During an ongoing epidemic, especially in the case of a new agent, data are partial and sparse, also affected by external factors as for example climatic effects or preparedness and response capability of healthcare structures. Despite that we showed how, under some universality assumptions, it is possible to extract strategic insights by modelling the pandemic trough a probabilistic Polya urn scheme. In the Polya framework, we provide both the distribution of infected cases and the asymptotic estimation of the incidence rate, showing that data are consistent with a general underlying process at different scales. Using European confirmed cases and diagnostic test data on COVID-19 , we provided an extensive comparison among European countries and between Europe and Italy at regional scale, for both the two big waves of infection. We globally estimated an incidence rate in accordance with previous studies. On the other hand, this quantity could play a crucial role as a proxy variable for an unbiased estimation of the real incidence rate, including symptomatic and asymptomatic cases.
翻译:在流行病持续流行期间,特别是在新制剂的情况下,数据是不完整和稀少的,也受到气候影响或保健结构的准备和反应能力等外部因素的影响。尽管我们表明,根据一些普遍性假设,通过一种概率性多胞胎计划模拟这一流行病,是有可能取得战略见解的。在Polica框架内,我们既提供感染病例的分布情况,又提供对发病率的无症状估计,表明数据与不同尺度的一般基本过程是一致的。我们利用欧洲已确认的病例和关于COVID-19的诊断测试数据,提供了欧洲国家之间以及欧洲和意大利之间在区域范围内对两种大感染浪潮的广泛比较。我们根据以往的研究,在全球范围内估计了发病率。另一方面,这一数量可以作为不偏袒地估计实际发病率的替代变量,包括症状和症状病例。