ArticleSpatial and spatio-temporal epidemiology2022
Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland.
Article in Spatial and spatio-temporal epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Spatial epidemiology of dengue and chikungunya in Karnataka using GIS-based analysis.Global health action · 2025Article
- Article
- Seroprevalence and geospatial epidemiology of yaws: Evidence from Ghana.PLoS neglected tropical diseases · 2025Article
- Effect of Socioeconomic Strata and Land Cover on Dengue Hotspots in Medellin, Colombia.The American journal of tropical medicine and hygiene · 2025Article
- Bayesian spatio-temporal modeling of severe acute respiratory syndrome in Brazil: A comparative analysis across pre-, during, and post-COVID-19 eras.Infectious Disease Modelling · 2025Article
- Spatio-temporal clustering analysis of COVID-19 cases in Johor.Infectious Disease Modelling · 2024Article
- Review
- Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.PloS one · 2024Article
- Spatiotemporal modeling of COVID-19 spread: unveiling socioeconomic disparities and patterns, across social classes in the urban population of Kermanshah, Iran.Frontiers in public health · 2024Article
- Housing situations and local COVID-19 infection dynamics using small-area data.Scientific reports · 2023Article
- Spatiotemporal pattern of COVID-19 mortality and its relationship with socioeconomic and environmental factors in England.Spatial and spatio-temporal epidemiology · 2023Article
- Potential Epidemic Vulnerability and Socioepidemiological Profile of SARS-CoV2 in the Brazilian Northeast Region.Tropical medicine and infectious disease · 2023Article
- Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling.International journal of environmental research and public health · 2023Article
- Unraveling the Variation Pattern ofTropical medicine and infectious disease · 2023Article
- Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting.Frontiers in public health · 2023Article
- Geographic hot spots of kidney transplant candidates wait-listed post-dialysis.Clinical transplantation · 2022Article
- Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis.BMC public health · 2022Article
- Measurement of contagion spatial spread probability in public places: A case study on COVID-19.Applied geography (Sevenoaks, England) · 2022Article
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Authors and funding
5 authors.
Funding
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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.
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