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

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. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. 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.

Reproducibility of COVID-era infectious disease models

“…In this study we verified previous code transparency results [10, 11], finding that less than 20% of randomly selected infectious disease modelling studies provide working links to code. Notably, the top-cited set of studies released code far more frequently (48%); however this is may be a function of the journals in which these articles were published and the mandates which they impose.…”

Assessment of transparency indicators in Space Medicine

“…When compared to transparency indicators in infectious disease models, another medical field where in-depth assessments have been conducted in the past 14 , our sample showed lower rates of code sharing (3% versus 21.5%), but similar rates of data sharing. Another paper 15 assessed transparency of COVID-19-related research in dental journals.…”

Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research

“…In our sample, most papers provided the model description; however, the code used to implement the model and create the results was reported in less than 25% of the studies. In the previous review of COVID-19 computational modeling studies, researchers found that a similar 21.5% of publications reported the code (n = 288) (20). With increasingly complex computational methodologies in infectious disease modeling literature, withholding the exact data manipulation and analysis steps can impede the consistent regeneration of modeling results.…”

“…Our estimate was similar to the 60% (n = 29) of CDC-compiled COVID-19 modeling studies analyzed by Jalali et al and much higher than the 24.8% (n = 332) reviewed by Ioannidis et al that reported to share their data. However, Ioannidis et al, used a text mining algorithm which may not have picked up publications that shared their data (19,20). Many journals now require researchers to provide a data availability statement when submitting a publication but allow researchers to circumnavigate the provision by stating “the datasets and code are available from the corresponding author on reasonable request.” Some publishers require authors to make their publication data publicly available (21).…”