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

Gerlee, Philip ; Karlsson, Julia ; Fritzell, Ingrid ; Brezicka, Thomas ; Spreco, Armin ; Timpka, Toomas ; Jöud, Anna LU orcid and Lundh, Torbjörn (2021) In Scientific Reports 11(1).
Abstract

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... (More)

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.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
11
issue
1
article number
24171
publisher
Nature Publishing Group
external identifiers
  • scopus:85121559929
  • pmid:34921175
ISSN
2045-2322
DOI
10.1038/s41598-021-03499-y
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021, The Author(s).
id
4854870a-d49c-4e5f-8242-21b06dfaaeb5
date added to LUP
2022-02-21 08:48:28
date last changed
2024-05-30 09:55:42
@article{4854870a-d49c-4e5f-8242-21b06dfaaeb5,
  abstract     = {{<p>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.</p>}},
  author       = {{Gerlee, Philip and Karlsson, Julia and Fritzell, Ingrid and Brezicka, Thomas and Spreco, Armin and Timpka, Toomas and Jöud, Anna and Lundh, Torbjörn}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Predicting regional COVID-19 hospital admissions in Sweden using mobility data}},
  url          = {{http://dx.doi.org/10.1038/s41598-021-03499-y}},
  doi          = {{10.1038/s41598-021-03499-y}},
  volume       = {{11}},
  year         = {{2021}},
}