ABSTRACT
Background As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from COVID-19 patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies.Objectives The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models.Methods All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source.Results Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital-signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%.Conclusion An efficient and portable tool capable to discover inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.
Subject(s)
COVID-19ABSTRACT
Glycoprotein 90K, encoded by the interferon-stimulated gene LGALS3BP, displays broad antiviral activity. It reduces HIV-1 infectivity by interfering with Env maturation and virion incorporation, and increases survival of Influenza A virus-infected mice via antiviral innate immune signaling. Here, we analyzed the expression of 90K/LGALS3BP in 44 hospitalized COVID-19 patients. 90K protein serum levels were significantly elevated in COVID-19 patients compared to uninfected sex- and age-matched controls. Furthermore, PBMC-associated concentrations of 90K protein were overall reduced by SARS-CoV-2 infection in vivo, suggesting enhanced secretion into the extracellular space. Mining of published PBMC scRNA-seq datasets uncovered monocyte-specific induction of LGALS3BP mRNA expression in COVID-19 patients. In functional assays, neither 90K overexpression in susceptible cell lines nor exogenous addition of purified 90K consistently inhibited SARS-CoV-2 infection. Our data suggests that 90K/LGALS3BP contributes to the global type I IFN response during SARS-CoV-2 infection in vivo without displaying detectable antiviral properties.
Subject(s)
COVID-19 , HIV Infections , Tumor Virus InfectionsABSTRACT
Background: With the absence of immunization, public health interventions are the basis for curbing the spread of the SARS-CoV-2 virus. Evidence of impact of non-pharmaceutical interventions (NPI) on SARS-CoV-2 spread in Germany is scarce. The objectives of this study were to use a Delphi-panel based assessment of the effectiveness of different COVID-19 specific prevention measures in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (SEIR: Susceptible - Exposed - Infectious - Removed). The SEIR model will be made available with a modifiable user interface.Methods: We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs being discussed in Germany on the SARS-CoV-2 transmission rate R0. Initial estimates were discussed and agreed upon in two Delphi stages resulting in a final set of average efficacy and compliance estimates for each NPI. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.·8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on pandemic course. The model was populated with the Delphi-panel results and varied in sensitivity analyses.Results: Efficacy and compliance estimates were obtained as follows (ranked by effectiveness): test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%, ban of large public events 26%/96%, contact reduction 33%/59%, closure of non-essential stores 10%/97%, closure of schools 10%/100%, working from home 12%/66%, closure of restaurants 8%/96%, and improved hand hygiene 7%/54%. Applying these NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic.Conclusion: Employing an evidence-educated Delphi-panel approach for generating NPI effectiveness estimates is feasible and could help to generate model simulations with close replication of reported infected cases in Germany. Future curbing scenarios require a combination of NPIs. A Delphi-panel based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.Funding Statement: None.Declaration of Interests: None declared.Ethics Approval Statement: Not applicable.