Network Modeling and Analysis of COVID-19 Testing Strategies
Network Modeling and Analysis of COVID-19 Testing Strategies
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COVID-19 测试策略的网络建模和分析
DOI：
10.1109/embc46164.2021.9629754
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yang, Hui
中科院分区：
文献类型：
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作者：
Zhang, Siqi;Ventura, Marta J.;Yang, Hui
美国疾病控制和预防中心的COVID-19准备计划强烈强调了高效和有效的检测策略的必要性。这反过来要求设计和开发COVID-19战略的统计抽样和测试。然而，操作细节的评估需要在流行病模拟模型中详细表示人类行为。传统的流行病模拟主要基于系统动力学模型，利用微分方程来研究种群亚群的宏观行为和聚集行为。因此，个人行为（例如，个人保护、通勤条件、社会模式）无法充分建模和跟踪，以评估卫生政策和行动战略。因此，本文提出了一种基于网络的仿真模型，以优化COVID-19检测策略，从而有效识别空间区域内的病毒携带者。具体而言，我们设计了一个数据驱动的风险评分系统，用于COVID-19的统计抽样和测试。该系统通过模拟空间网络中个体的网络行为，实时采集数据，为病毒传播过程中的决策提供支持。实验结果表明，该框架在优化COVID-19检测决策和有效识别人群中的病毒携带者方面具有上级性能。
The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can’t be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.
DOI：
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发表时间：
2016
期刊：
影响因子：
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作者：
Yunchao Chen;F. Leonelli;Hui Yang;Eva K. Lee
通讯作者：
Eva K. Lee
影响因子：
9.3
作者：
Johansson MA;Wolford H;Paul P;Diaz PS;Chen TH;Brown CM;Cetron MS;Alvarado-Ramy F
通讯作者：
Alvarado-Ramy F
旨在打造安全社区的家庭暴力私人妇女庇护所和配偶暴力咨询和支持中心：应对新型冠状病毒感染
DOI：
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发表时间：
2020
期刊：
日本セーフティプロモーション学会誌
影响因子：
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作者：
Tsunoda;H.;and Sakurai;R;須賀 朋子
通讯作者：
須賀 朋子