ICES: Large: Collaborative Research: The Role of Space, Time and Information in Controlling Epidemics

ICES:大型:协作研究:空间、时间和信息在控制流行病中的作用

基本信息

项目摘要

The control of epidemics, broadly defined to range from human diseases such as influenza and smallpox to malware in communication networks, relies crucially on interventions such as vaccinations and anti-virals (in human diseases) or software patches (for malware). These interventions are almost always voluntary directives from public agencies; however, people do not always adhere to such recommendations, and make individual decisions based on their specific "self interest". Additionally, people alter their contacts dynamically, and these behavioral changes have a huge impact on the dynamics and the effectiveness of these interventions, so that "good" intervention strategies might, in fact, be ineffective, depending upon the individual response. The goal of this project is to study the foundations of policy design for controlling epidemics, using a broad class of epidemic games on complex networks involving uncertainty in network information, temporal evolution and learning. Models will be proposed to capture the complexity of static and temporal interactions and patterns of information exchange, including the possibility of failed interventions and the potential for moral hazard. The project will also study specific policies posed by public agencies and network security providers for controlling the spread of epidemics and malware, and will develop resource constrained mechanisms to implement them in this framework.This project will integrate approaches from Computer Science, Economics, Mathematics, and Epidemiology to give intellectual unity to the study and design of public health policies and has the potential for strong dissertation work in all these areas. Education and outreach is an important aspect of the project, and includes curriculum development at both the graduate and under-graduate levels. A multi-disciplinary workshop is also planned as part of the project.
流行病的广义定义是,从流感和天花等人类疾病到通信网络中的恶意软件,其控制主要依赖于疫苗接种和抗病毒药物(针对人类疾病)或软件补丁(针对恶意软件)等干预措施。这些干预几乎总是来自公共机构的自愿指令;然而,人们并不总是坚持这样的建议,并根据他们特定的“自我利益”做出个人决定。此外,人们动态地改变他们的接触,这些行为的改变对这些干预的动态和有效性有巨大的影响,因此,“好的”干预策略实际上可能是无效的,这取决于个人的反应。本项目的目标是研究控制流行病的政策设计基础,使用涉及网络信息不确定性、时间进化和学习的复杂网络上的广泛流行病博弈。将提出模型,以捕捉静态和时间相互作用和信息交换模式的复杂性,包括干预失败的可能性和潜在的道德风险。该项目还将研究公共机构和网络安全提供商为控制流行病和恶意软件的传播而提出的具体政策,并将开发资源限制机制,以便在此框架内实施这些政策。该项目将整合计算机科学、经济学、数学和流行病学的方法,为公共卫生政策的研究和设计提供知识统一,并在所有这些领域都有很强的论文工作潜力。教育和推广是该项目的一个重要方面,包括研究生和本科生两级的课程开发。作为项目的一部分,还计划举办一个多学科研讨会。

项目成果

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Anil Kumar Vullikanti其他文献

Anil Kumar Vullikanti的其他文献

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{{ truncateString('Anil Kumar Vullikanti', 18)}}的其他基金

Collaborative Research: SaTC: CORE: Medium: Graph Mining and Network Science with Differential Privacy: Efficient Algorithms and Fundamental Limits
协作研究:SaTC:核心:媒介:具有差异隐私的图挖掘和网络科学:高效算法和基本限制
  • 批准号:
    2317193
  • 财政年份:
    2023
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Continuing Grant
III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections
III:媒介:合作研究:检测和控制医院获得性感染的网络传播
  • 批准号:
    1955797
  • 财政年份:
    2020
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Standard Grant
RAPID: Collaborative Research: Using Phylodynamics and Line Lists for Adaptive COVID-19 Monitoring
RAPID:协作研究:使用系统动力学和线路列表进行自适应 COVID-19 监测
  • 批准号:
    2027848
  • 财政年份:
    2020
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Standard Grant
BIGDATA: Collaborative Research: F: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics
BIGDATA:协作研究:F:流行病学和传染动力学中大规模图问题的高效分布式计算
  • 批准号:
    1931628
  • 财政年份:
    2019
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Standard Grant
BIGDATA: Collaborative Research: F: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics
BIGDATA:协作研究:F:流行病学和传染动力学中大规模图问题的高效分布式计算
  • 批准号:
    1633028
  • 财政年份:
    2016
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Standard Grant
CAREER: Cross-layer optimization in Cognitive Radio Networks in the Physical interference model based on SINR constraints: Algorithmic Foundations
职业:基于 SINR 约束的物理干扰模型中认知无线电网络的跨层优化:算法基础
  • 批准号:
    0845700
  • 财政年份:
    2009
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: NECO: A Market-Driven Approach to Dynamic Spectrum Sharing
合作研究:NECO:市场驱动的动态频谱共享方法
  • 批准号:
    0831633
  • 财政年份:
    2008
  • 资助金额:
    $ 29.5万
  • 项目类别:
    Continuing Grant

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