Recent evolution in data acquisition technologies enabled the generation of high-dimensional, complex data in several research areas – in the sciences and engineering, among other disciplines. Increasingly sophisticated statistical and computational methods are needed in order to analyze these data. Functional data analysis (FDA) can be broadly employed to analyze functional data, i.e. data that vary over a continuum and can be naturally viewed as smooth curves or surfaces, exploiting information in their shapes.

In this talk, we present several (supervised and unsupervised) statistical learning methods for the analysis of functional data, and we show how FDA can be effectively employed to characterize COVID-19 in Italy. In particular, we investigate patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental covariates. We find that mobility and positivity can predict COVID-19 mortality, also when controlling for relevant covariates. Among the latter, primary care appears to mitigate mortality, and contacts in hospitals, schools and workplaces to aggravate it.