RAPID/Collaborative Research: Developing Pandemics and Healing Models for Coronavirus COVID-19 to Assist in Policy Making
快速/合作研究:开发冠状病毒 COVID-19 的流行病和治疗模型以协助政策制定
基本信息
- 批准号:2029291
- 负责人:
- 金额:$ 4万
- 依托单位:
- 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 2022-05-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The current pandemic has stimulated a strong response on the part of local, state, and federal government, with containment largely achieved through stringent lockdowns, effectively quarantining nearly every household in the country. Given the enormous socio-economic impacts of this approach, it is imperative to understand how to minimize the spread of the epidemic while also minimizing deleterious effects and maximizing the availability of critical health resources. This project seeks to address this challenge by devising a better and scalable alternative to lockdown under suitable constraints.This project focuses on developing models for the COVID-19 pandemic, in particular looking at neighboring community spread, mitigation measures, and optimal distribution of healthcare resources in that context. This project aims to (i) devise a better and scalable alternative to full lockdown; (ii) devise a cognitive solution that can be applied to various demographics having heterogeneous connectivity and population distribution with minimal information regarding previous epidemic spread; and (iii) minimize the impact of epidemic model uncertainties on the confinement and medical resource allocation strategies. The PIs will employ a collection of novel mathematical techniques to the problem that can handle heterogeneity and are scalable. The interdisciplinary team includes Johns Hopkins University, which has been a major Center for the collection of COVID-19 data.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.
当前的疫情已引起地方、州和联邦政府的强烈反应,通过严格的封锁措施基本上实现了遏制,有效地使该国几乎每个家庭都受到了影响。 鉴于这一做法的巨大社会经济影响,必须了解如何尽量减少这一流行病的传播,同时尽量减少有害影响,并尽量提供关键的卫生资源。 该项目旨在通过在适当的限制下设计更好和可扩展的封锁替代方案来应对这一挑战。该项目专注于开发COVID-19大流行的模型,特别是在此背景下研究邻近社区的传播,缓解措施以及医疗资源的最佳分配。 该项目旨在(i)设计一个更好和可扩展的替代全面封锁的方案;(ii)设计一个认知解决方案,可应用于具有异质连接和人口分布的各种人口统计数据,并提供有关先前流行病传播的最少信息;以及(iii)尽量减少流行病模型不确定性对隔离和医疗资源分配策略的影响。PI将采用一系列新颖的数学技术来解决问题,这些技术可以处理异质性并且是可扩展的。该跨学科团队包括约翰霍普金斯大学,该大学一直是COVID-19数据收集的主要中心。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。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Demography-aware COVID-19 Confinement with Game Theory
利用博弈论进行具有人口统计特征的 COVID-19 限制
- DOI:10.1109/aicas51828.2021.9458525
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Kasarapu, Sreenitha;Hassan, Rakibul;Rafatirad, Setareh;Homayoun, Houman;Pudukotai Dinakarrao, Sai Manoj
- 通讯作者:Pudukotai Dinakarrao, Sai Manoj
Scalable and Demography-Agnostic Confinement Strategies for COVID-19 Pandemic with Game Theory and Graph Algorithms
- DOI:10.3390/covid2060058
- 发表时间:2022-06-01
- 期刊:
- 影响因子:0
- 作者:Kasarapu,Sreenitha;Hassan,Rakibul;Pudukotai Dinakarrao,Sai Manoj
- 通讯作者:Pudukotai Dinakarrao,Sai Manoj
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Sai Manoj Pudukotai Dinakarrao其他文献
Address Obfuscation to Protect against Hardware Trojans in Network-on-Chips
通过地址混淆来防御片上网络中的硬件木马
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:2.1
- 作者:
Thomas Mountford;Abhijitt Dhavlle;Andrew Tevebaugh;N. Mansoor;Sai Manoj Pudukotai Dinakarrao;A. Ganguly - 通讯作者:
A. Ganguly
Comprehensive Analysis of Consistency and Robustness of Machine Learning Models in Malware Detection
恶意软件检测中机器学习模型的一致性和鲁棒性综合分析
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Sreenitha Kasarapu;Dipkamal Bhusal;Nidhi Rastogi;Sai Manoj Pudukotai Dinakarrao - 通讯作者:
Sai Manoj Pudukotai Dinakarrao
Memristors' Potential for Multi-bit Storage and Pattern Learning
忆阻器在多位存储和模式学习方面的潜力
- DOI:
10.1109/ems.2015.73 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
N. Taherinejad;Sai Manoj Pudukotai Dinakarrao;A. Jantsch - 通讯作者:
A. Jantsch
Unified Testing and Security Framework for Wireless Network-on-Chip Enabled Multi-Core Chips
支持无线片上网络的多核芯片的统一测试和安全框架
- DOI:
10.1145/3358212 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Abhishek Vashist;Andrew Keats;Sai Manoj Pudukotai Dinakarrao;A. Ganguly - 通讯作者:
A. Ganguly
Generative AI-Based Effective Malware Detection for Embedded Computing Systems
针对嵌入式计算系统的基于生成式人工智能的有效恶意软件检测
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Sreenitha Kasarapu;Sanket Shukla;Rakibul Hassan;Avesta Sasan;H. Homayoun;Sai Manoj Pudukotai Dinakarrao - 通讯作者:
Sai Manoj Pudukotai Dinakarrao
Sai Manoj Pudukotai Dinakarrao的其他文献
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{{ truncateString('Sai Manoj Pudukotai Dinakarrao', 18)}}的其他基金
Collaborative Research: CNS Core: Small: NV-RGRA: Non-Volatile Nano-Second Right-Grained Reconfigurable Architecture for Data-Intensive Machine Learning and Graph Computing
合作研究:CNS 核心:小型:NV-RGRA:用于数据密集型机器学习和图计算的非易失性纳秒右粒度可重构架构
- 批准号:
2228239 - 财政年份:2022
- 资助金额:
$ 4万 - 项目类别:
Standard Grant
Collaborative Research: EAGER: IC-Cloak: Integrated Circuit Cloaking against Reverse Engineering
合作研究:EAGER:IC-Cloak:针对逆向工程的集成电路隐形
- 批准号:
2213404 - 财政年份:2022
- 资助金额:
$ 4万 - 项目类别:
Standard Grant
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