Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis

TL;DR

This study analyzes and compares COVID-19 mortality patterns in Italy's first two waves using Functional Data Analysis, revealing differences in spread, associations with mobility, and the impact of restrictions.

Contribution

It applies Functional Data Analysis to characterize COVID-19 mortality curves across Italian provinces, highlighting differences between waves and the effects of mobility and restrictions.

Findings

Second wave spread more broadly and asynchronously

Positive correlation between mobility and mortality

Restrictions effectively reduced mortality

Abstract

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.