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本文引用的文献

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Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey.用于新冠病毒监测及其他用途的基于废水的流行病学:一项调查
Epidemics. 2024 Dec;49:100793. doi: 10.1016/j.epidem.2024.100793. Epub 2024 Sep 26.
2
Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates.为何类似政策导致了不同的新冠疫情结果:响应能力和文化如何影响死亡率。
Health Aff (Millwood). 2023 Dec;42(12):1637-1646. doi: 10.1377/hlthaff.2023.00713.
3
Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions.基因组监测与流行疫情演变的耦合模型及其在及时公共卫生干预中的应用。
Proc Natl Acad Sci U S A. 2023 Nov 28;120(48):e2305227120. doi: 10.1073/pnas.2305227120. Epub 2023 Nov 20.
4
Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention.个体对非药物干预措施依从性的行为-流行病学模型中的动力学。
Proc Natl Acad Sci U S A. 2023 Oct 31;120(44):e2311584120. doi: 10.1073/pnas.2311584120. Epub 2023 Oct 27.
5
Medium-term scenarios of COVID-19 as a function of immune uncertainties and chronic disease.COVID-19 中期情景作为免疫不确定性和慢性病的函数。
J R Soc Interface. 2023 Aug;20(205):20230247. doi: 10.1098/rsif.2023.0247. Epub 2023 Aug 30.
6
Individual costs and societal benefits of interventions during the COVID-19 pandemic.新冠疫情期间干预措施的个体成本和社会效益。
Proc Natl Acad Sci U S A. 2023 Jun 13;120(24):e2303546120. doi: 10.1073/pnas.2303546120. Epub 2023 Jun 7.
7
Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications.室内聚集活动中跨多个时间尺度的空气传播疾病传播:建模框架和政策意义。
Proc Natl Acad Sci U S A. 2023 Apr 18;120(16):e2216948120. doi: 10.1073/pnas.2216948120. Epub 2023 Apr 10.
8
A missing behavioural feedback in COVID-19 models is the key to several puzzles.新冠病毒疾病模型中缺失的行为反馈是解开几个谜团的关键。
BMJ Glob Health. 2022 Oct;7(10). doi: 10.1136/bmjgh-2022-010463.
9
Behavioral responses to risk promote vaccinating high-contact individuals first.对风险的行为反应促使首先为高接触人群接种疫苗。
Syst Dyn Rev. 2022 Jul-Sep;38(3):246-263. doi: 10.1002/sdr.1714. Epub 2022 Aug 23.
10
Sociocultural determinants of global mask-wearing behavior.全球戴口罩行为的社会文化决定因素。
Proc Natl Acad Sci U S A. 2022 Oct 11;119(41):e2213525119. doi: 10.1073/pnas.2213525119. Epub 2022 Oct 3.

适应性人类行为调节免疫生活史和疫苗接种对长期流行动力学的影响。

Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics.

机构信息

Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.

Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA.

出版信息

Proc Biol Sci. 2024 Oct;291(2033):20241772. doi: 10.1098/rspb.2024.1772. Epub 2024 Oct 30.

DOI:10.1098/rspb.2024.1772
PMID:39471851
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11521615/
Abstract

The multiple immunity responses exhibited in the population and co-circulating variants documented during pandemics show a high potential to generate diverse long-term epidemiological scenarios. Transmission variability, immune uncertainties and human behaviour are crucial features for the predictability and implementation of effective mitigation strategies. Nonetheless, the effects of individual health incentives on disease dynamics are not well understood. We use a behavioural-immuno-epidemiological model to study the joint evolution of human behaviour and epidemic dynamics for different immunity scenarios. Our results reveal a trade-off between the individuals' immunity levels and the behavioural responses produced. We find that adaptive human behaviour can avoid dynamical resonance by avoiding large outbreaks, producing subsequent uniform outbreaks. Our forward-looking behaviour model shows an optimal planning horizon that minimizes the epidemic burden by balancing the individual risk-benefit trade-off. We find that adaptive human behaviour can compensate for differential immunity levels, equalizing the epidemic dynamics for scenarios with diverse underlying immunity landscapes. Our model can adequately capture complex empirical behavioural dynamics observed during pandemics. We tested our model for different US states during the COVID-19 pandemic. Finally, we explored extensions of our modelling framework that incorporate the effects of lockdowns, the emergence of a novel variant, prosocial attitudes and pandemic fatigue.

摘要

在大流行期间,人群中表现出的多种免疫反应和共同循环变异表明,它们具有产生多样化长期流行病学情景的巨大潜力。传播变异性、免疫不确定性和人类行为是预测和实施有效缓解策略的关键特征。然而,个人健康激励对疾病动态的影响还不是很清楚。我们使用一种行为-免疫-流行病学模型来研究不同免疫情景下人类行为和流行动态的共同演变。我们的研究结果揭示了个体免疫水平和产生的行为反应之间的权衡。我们发现,适应性人类行为可以通过避免大爆发来避免动力学共振,从而产生随后的均匀爆发。我们前瞻性的行为模型展示了一个最优的规划周期,通过平衡个体风险-收益的权衡,来最小化疫情负担。我们发现,适应性人类行为可以弥补不同的免疫水平,使具有不同潜在免疫景观的情景中的疫情动态达到平衡。我们的模型可以很好地捕捉大流行期间观察到的复杂经验行为动态。我们在 COVID-19 大流行期间针对不同的美国州进行了模型测试。最后,我们探索了扩展我们的建模框架,包括封锁、新型变异的出现、亲社会态度和大流行疲劳的影响。