A meta-epidemiological assessment of transparency indicators of infectious disease models
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
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.
Article activity feed
-
-
SciScore for 10.1101/2022.04.11.22273744: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization To further validate the performance of the algorithms in detecting code sharing and data sharing reliably, a random sample of 10% of publications that the algorithm identified as sharing code and 10% of those that the algorithm identified as sharing data were manually assessed looking into whether the statements indeed represented true sharing. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: For example, to identify code sharing, rtransparent looks for “code” and “available” as well as the repository “GitHub” and its variations, and in a paper selected (53) from our dataset it finds … SciScore for 10.1101/2022.04.11.22273744: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization To further validate the performance of the algorithms in detecting code sharing and data sharing reliably, a random sample of 10% of publications that the algorithm identified as sharing code and 10% of those that the algorithm identified as sharing data were manually assessed looking into whether the statements indeed represented true sharing. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: For example, to identify code sharing, rtransparent looks for “code” and “available” as well as the repository “GitHub” and its variations, and in a paper selected (53) from our dataset it finds the following: “the model and code for reproducing all figures in this manuscript from model output are publicly available online (https://github.com/bdi-pathogens/openabm-covid19-model-paper)” The approach has been previously validated and tested in Serghiou et al. (33) across the entire biomedical literature and has a positive predictive value (PPV) of 88.2% (81.7%-93.8%) and negative predictive value (NPV) of 98.6% (96.2-99.9%) for code sharing; 93.0% (88.0%-97.0%) and 94.4% (89.1%-97.0%) for data sharing, 92.1% (88.3-98.6%) and 99.8% (99.7%-99.9 Table 2: Resources
Software and Algorithms Sentences Resources For each eligible article we used PubMed to extract information on metadata that included PMID, PMCID, publication year, journal name and the R package rtransparent(33) to extract the following transparency indicators: (i) code sharing (ii) data sharing (iii) (pre-)registration, (iv) COI and (v) funding statements. rtransparent searches through the full text of the papers for specific words or phrases that strongly suggest that the aforementioned transparency indicators are present in that particular paper. PubMedsuggested: (PubMed, RRID:SCR_004846)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
