Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease model...

Full description

Saved in:
Bibliographic Details
Published in:Epidemics 2024-03, Vol.46, p.100743-100743, Article 100743
Main Authors: Henderson, Alec S., Hickson, Roslyn I., Furlong, Morgan, McBryde, Emma S., Meehan, Michael T.
Format: Article
Language:English
Subjects:
COVID-19
Infectious disease modelling
Open science
Reproducibility
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed. •88 out of 100 randomly sampled studies Infectious disease models were not computationally reproducible.•67 out of 100 top cited Infectious disease models were not computationally reproducible.•Journals mandating data release are significantly associated with code release.
ISSN:1755-4365
1878-0067
DOI:10.1016/j.epidem.2024.100743