Investigating the Dark Figure of COVID-19 Cases in Austria: Borrowing From the Decode Genetics Study in Iceland

The number of undetected cases of SARS-CoV-2 infections is expected to be a multiple of the reported figures mainly due to the assumed high proportion of asymptomatic infections and to limited availability of trustworthy testing resources. Relying on the deCODE genetics study in Iceland, which offers large scale testing among the general population, we investigate the magnitude and uncertainty of the number of undetected cases COVID-19 cases in Austria. We formulate several scenarios relying on data on the number of COVID-19 cases which have been hospitalized, in intensive care, as well as on the number of deaths and positive tests in Iceland and Austria. We employ frequentist and Bayesian methods for estimating the dark figure in Austria based on the hypothesized scenarios and for accounting for the uncertainty surrounding this figure. Using data available on April 01, 2020, our study contains two main findings: First, we find the estimated number of infections to be on average around 8.35 times higher than the recorded number of infections. Second, the width of the uncertainty bounds associated with this figure depends highly on the statistical method employed. At a 95% level, lower bounds range from 3.96 to 6.83 and upper bounds range from 9.82 to 12.61. Overall, our findings confirm the need for systematic tests in the general population of Austria

Multivariate ordinal regression models: an analysis of corporate credit ratings

Correlated ordinal data typically arises from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal regression models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. Using simulated data sets with varying number of subjects, we investigate the performance of the pairwise likelihood estimates and find them to be robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US firms as well as an unbalanced panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework

mvord: An R Package for Fitting Multivariate Ordinal Regression Models

The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link. A flexible modeling framework for multiple ordinal measurements on the same subject is set up, which takes into consideration the dependence among the multiple observations by employing different error structures. Heterogeneity in the error structure across the subjects can be accounted for by the package, which allows for covariate dependent error structures. In addition, different regression coefficients and threshold parameters for each response are supported. If a reduction of the parameter space is desired, constraints on the threshold as well as on the regression coefficients can be specified by the user. The proposed multivariate framework is illustrated by means of a credit risk application

Statistical Modeling for Credit Ratings

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology

Multivariate Ordinal Regression Models: An Analysis of Corporate Credit Ratings

Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.Series: Research Report Series / Department of Statistics and Mathematic

The impact of the COVID-19 pandemic on European police officers: Stress, demands, and coping resources

Purpose: Facing the COVID-19 pandemic, police officers are confronted with various novel challenges, which might place additional strain on officers. This mixed-method study investigated officers’ strain over a three- month-period after the lockdown. Methods: In an online survey, 2567 police officers (77% male) from Austria, Germany, Switzerland, the Netherlands, and Spain participated at three measurement points per country in spring, 2020. Three-level growth curve models assessed changes in strain and its relation to stressor appraisal, emotion regulation, and pre- paredness through training. To add context to the findings, free response answers about officers’ main tasks, stressors, and crisis measures were coded inductively. Results: On average, officers seemed to tolerate the pandemic with slight decreases in strain over time. Despite substantial variance between countries, 66% of the variance occurred between individuals. Sex, work experience, stressor appraisal, emotion regulation, and preparedness significantly predicted strain. Risk of infection and deficient communication emerged as main stressors. Officers’ reports allowed to derive implications for governmental, organizational, and individual coping strategies during pandemics. Conclusion: Preparing for a pandemic requires three primary paths: 1) enacting unambiguous laws and increasing public compliance through media communication, 2) being logistically prepared, and 3) improving stress regulation skills in police training

CD4 recovery following antiretroviral treatment interruptions in children and adolescents with HIV infection in Europe and Thailand

Objectives: The aim of the study was to explore factors associated with CD4 percentage (CD4%) reconstitution following treatment interruptions (TIs) of antiretroviral therapy (ART). Methods: Data from paediatric HIV-infected cohorts across 17 countries in Europe and Thailand were pooled. Children on combination ART (cART; at least three drugs from at least two classes) for > 6 months before TI of ≥ 30 days while aged < 18 years were included. CD4% at restart of ART (r-ART) and in the long term (up to 24 months after r-ART) following the first TI was modelled using asymptotic regression. Results: In 779 children with at least one TI, the median age at first TI was 10.1 [interquartile range (IQR) 6.4, 13.6] years and the mean CD4% was 27.3% [standard deviation (SD) 11.0%]; the median TI duration was 9.0 (IQR 3.5, 22.5) months. In regression analysis, the mean CD4% was 19.2% [95% confidence interval (CI) 18.3, 20.1%] at r-ART, and 27.1% (26.2, 27.9%) in the long term, with half this increase in the first 6 months. r-ART and long-term CD4% values were highest in female patients and in children aged < 3 years at the start of TI. Long-term CD4% was highest in those with a TI lasting 1 to <3 months, those with r-ART after year 2000 and those with a CD4% nadir ≥ 25% (all P < 0.001). The effect of CD4% nadir during the TI differed significantly (P = 0.038) by viral suppression at the start of the TI; in children with CD4% nadir < 15% during TI, recovery was better in those virally suppressed prior to the TI; viral suppression was not associated with recovery in children with CD4% nadir ≥ 25%. Conclusions: After restart of ART following TI, most children reconstituted well immunologically. Nevertheless, several factors predicted better immunological reconstitution, including younger age and higher nadir CD4% during TI

Impact of COVID-19 on global burn care.

Worldwide, different strategies have been chosen to face the COVID-19-patient surge, often affecting access to health care for other patients. This observational study aimed to investigate whether the standard of burn care changed globally during the pandemic, and whether country´s income, geographical location, COVID-19-transmission pattern, and levels of specialization of the burn units affected reallocation of resources and access to burn care. The Burn Care Survey is a questionnaire developed to collect information on the capacity to provide burn care by burn units around the world, before and during the pandemic. The survey was distributed between September and October 2020. McNemar`s test analyzed differences between services provided before and during the pandemic, χ2 or Fisher's exact test differences between groups. Multivariable logistic regression analyzed the independent effect of different factors on keeping the burn units open during the pandemic. The survey was completed by 234 burn units in 43 countries. During the pandemic, presence of burn surgeons did not change (p = 0.06), while that of anesthetists and dedicated nursing staff was reduced (<0.01), and so did the capacity to manage patients in all age groups (p = 0.04). Use of telemedicine was implemented (p < 0.01), collaboration between burn centers was not. Burn units in LMICs and LICs were more likely to be closed, after adjustment for other factors. During the pandemic, most burn units were open, although availability of standard resources diminished worldwide. The use of telemedicine increased, suggesting the implementation of new strategies to manage burns. Low income was independently associated with reduced access to burn care. [Abstract copyright: Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

World checklist of hornworts and liverworts

A working checklist of accepted taxa worldwide is vital in achieving the goal of developing an online flora of all known plants by 2020 as part of the Global Strategy for Plant Conservation. We here present the first-ever worldwide checklist for liverworts (Marchantiophyta) and hornworts (Anthocerotophyta) that includes 7486 species in 398 genera representing 92 families from the two phyla. The checklist has far reaching implications and applications, including providing a valuable tool for taxonomists and systematists, analyzing phytogeographic and diversity patterns, aiding in the assessment of floristic and taxonomic knowledge, and identifying geographical gaps in our understanding of the global liverwort and hornwort flora. The checklist is derived from a working data set centralizing nomenclature, taxonomy and geography on a global scale. Prior to this effort a lack of centralization has been a major impediment for the study and analysis of species richness, conservation and systematic research at both regional and global scales. The success of this checklist, initiated in 2008, has been underpinned by its community approach involving taxonomic specialists working towards a consensus on taxonomy, nomenclature and distribution