Transparency assessment of COVID-19 models.
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ABSTRACT:
SUBMITTER: Jalali MS
PROVIDER: S-EPMC7833180 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Transparency assessment of COVID-19 models.
Jalali Mohammad S MS DiGennaro Catherine C Sridhar Devi D
The Lancet. Global health 20201027 12
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Project description: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.
| S-EPMC9543956 | biostudies-literature
Project description:ObjectiveWe aimed to assess the adherence to transparency practices (data availability, code availability, statements of protocol registration and conflicts of interest and funding disclosures) and FAIRness (Findable, Accessible, Interoperable, and Reusable) of shared data from open access COVID-19-related articles published in dental journals available from the Europe PubMed Central (PMC) database.MethodsWe searched and exported all COVID-19-related open-access articles from PubMed-indexed dental journals available in the Europe PMC database in 2020 and 2021. We detected transparency indicators with a validated and automated tool developed to extract the indicators from the downloaded articles. Basic journal- and article-related information was retrieved from the PMC database. Then, from those which had shared data, we assessed their accordance with FAIR data principles using the F-UJI online tool (f-uji.net).ResultsOf 650 available articles published in 59 dental journals, 74% provided conflicts of interest disclosure and 40% funding disclosure and 4% were preregistered. One study shared raw data (0.15%) and no study shared code. Transparent practices were more common in articles published in journals with higher impact factors, and in 2020 than in 2021. Adherence to the FAIR principles in the only paper that shared data was moderate.ConclusionWhile the majority of the papers had a COI disclosure, the prevalence of the other transparency practices was far from the acceptable level. A much stronger commitment to open science practices, particularly to preregistration, data and code sharing, is needed from all stakeholders.
| S-EPMC9019132 | biostudies-literature
Project description:The COVID-19-the worst pandemic since the Spanish flu-has dramatically changed the world, with a significant number of people suffering from and dying of the disease. Some scholars argue that democratic governments are disadvantaged in coping with the current pandemic mainly because they cannot intervene in their citizens' lives as aggressively as their authoritarian counterparts. Other scholars, however, suggest that possible data manipulation may account for the apparent advantage of authoritarian countries. Taking such a possibility seriously, this paper analyzes the relationship between political regimes, data transparency, and COVID-19 deaths using cross-national data for over 108 countries, obtained from Worldometer COVID-19 Data, Polity V Project, Variety of Democracy (V-Dem) Project, HRV Transparency Project among other sources. Regression analyses indicate that authoritarian countries do not necessarily tend to have fewer COVID-19 deaths than their democratic counterparts after controlling for other factors, especially data transparency. The transparency variable itself, on the other hand, is positively correlated with the number of death cases more consistently (P <0.05). Overall, the estimation results point to the possible data manipulation, not the nature of regime characteristics itself, as a more significant source for the seemingly low casualty rates in authoritarian countries.
| S-EPMC8219996 | biostudies-literature
Project description:Human beings have experienced a serious public health event as the new pneumonia (COVID-19), caused by the severe acute respiratory syndrome coronavirus has killed more than 3000 people in China, most of them elderly or people with underlying chronic diseases or immunosuppressed states. Rapid assessment and early warning are essential for outbreak analysis in response to serious public health events. This paper reviews the current model analysis methods and conclusions from both micro and macro perspectives. The establishment of a comprehensive assessment model, and the use of model analysis prediction, is very efficient for the early warning of infectious diseases. This would significantly improve global surveillance capacity, particularly in developing regions, and improve basic training in infectious diseases and molecular epidemiology.
| S-EPMC7110270 | biostudies-literature
Project description:BackgroundWe aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles.MethodsWe searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses.ResultsThe total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7-85.8 95%) and protocol registration (53.5%, 95% CI: 50.7-56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3-2.8) and code (0.4%, 95% CI: 0.4-0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited.ConclusionWhile most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders.
| S-EPMC10370694 | biostudies-literature
Project description:BackgroundIn the context of the COVID-19 pandemic, randomized controlled trials (RCTs) are essential to support clinical decision-making. We aimed (1) to assess and compare the reporting characteristics of RCTs between preprints and peer-reviewed publications and (2) to assess whether reporting improves after the peer review process for all preprints subsequently published in peer-reviewed journals.MethodsWe searched the Cochrane COVID-19 Study Register and L·OVE COVID-19 platform to identify all reports of RCTs assessing pharmacological treatments of COVID-19, up to May 2021. We extracted indicators of transparency (e.g., trial registration, data sharing intentions) and assessed the completeness of reporting (i.e., some important CONSORT items, conflict of interest, ethical approval) using a standardized data extraction form. We also identified paired reports published in preprint and peer-reviewed publications.ResultsWe identified 251 trial reports: 121 (48%) were first published in peer-reviewed journals, and 130 (52%) were first published as preprints. Transparency was poor. About half of trials were prospectively registered (n = 140, 56%); 38% (n = 95) made their full protocols available, and 29% (n = 72) provided access to their statistical analysis plan report. A data sharing statement was reported in 68% (n = 170) of the reports of which 91% stated their willingness to share. Completeness of reporting was low: only 32% (n = 81) of trials completely defined the pre-specified primary outcome measures; 57% (n = 143) reported the process of allocation concealment. Overall, 51% (n = 127) adequately reported the results for the primary outcomes while only 14% (n = 36) of trials adequately described harms. Primary outcome(s) reported in trial registries and published reports were inconsistent in 49% (n = 104) of trials; of them, only 15% (n = 16) disclosed outcome switching in the report. There were no major differences between preprints and peer-reviewed publications. Of the 130 RCTs published as preprints, 78 were subsequently published in a peer-reviewed journal. There was no major improvement after the journal peer review process for most items.ConclusionsTransparency, completeness, and consistency of reporting of COVID-19 clinical trials were insufficient both in preprints and peer-reviewed publications. A comparison of paired reports published in preprint and peer-reviewed publication did not indicate major improvement.
| S-EPMC9510360 | biostudies-literature
Project description:The recent emergence of COVID-19 presents a major global crisis. Profound knowledge gaps remain about the interaction between the virus and the immune system. Here, we used a systems biology approach to analyze immune responses in 76 COVID-19 patients and 69 age and sex- matched controls, from Hong Kong and Atlanta. Mass cytometry revealed prolonged plasmablast and effector T cell responses, reduced myeloid expression of HLA-DR and inhibition of mTOR signaling in plasmacytoid DCs (pDCs) during infection. Production of pro-inflammatory cytokines plasma levels of inflammatory mediators, including EN-RAGE, TNFSF14, and Oncostatin-M, which correlated with disease severity, and increased bacterial DNA and endotoxin in plasma in and reduced HLA-DR and CD86 but enhanced EN-RAGE expression in myeloid cells in severe transient expression of IFN stimulated genes in moderate infections, consistent with transcriptomic analysis of bulk PBMCs, that correlated with transient and low levels of plasma COVID-19.
2020-07-31 | GSE152418 | GEO
Project description:To understand how COVID-19 alters the platelet transcriptome.
2021-09-09 | GSE176480 | GEO
Project description:CITE-seq of 7 patients with COVID-19 and 5 healthy controls
2020-08-07 | GSE155673 | GEO
Project description:In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.
| S-EPMC10584164 | biostudies-literature