Risk factors for Covid-19 severity and fatality: a structured literature review.

PURPOSE: Covid-19 is a global threat that pushes health care to its limits. Since there is neither a vaccine nor a drug for Covid-19, people with an increased risk for severe and fatal courses of disease particularly need protection. Furthermore, factors increasing these risks are of interest in the search of potential treatments. A systematic literature review on the risk factors of severe and fatal Covid-19 courses is presented. METHODS: The review is carried out on PubMed and a publicly available preprint dataset. For analysis, risk factors are categorized and information regarding the study such as study size and location are extracted. The results are compared to risk factors listed by four public authorities from different countries. RESULTS: The 28 records included, eleven of which are preprints, indicate that conditions and comorbidities connected to a poor state of health such as high age, obesity, diabetes and hypertension are risk factors for severe and fatal disease courses. Furthermore, severe and fatal courses are associated with organ damages mainly affecting the heart, liver and kidneys. Coagulation dysfunctions could play a critical role in the organ damaging. Time to hospital admission, tuberculosis, inflammation disorders and coagulation dysfunctions are identified as risk factors found in the review but not mentioned by the public authorities. CONCLUSION: Factors associated with increased risk of severe or fatal disease courses were identified, which include conditions connected with a poor state of health as well as organ damages and coagulation dysfunctions. The results may facilitate upcoming Covid-19 research.

Basins of Attraction, Commitment Sets, and Phenotypes of Boolean Networks.

The attractors of Boolean networks and their basins have been shown to be highly relevant for model validation and predictive modeling, e.g., in systems biology. Yet, there are currently very few tools available that are able to compute and visualize not only attractors but also their basins. In the realm of asynchronous, non-deterministic modeling not only is the repertoire of software even more limited, but also the formal notions for basins of attraction are often lacking. In this setting, the difficulty both for theory and computation arises from the fact that states may be elements of several distinct basins. In this paper, we address this topic by partitioning the state space into sets that are committed to the same attractors. These commitment sets can easily be generalized to sets that are equivalent w.r.t. the long-term behaviors of pre-selected nodes which leads us to the notions of markers and phenotypes which we illustrate in a case study on bladder tumorigenesis. For every concept, we propose equivalent CTL model checking queries and an extension of the state of the art model checking software NuSMV is made available that is capable of computing the respective sets. All notions are fully integrated as three new modules in our Python package PyBoolNet, including functions for visualizing the basins, commitment sets, and phenotypes as quotient graphs and pie charts.