A meta-epidemiological assessment of transparency indicators of infectious disease models release_lvghfloen5eerlrsz7ft5vs2sy

by Emmanuel A. Zavalis, John Ioannidis

Released as a post by Cold Spring Harbor Laboratory.

2022  

Abstract

Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Manual assessment of 10% of the articles that were identified by the text mining algorithms as fulfilling transparency indicators showed that 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data; and all had COI and funding statements, but 95.8% disclosed no conflict and 11.7% reported no funding. On manual assessment, 5/6 articles identified as registered had indeed been registered. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
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Date   2022-04-16
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