佛罗里达州 COVID-19 大流行的代理建模

IF 3 3区 医学 Q2 INFECTIOUS DISEASES Epidemics Pub Date : 2024-06-01 DOI:10.1016/j.epidem.2024.100774
Alexander N. Pillai , Kok Ben Toh , Dianela Perdomo , Sanjana Bhargava , Arlin Stoltzfus , Ira M. Longini Jr , Carl A.B. Pearson , Thomas J. Hladish
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引用次数: 0

摘要

COVID-19 大流行的爆发推动了各研究小组广泛而又往往缺乏协调地开发 SARS-CoV-2 的数学模型,以研究其传播情况并为控制工作提供信息。在疫情爆发之初,对洞察力的迫切需求意味着早期的模型通常要么很简单,要么是从现有的研究议程中挪用过来的。我们小组主要使用基于代理的模型(ABM)来研究精细的干预方案。这些高分辨率模型庞大、复杂,需要大量的经验数据,而且往往比回答 "我们是否应该封锁 "等定性问题所需的数据更为详细。在特殊传染病危机的早期阶段,特别是在有明确的经验证据之前,更适合使用简单的模型。然而,随着更详细的经验证据的出现,以及政策决策变得更加细微和复杂,像我们这样的精细方法就变得更加有用了。在本手稿中,我们将讨论我们的研究小组在建立大流行病模型的过程中是如何驾驭这一转变的。建模者的角色往往包括近乎实时的分析,以及快速调整工具的艰巨任务。我们经常要在大量证据面前奋起直追,同时还要努力开展学术研究和实时决策支持,而这两方面的条件对我们都不利。通过反思我们应对大流行病的经验以及从这些挑战中学到的东西,我们可以更好地应对未来的需求。
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Agent-based modeling of the COVID-19 pandemic in Florida

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like “Should we lockdown?” During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.

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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.
期刊最新文献
Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland Impact of COVID-19 control measures on respiratory syncytial virus and hand-foot-and-mouth disease transmission in Hong Kong and South Korea The effects of HIV self-testing on HIV incidence and awareness of status among men who have sex with men in the United States: Insights from a novel compartmental model Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey
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