论文标题
一个动态的框架，用于建模对感染和沮丧的恐惧，以期在Covid-19中传播的社会疏远
A Dynamical Framework for Modeling Fear of Infection and Frustration with Social Distancing in COVID-19 Spread
论文作者
论文摘要
在本文中，我们介绍了一个新颖的建模框架，以纳入对感染和社会疏远疾病动态的恐惧。我们表明，由此产生的SEIR行为感知模型具有定性行为的三种主要模式 - 没有爆发，受控爆发和不受控制的爆发。我们还证明，该模型可以产生与继发暴发一致的瞬时和持续的感染波。我们将模型适合于累积的COVID-19病例和来自几个地区的死亡率数据。我们的分析表明，在第一波感染（例如加拿大和以色列）之后经历大幅下降的区域更有可能含有感染的次要波浪，而仅在减轻疾病的传播（例如美国）中仅取得了适度成功的区域，例如美国，可能会经历大量的次要波或未经对照的爆发。
In this paper, we introduce a novel modeling framework for incorporating fear of infection and frustration with social distancing into disease dynamics. We show that the resulting SEIR behavior-perception model has three principal modes of qualitative behavior---no outbreak, controlled outbreak, and uncontrolled outbreak. We also demonstrate that the model can produce transient and sustained waves of infection consistent with secondary outbreaks. We fit the model to cumulative COVID-19 case and mortality data from several regions. Our analysis suggests that regions which experience a significant decline after the first wave of infection, such as Canada and Israel, are more likely to contain secondary waves of infection, whereas regions which only achieve moderate success in mitigating the disease's spread initially, such as the United States, are likely to experience substantial secondary waves or uncontrolled outbreaks.
