We use data from 107 Italian provinces to characterize and compare mortality patterns in the first two COVID-19 epidemic waves, which occurred prior to the introduction of vaccines. We also associate these patterns with mobility, timing of government restrictions, and socio-demographic, infrastructural, and environmental covariates. Notwithstanding limitations in the accuracy and reliability of publicly available data, we are able to exploit information in curves and shapes through Functional Data Analysis techniques. Specifically, we document differences in magnitude and variability between the two waves; while both were characterized by a co-occurrence of 'exponential' and 'mild' mortality patterns, the second spread much more broadly and asynchronously through the country. Moreover, we find evidence of a significant positive association between local mobility and mortality in both epidemic waves and corroborate the effectiveness of timely restrictions in curbing mortality. The techniques we describe could capture additional signals of interest if applied, for instance, to data on cases and positivity rates. However, we show that the quality of such data, at least in the case of Italian provinces, was too poor to support meaningful analyses.

翻译：我们利用意大利107个省份的数据，对前两波新冠疫情（疫苗引入前）的死亡模式进行刻画与比较，并将其与人口流动性、政府限制措施时间节点以及社会人口、基础设施和环境协变量建立关联。尽管公开数据的准确性与可靠性存在局限，我们仍能通过函数型数据分析技术挖掘曲线与形态中的信息特征。具体而言，我们揭示了两波疫情在规模与变异性上的差异：虽然两者均呈现"指数型"与"温和型"死亡模式共存的特征，但第二波疫情在意大利全国的传播范围更广且时空同步性更弱。此外，我们发现两波疫情中局部人口流动性与死亡率之间存在显著正向关联，并验证了及时限制措施在遏制死亡率方面的有效性。本文所述技术若应用于病例数与阳性率等数据，或可捕获更多有价值的信号。然而我们证实，至少对意大利省份而言，此类数据质量过差而无法支撑有意义的分析。