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Background: As the COVID-19 pandemic continues to spread across the globe, the search for an effective medication to treat the symptoms of COVID-19 continues as well. It would be desirable to identify a medication that is already in use for another condition and whose side effect profile and safety data are already known and approved.
Objective: The objective of this study was to evaluate the effect of different medications on typical COVID-19 symptoms by using data from an online surveillance survey.
Methods: Between early April and late-July 2020, a total of 3654 individuals in Lower Saxony, Germany, participated in an online symptom-tracking survey conducted through the app covid-nein-danke.de. The questionnaire comprised items on typical COVID-19 symptoms, age range, gender, employment in patient-facing healthcare, housing status, postal code, previous illnesses, permanent medication, vaccination status, results of reverse transcription polymerase chain reaction (RT-PCR) and antibody tests for COVID-19 diagnosis, and consequent COVID-19 treatment if applicable. Odds ratio estimates with corresponding 95% CIs were computed for each medication and symptom by using logistic regression models.
Results: Data analysis suggested a statistically significant inverse relationship between typical COVID-19 symptoms self-reported by the participants and self-reported statin therapy and, to a lesser extent, antihypertensive therapy. When COVID-19 diagnosis was based on restrictive symptom criteria (ie, presence of 4 out of 7 symptoms) or a positive RT-PCR test, a statistically significant association was found solely for statins (odds ratio 0.28, 95% CI 0.1-0.78).
Conclusions: Individuals taking statin medication are more likely to have asymptomatic COVID-19, in which case they may be at an increased risk of transmitting the disease unknowingly. We suggest that the results of this study be incorporated into symptoms-based surveillance and decision-making protocols in regard to COVID-19 management. Whether statin therapy has a beneficial effect in combating COVID-19 cannot be deduced based on our findings and should be investigated by further study.
The economics of COVID-19
(2020)
Purpose
Within a very short period of time, the worldwide pandemic triggered by the novel coronavirus has not only claimed numerous lives but also caused severe limitations to daily private as well as business life. Just about every company has been affected in one way or another. This first empirical study on the effects of the COVID-19 crisis on family firms allows initial conclusions to be drawn about family firm crisis management.
Design/methodology/approach
Exploratory qualitative research design based on 27 semi-structured interviews with key informants of family firms of all sizes in five Western European countries that are in different stages of the crisis.
Findings
The COVID-19 crisis represents a new type and quality of challenge for companies. These companies are applying measures that can be assigned to three different strategies to adapt to the crisis in the short term and emerge from it stronger in the long run. Our findings show how companies in all industries and of all sizes adapt their business models to changing environmental conditions within a short period of time. Finally, the findings also show that the crisis is bringing about a significant yet unintended cultural change. On the one hand, a stronger solidarity and cohesion within the company was observed, while on the other hand, the crisis has led to a tentative digitalization.
Originality/value
To the knowledge of the authors, this is the first empirical study in the management realm on the impacts of COVID-19 on (family) firms. It provides cross-national evidence of family firms' current reactions to the crisis.
From learners to educators
(2020)
The rapid growth of technology and its evolving potential to support the transformation of teaching and learning in post-secondary institutions is a major challenge to the basic understanding of both the university and the communities it serves. In higher education, the standard forms of learning and teaching are increasingly being challenged and a more comprehensive process of differentiation is taking place. Student-centered teaching methods are becoming increasingly important in course design and the role of the lecturer is changing from the knowledge mediator to moderator and learning companion. However, this is accelerating the need for strategically planned faculty support and a reassessment of the role of teaching and learning. Even though the benefits of experience-based learning approaches for the development of life skills are well known, most knowledge transfer is still realized through lectures in higher education. Teachers have the goal to design the curriculum, new assignments, and share insights into evolving pedagogy. Student engagement could be the most important factor in the learning success of university students, regardless of the university program or teaching format. Against this background, this article presents the development, application, and initial findings of an innovative learning concept. In this concept, students are allowed to deal with a scientific topic, but instead of a presentation and a written elaboration, their examination consists of developing an online course in terms of content, didactics, and concept to implement it in a learning environment, which is state of the art. The online courses include both self-created teaching material and interactive tasks. The courses are created to be available to other students as learning material after a review process and are thus incorporated into the curriculum.
Background:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.
Results:
Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.
Conclusions:
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
Drawing on three waves of survey data from a non-probability sample from Germany, this paper examines two opposing expectations about the pandemic's impacts on gender equality: The optimistic view suggests that gender equality has increased, as essential workers in Germany have been predominantly female and as fathers have had more time for childcare. The pessimistic view posits that lockdowns have also negatively affected women's jobs and that mothers had to shoulder the additional care responsibilities. Overall, our exploratory analyses provide more evidence supporting the latter view. Parents were more likely than non-parents to work fewer hours during the pandemic than before, and mothers were more likely than fathers to work fewer hours once lockdowns were lifted. Moreover, even though parents tended to divide childcare more evenly, at least temporarily, mothers still shouldered more childcare work than fathers. The division of housework remained largely unchanged. It is therefore unsurprising that women, in particular mothers, reported lower satisfaction during the observation period. Essential workers experienced fewer changes in their working lives than respondents in other occupations.
