Reproducibility of COVID-era infectious disease models
Alec S. Henderson,
Roslyn I. Hickson,
Morgan Furlong,
Emma S. McBryde,
Michael T. Meehan
Affiliations
Alec S. Henderson
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia; Corresponding author.
Roslyn I. Hickson
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia; Commonwealth Scientific Industrial Research Organisation (CSIRO), Townsville, Australia
Morgan Furlong
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
Emma S. McBryde
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
Michael T. Meehan
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia
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.
