Community vulnerability is widely viewed as an important aspect to consider when modeling disease. Although COVID-19 does disproportionately impact vulnerable populations, human behavior as measured by community mobility is equally influential in understanding disease spread. In this research, we seek to understand which of four composite measures perform best in explaining disease spread and mortality, and we explore the extent to which mobility account for variance in the outcomes of interest. We compare two community mobility measures, three composite measures of community vulnerability, and one composite measure that combines vulnerability and human behavior to assess their relative feasibility in modeling the US COVID-19 pandemic. Extensions – via temporally dependent fixed effect coefficients – of the commonly used Bayesian spatio-temporal Poisson disease mapping models are implemented and compared in terms of goodness of fit as well as estimate precision and viability. A comparison of goodness of fit measures nearly unanimously suggests the human behavior-based models are superior. The duration at residence mobility measure indicates two unique and seemingly inverse relationships between mobility and the COVID-19 pandemic: the findings indicate decreased COVID-19 presence with decreased mobility early in the pandemic and increased COVID-19 presence with decreased mobility later in the pandemic. The early indication is likely influenced by a large presence of state-issued stay at home orders and self-quarantine, while the later indication likely emerges as a consequence of holiday gatherings in a country under limited restrictions. This study implements innovative statistical methods and furnishes results that challenge the generally accepted notion that vulnerability and deprivation are key to understanding disparities in health outcomes. We show that human behavior is equally, if not more important to understanding disease spread. We encourage researchers to build upon the work we start here and continue to explore how other behaviors influence the spread of COVID-19.

社区脆弱性被广泛视为建模疾病时要考虑的一个重要方面。尽管 COVID-19 确实对弱势群体产生了不成比例的影响，但以社区流动性衡量的人类行为对于理解疾病传播同样具有影响力。在这项研究中，我们试图了解四种综合指标中哪一种在解释疾病传播和死亡率方面表现最好，并且我们探讨了流动性在多大程度上解释了感兴趣的结果的差异。我们比较了两种社区流动性措施、三种社区脆弱性综合措施和一种结合脆弱性和人类行为的综合措施，以评估它们在美国 COVID-19 大流行建模中的相对可行性。通过时间相关的固定效应系数扩展常用的贝叶斯时空泊松疾病映射模型，并在拟合优度以及估计精度和可行性方面进行比较。拟合优度的比较几乎一致表明基于人类行为的模型更优越。居住时间流动性衡量指标表明流动性和 COVID-19 大流行之间存在两种独特且看似相反的关系：研究结果表明，在大流行早期，随着流动性下降，COVID-19 的存在减少，而在大流行后期，随着流动性下降，COVID-19 的存在增加。 . 早期迹象可能受到国家发布的大量居家令和自我隔离的影响，而后来的迹象可能是由于在一个限制有限的国家/地区举行的假期聚会而出现的。这项研究采用了创新的统计方法并提供了结果，这些结果挑战了普遍接受的观念，即脆弱性和剥夺是理解健康结果差异的关键。我们表明，人类行为对于理解疾病传播同样重要，甚至更重要。我们鼓励研究人员以我们在这里开始的工作为基础，继续探索其他行为如何影响 COVID-19 的传播。我们表明，人类行为对于理解疾病传播同样重要，甚至更重要。我们鼓励研究人员以我们在这里开始的工作为基础，继续探索其他行为如何影响 COVID-19 的传播。我们表明，人类行为对于理解疾病传播同样重要，甚至更重要。我们鼓励研究人员以我们在这里开始的工作为基础，继续探索其他行为如何影响 COVID-19 的传播。