异质性的作用:美国 COVID-19 情景建模中心的全国规模数据驱动代理建模

IF 3 3区 医学 Q2 INFECTIOUS DISEASES
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引用次数: 0

摘要

UVA-EpiHiper 是一个基于国家规模代理的模型,用于支持美国 COVID-19 场景建模中心 (SMH)。UVA-EpiHiper 使用底层社会接触网络的详细表示法以及大流行期间测量的数据来初始化和校准模型。在本文中,我们使用 UVA-EpiHiper 研究了异质性对模型复杂性和由此产生的流行动态的作用。我们讨论了在使用 UVA-EpiHiper 支持各种情景下流行病动态建模和分析时遇到的各种异质性来源。我们还讨论了这如何影响模型复杂性和相应模拟的计算复杂性。以第13轮SMH为例,我们讨论了如何对UVA-EpiHiper进行初始化和校准。然后,我们讨论如何分析 UVA-EpiHiper 产生的详细输出,以获得有趣的见解。我们发现,尽管基于代理的模型在情景建模中需要复杂的模型、软件和计算,但它能够捕捉现实世界系统的各种异质性,尤其是网络和行为中的异质性,并能分析不同人口、地理和社会群组之间流行病学结果的异质性。在应用 UVA-EpiHiper 进行第 13 轮情景建模时,我们发现各州之间、各州内部以及不同人口群体之间的疾病结果是不同的,这可归因于人口统计、网络结构和初始免疫力的异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub

UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.

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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
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