病控制工具，有必要首先开发能够复制疾病传播的显着地理时间特征的数学模型。这些模式受到疾病传播地区的地理以及人口流动模式的强烈影响。在这个方向上，我们将使用复杂的接触图来模拟现实的地理限制和移动模式。特别是，该图的顶点对应于城镇/地区，其链接表示它们之间的相互作用。在这个接触图的基础上，我们将建立一个动态模型，旨在复制疾病的复杂地理时间传播。在这个方向上，我们将考虑一个系统的随机过程，耦合通过边的接触图，模拟疾病的演变。一旦传播模型得到调整，我们将着手设计一个协调的战略，通过在整个人口中分配资源来控制感染的传播。在这个方向上，我们将设计和实现一个优化程序，以找到成本最优分配的异构资源给定的有限预算。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，以及抽样偏差的存在，可以有一个显着的影响公平的成本最优的资源分配。该研究项目的成功将大大提高我们有效检测和适当应对疫情爆发的能力，从而可以部署快速控制响应。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审查标准进行评估，被认为值得支持。