Glaubitz Alina, Fu Feng
ArXiv. 2020 Nov 26:arXiv:2011.13358v1.
Social distancing as one of the main non-pharmaceutical interventions can help slow down the spread of diseases, like in the COVID-19 pandemic. Effective social distancing, unless enforced as drastic lockdowns and mandatory cordon sanitaire, requires consistent strict collective adherence. However, it remains unknown what the determinants for the resultant compliance of social distancing and their impact on disease mitigation are. Here, we incorporate into the epidemiological process with an evolutionary game theory model that governs the evolution of social distancing behavior. In our model, we assume an individual acts in their best interest and their decisions are driven by adaptive social learning of the real-time risk of infection in comparison with the cost of social distancing. We find interesting oscillatory dynamics of social distancing accompanied with waves of infection. Moreover, the oscillatory dynamics are dampened with a nontrivial dependence on model parameters governing decision-makings and gradually cease when the cumulative infections exceed the herd immunity. Compared to the scenario without social distancing, we quantify the degree to which social distancing mitigates the epidemic and its dependence on individuals' responsiveness and rationality in their behavior changes. Our work offers new insights into leveraging human behavior in support of pandemic response.
社交距离作为主要的非药物干预措施之一，有助于减缓疾病传播，如在新冠疫情中。有效的社交距离，除非像严格封锁和强制卫生警戒线那样实施，需要持续严格的集体遵守。然而，社交距离最终合规的决定因素及其对疾病缓解的影响仍然未知。在此，我们将一个控制社交距离行为演变的进化博弈论模型纳入流行病学过程。在我们的模型中，我们假设个体以自身最佳利益行事，其决策由与社交距离成本相比的实时感染风险的适应性社会学习驱动。我们发现社交距离有趣的振荡动态伴随着感染浪潮。此外，振荡动态会因对控制决策的模型参数的非平凡依赖而受到抑制，并且当累计感染超过群体免疫时会逐渐停止。与没有社交距离的情况相比，我们量化了社交距离减轻疫情的程度及其对个体行为变化中反应性和理性的依赖。我们的工作为利用人类行为支持疫情应对提供了新见解。
