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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

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 o...

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Autores principales: Gerlee, Philip, Jöud, Anna, Spreco, Armin, Timpka, Toomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345013/
https://www.ncbi.nlm.nih.gov/pubmed/35918476
http://dx.doi.org/10.1038/s41598-022-16159-6
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author Gerlee, Philip
Jöud, Anna
Spreco, Armin
Timpka, Toomas
author_facet Gerlee, Philip
Jöud, Anna
Spreco, Armin
Timpka, Toomas
author_sort Gerlee, Philip
collection PubMed
description 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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spelling pubmed-93450132022-08-03 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 Spreco, Armin Timpka, Toomas Sci Rep Article 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. Nature Publishing Group UK 2022-08-02 /pmc/articles/PMC9345013/ /pubmed/35918476 http://dx.doi.org/10.1038/s41598-022-16159-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gerlee, Philip
Jöud, Anna
Spreco, Armin
Timpka, Toomas
Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title_full Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title_fullStr Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title_full_unstemmed Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title_short Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
title_sort computational models predicting the early development of the covid-19 pandemic in sweden: systematic review, data synthesis, and secondary validation of accuracy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345013/
https://www.ncbi.nlm.nih.gov/pubmed/35918476
http://dx.doi.org/10.1038/s41598-022-16159-6
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