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)

Vol. 11 1 24171

Subject Categories

Probability Theory and Statistics

Public Health, Global Health, Social Medicine and Epidemiology

DOI

10.1038/s41598-021-03499-y

PubMed

34921175

More information

Latest update

1/17/2022