Orr Mark, Mortveit Henning S, Lebiere Christian, Pirolli Pete
Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, United States.
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States.
Front Psychol. 2023 Jun 9;14:986289. doi: 10.3389/fpsyg.2023.986289. eCollection 2023.
There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations, and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior in its seemingly infinite variation and heterogeneity, susceptibility to bias, context, and habit is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder. We offer a 10-year prospectus of kinds that centers around an unprecedented scientific approach: the integration of detailed psychological models into rigorous mathematical and computational epidemiological frameworks in a way that pushes the boundaries of both psychological science and population models of behavior.
在数学与计算流行病学以及详细的心理过程、表征和机制的交叉领域,几乎没有重要的研究工作。尽管科学界和公众普遍认为,人类行为看似无穷的变化和异质性、对偏差、背景和习惯的敏感性,即使不是驱动传染病动态的基本组成部分,也是其不可或缺的一部分,但情况依然如此。新冠疫情就是一个切近而深刻的例证。我们提供了一份为期十年的展望,其核心是一种前所未有的科学方法:将详细的心理模型以推动心理科学和行为人口模型边界的方式,整合到严谨的数学和计算流行病学框架中。