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Computational models predicting the early development of the COVID-19 pandemic in Sweden : systematic review, data synthesis, and secondary validation of accuracy

Gerlee, Philip ; Jöud, Anna LU orcid ; Spreco, Armin and Timpka, Toomas (2022) In Scientific Reports 12(1).
Abstract

Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that... (More)

Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
12
issue
1
article number
13256
publisher
Nature Publishing Group
external identifiers
  • scopus:85135254496
  • pmid:35918476
ISSN
2045-2322
DOI
10.1038/s41598-022-16159-6
language
English
LU publication?
yes
id
817c43a2-6f48-4883-95bc-8c894dd688bf
date added to LUP
2022-09-13 12:27:46
date last changed
2024-06-27 20:43:20
@article{817c43a2-6f48-4883-95bc-8c894dd688bf,
  abstract     = {{<p>Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE &lt; 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.</p>}},
  author       = {{Gerlee, Philip and Jöud, Anna and Spreco, Armin and Timpka, Toomas}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Computational models predicting the early development of the COVID-19 pandemic in Sweden : systematic review, data synthesis, and secondary validation of accuracy}},
  url          = {{http://dx.doi.org/10.1038/s41598-022-16159-6}},
  doi          = {{10.1038/s41598-022-16159-6}},
  volume       = {{12}},
  year         = {{2022}},
}