Predicting regional COVID-19 hospital admissions in Sweden using mobility data
Journal article, 2021
The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.
Author
Philip Gerlee
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Julia Karlsson
Sahlgrenska University Hospital
Ingrid Fritzell
Sahlgrenska University Hospital
Thomas Brezicka
Sahlgrenska University Hospital
Armin Spreco
Linköping University
Center for Health Services Development
Toomas Timpka
Center for Health Services Development
Linköping University
Anna Jöud
Lund University
Skåne University Hospital
Torbjörn Lundh
University of Gothenburg
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Scientific Reports
2045-2322 (ISSN) 20452322 (eISSN)
Vol. 11 1 24171Subject Categories
Probability Theory and Statistics
Public Health, Global Health, Social Medicine and Epidemiology
DOI
10.1038/s41598-021-03499-y
PubMed
34921175