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.
Författare
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 24171Ämneskategorier
Sannolikhetsteori och statistik
Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi
DOI
10.1038/s41598-021-03499-y
PubMed
34921175