The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a constant - effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified, and its extent remains unknown. How can this intrinsic variability be percolated into epidemic models, and its impact, better quantified? We study this question here through a Bayesian perspective that captures at scale the heterogeneity of a population and environmental conditions, creating a bridge between the traditional agent-based and compartmental approaches. We use our model to simulate the spread as well as the impact of different social distancing strategies on real COVID-19 data, and highlight the significant impact of the heterogeneity. We emphasize that the contribution of this paper focuses on discussing the importance of the impact of R's heterogeneity on uncertainty quantification from a statistical viewpoint, rather than developing new predictive models.

正确评估 COVID-19 的传染数R对于量化大流行的潜在范围和选择适当的行动方案至关重要。在大多数模型中，R 被建模为一个常数 - 有效地平均了由于个体接触率、人口密度或时间因素的变化而导致的传播过程的固有变异性。然而，由于流行病呈指数增长，这种简化所产生的误差可能会迅速放大，其程度仍然未知。如何将这种内在的变异性渗透到流行病模型中，并更好地量化其影响？我们通过贝叶斯视角来研究这个问题，该视角大规模地捕捉了人口和环境条件的异质性，在传统的基于主体的方法和分区方法之间架起了一座桥梁。我们使用我们的模型来模拟传播以及不同社交距离策略对真实 COVID-19 数据的影响，并强调异质性的重大影响。我们强调，本文的贡献集中于从统计角度讨论R的异质性对不确定性量化影响的重要性，而不是开发新的预测模型。