Predicting regional COVID-19 hospital admissions in Sweden using mobility data
Artikel i vetenskaplig tidskrift, 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.


Philip Gerlee

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Julia Karlsson

Sahlgrenska universitetssjukhuset

Ingrid Fritzell

Sahlgrenska universitetssjukhuset

Thomas Brezicka

Sahlgrenska universitetssjukhuset

Armin Spreco

Linköpings universitet

Center for Health Services Development

Toomas Timpka

Center for Health Services Development

Linköpings universitet

Anna Jöud

Lunds universitet

Skånes universitetssjukhus (SUS)

Torbjörn Lundh

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Scientific Reports

2045-2322 (ISSN) 20452322 (eISSN)

Vol. 11 1 24171


Sannolikhetsteori och statistik

Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi





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