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  • Gerlee, Philip,1980Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Chalmers tekniska högskola,Chalmers University of Technology,University of Gothenburg,Chalmers Univ Technol, Sweden; Univ Gothenburg, Sweden (author)

Predicting regional COVID-19 hospital admissions in Sweden using mobility data.

  • Article/chapterEnglish2021

Publisher, publication year, extent ...

  • 2021-12-17
  • Springer Science and Business Media LLC,2021

Numbers

  • LIBRIS-ID:oai:gup.ub.gu.se/312030
  • https://gup.ub.gu.se/publication/312030URI
  • https://doi.org/10.1038/s41598-021-03499-yDOI
  • https://research.chalmers.se/publication/527968URI
  • https://lup.lub.lu.se/record/4854870a-d49c-4e5f-8242-21b06dfaaeb5URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-182375URI

Supplementary language notes

  • Language:English

Part of subdatabase

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Funding Agencies|Chalmers University of Technology
  • 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 3weeks 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 3weeks in advance.

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  • Karlsson, JuliaSahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden (author)
  • Fritzell, IngridSahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden (author)
  • Brezicka, Thomas,1961Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden(Swepub:gu)xbreth (author)
  • Spreco, ArminLinköpings universitet,Linköping University,Avdelningen för samhälle och hälsa,Medicinska fakulteten,Region Östergötland, Enheten för folkhälsa(Swepub:liu)armsp05 (author)
  • Timpka, ToomasLinköpings universitet,Linköping University,Avdelningen för samhälle och hälsa,Medicinska fakulteten,Region Östergötland, Enheten för folkhälsa(Swepub:liu)tooti02 (author)
  • Jöud, AnnaLund University,Lunds universitet,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,Tillämpad epidemiologi,Forskargrupper vid Lunds universitet,Epidemiologi,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine,Applied epidemiology,Lund University Research Groups,Epidemiology,Skåne University Hospital,Lund Univ, Sweden; Skane Univ Hosp, Sweden(Swepub:lu)med-aju (author)
  • Lundh, Torbjörn,1965Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, Tillämpad matematik och statistik,Department of Mathematical Sciences, Applied Mathematics and Statistics,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Chalmers Univ Technol, Sweden; Univ Gothenburg, Sweden(Swepub:cth)torbjrn (author)
  • Göteborgs universitetInstitutionen för matematiska vetenskaper (creator_code:org_t)

Related titles

  • In:Scientific reports: Springer Science and Business Media LLC11:12045-2322

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