Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA.
Proc Biol Sci. 2024 Oct;291(2033):20241772. doi: 10.1098/rspb.2024.1772. Epub 2024 Oct 30.
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.
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。
