A systematic review of statistical model selection techniques used to predict COVID-19 health outcomes

Background: Following the outbreak of the Covid-19 pandemic, the scientific community rapidly developed models to predict health outcomes. The Strengthening Analytical Thinking for Observational Studies (STRATOS) initiative's Topic Group 2 (TG2) ‘selection of variables and functional forms [for full text, please go to the a.m. URL]

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Publication Details

Journal
German Medical Science (German Research Foundation)
Published
2025-11-03
DOI
https://doi.org/10.3205/25gmds094
Primary Topic
Statistical Methods in Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
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article

A systematic review of statistical model selection techniques used to predict COVID-19 health outcomes

German Medical Science (German Research Foundation)
Statistical Methods in Epidemiology
article

A systematic review of statistical model selection techniques used to predict COVID-19 health outcomes

article en

Abstract

Background: Following the outbreak of the Covid-19 pandemic, the scientific community rapidly developed models to predict health outcomes. The Strengthening Analytical Thinking for Observational Studies (STRATOS) initiative's Topic Group 2 (TG2) ‘selection of variables and functional forms [for full text, please go to the a.m. URL]

German Medical Science (German Research Foundation)
University of Liverpool (GB), Liverpool School of Tropical Medicine (GB), Johannes Gutenberg University Mainz (DE), Malawi-Liverpool-Wellcome Trust Clinical Research Programme (MW), University Medical Center of the Johannes Gutenberg University Mainz (DE), Medical University of Vienna (AT)
Good health and well-being
Openalex Percentile: Top 36%
Statistical Methods in Epidemiology
DOIOpen Access PDFOpenAlex
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A systematic review of statistical model selection techniques used to predict COVID-19 health outcomes · German Medical Science (German Research Foundation) (2025) | TGRS Research Map | TGRS