ABSTRACT
Since the emergence of SARS-CoV-2 and the pandemic, massive amounts of daily data on incidence are being collected by governments and public health authorities, providing relevant information about the dissemination of pandemic in terms of its virological evolution and spatiotemporal distribution of cases, hospitalization, and deaths. We propose a novel approach combining functional data analysis and unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying SARS-CoV-2 incidence. We modelled the daily COVID-19 confirmed cases by municipality as a function of time using functional principal component analysis to describe their temporal evolution in order to outline the main temporal patterns of variability. Municipalities were classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. The proposed approach is applied to mainland Portugal with data collected between August 2020 and March 2022 by municipality. The results obtained discriminate northern and coastal regions from southern and hinterland, and the effects in 2020-21 from the effects in 2021-22 autumn-winter seasons. Spatiotemporal patterns and classification of municipalities agree with results reported by other works and provides proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach extends and refines existing exploratory tools for spatiotemporal analysis of public health data.