Henderson, Alec S., Hickson, Roslyn I., Furlong, Morgan, McBryde, Emma S., and Meehan, Michael T. (2024) Reproducibility of COVID-era infectious disease models. Epidemics, 46. 100743.

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Abstract

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.

Item ID:	82997
Item Type:	Article (Research - C1)
ISSN:	1878-0067
Keywords:	Infectious disease modelling, COVID-19, Open science, Reproducibility
Copyright Information:	© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited:	20 Jun 2024 06:01
FoR Codes:	42 HEALTH SCIENCES > 4202 Epidemiology > 420205 Epidemiological modelling @ 100%
SEO Codes:	20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response) @ 80% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 20%
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