Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Mika Siljander; Ruut Uusitalo; Petri Pellikka; Sanna Isosomppi; Olli Vapalahti

doi:10.1016/j.sste.2022.100493

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Spat Spatiotemporal Epidemiol. 2022 Jun:41:100493. doi: 10.1016/j.sste.2022.100493. Epub 2022 Feb 5.

Authors

Mika Siljander¹, Ruut Uusitalo², Petri Pellikka³, Sanna Isosomppi⁴, Olli Vapalahti⁵

Affiliations

¹ Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland. Electronic address: mika.siljander@helsinki.fi.
² Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland; Department of Veterinary Biosciences, Agnes Sjöberginkatu 2, P.O. Box 66, FI-00014 University of Helsinki, Helsinki, Finland.
³ Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, Finland; Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430000, China.
⁴ Epidemiological Operations Unit, P.O. Box 8650, 00099 City of Helsinki, Finland.
⁵ Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland; Department of Veterinary Biosciences, Agnes Sjöberginkatu 2, P.O. Box 66, FI-00014 University of Helsinki, Helsinki, Finland; Virology and Immunology, Diagnostic Center, HUSLAB, Helsinki University Hospital, Helsinki, Finland.

Abstract

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High-high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

Keywords: COVID-19; LISA); Regression; SARS-CoV-2; Space-time clusters; Spatial autocorrelation (Moran's I.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
Cluster Analysis
Finland / epidemiology
Humans
SARS-CoV-2*
Spatial Analysis
Spatio-Temporal Analysis