Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios

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