This paper examines and discusses the biases and pitfalls of retrospective survey questions that are currently being used in many medical, epidemiological, and sociological studies on the COVID-19 pandemic. By analyzing the consistency of answers to retrospective questions provided by respondents who participated in the first two waves of a survey on the social consequences of the COVID-19 pandemic, we illustrate the insights generated by a large body of survey research on the use of retrospective questions and recall accuracy.
Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI).
Digital inclusion
(2021)
In this thesis, we tackle two social disruptions: recent refugee waves in Germany and the COVID-19 pandemic. We focus on the use of information and communication technology (ICT) as a key means of alleviating these disruptions and promoting social inclusion. As social disruptions typically lead to frustration and fragmentation, it is essential to ensure the social inclusion of individuals and societies during such times.
In the context of the social inclusion of refugees, we focus on the Syrian refugees who arrived in Germany as of 2015, as they form a large and coherent refugee community. In particular, we address the role of ICTs in refugees’ social inclusion and investigate how different ICTs (especially smartphones and social networks) can foster refugees’ integration and social inclusion. In the context of the COVID-19 pandemic, we focus on the widespread unconventional working model of work from home (WFH). Our research here centers on the main constructs of WFH and the key differences in WFH experiences based on personal characteristics such as gender and parental status.
We reveal novel insights through four well-established research methods: literature review, mixed methods, qualitative method, and quantitative method. The results of our research have been published in the form of eight articles in major information systems venues and journals. Key results from the refugee research stream include the following: Smartphones represent a central component of refugee ICT use; refugees view ICT as a source of information and power; the social connectedness of refugees is strongly correlated with their Internet use; refugees are not relying solely on traditional methods to learn the German language or pursue further education; the ability to use smartphones anytime and anywhere gives refugees an empowering feeling of global connectedness; and ICTs empower refugees on three levels (community participation, sense of control, and self-efficacy).
Key insights from the COVID-19 WFH stream include: Gender and the presence of children under the age of 18 affect workers’ control over their time, technology usefulness, and WFH conflicts, while not affecting their WFH attitudes; and both personal and technology-related factors affect an individual’s attitude toward WFH and their productivity. Further insights are being gathered at the time of submitting this thesis.
This thesis contributes to the discussion within the information systems community regarding how to use different ICT solutions to promote the social inclusion of refugees in their new communities and foster an inclusive society. It also adds to the growing body of research on COVID-19, in particular on the sudden workplace transformation to WFH. The insights gathered in this thesis reveal theoretical implications and future opportunities for research in the field of information systems, practical implications for relevant stakeholders, and social implications related to the refugee crisis and the COVID-19 pandemic that must be addressed.
During COVID-19, various public institutions tried to shape citizens’ behaviour to slow the spread of the pandemic. How did their authority affect citizens’ support of public measures taken to combat the spread of COVID-19? The article makes two contributions. First, it presents a novel conceptualisation of authority as a source heuristic. Second, it analyses the authority of four types of public institutions (health ministries, universities, public health agencies, the WHO) in two countries (Germany and the UK), drawing on novel data from a survey experiment conducted in May 2020. On average, institutional endorsements seem to have mattered little. However, there is an observable polarisation effect where citizens who ascribe much expertise to public institutions support COVID-19 measures more than the control group. Furthermore, those who ascribe little expertise support them less than the control group. Finally, neither perception of biases nor exposure to institutions in public debates seems consistently to affect their authority.
There is an urgent need for screening of patients with a communicable viral disease to cut infection chains. Recently, we demonstrated that ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is able to identify influenza-A infections in patients' breath. With a decreasing influenza epidemic and upcoming SARS-CoV-2 infections we proceeded further and analyzed patients with suspected SARS-CoV-2 infections. In this study, the nasal breath of 75 patients (34 male, 41 female, aged 64.4 +/- 15.4 years) was investigated by MCC-IMS for viral infections. Fourteen were positively diagnosed with influenza-A infection and sixteen with SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) of nasopharyngeal swabs. In one patient RT-PCR was highly suspicious of SARS-CoV-2 but initially inconclusive. The remaining 44 patients served as controls. Breath fingerprints for specific infections were assessed by a combination of cluster analysis and multivariate statistics. There were no significant differences in gender or age according to the groups. In the cross validation of the discriminant analysis 72 of the 74 clearly defined patients could be correctly classified to the respective group. Even the inconclusive patient could be mapped to the SARS-CoV-2 group by applying the discrimination functions. Conclusion: SARS-CoV-2 infection and influenza-A infection can be detected with the help of MCC-IMS in breath in this pilot study. As this method provides a fast non-invasive diagnosis it should be further developed in a larger cohort for screening of communicable viral diseases. A validation study is ongoing during the second wave of COVID-19.
Trial registration: ClinicalTrial.gov, NCT04282135 Registered 20 February 2020-Retrospectively registered,
Despite new challenges like climate change and digitalization, global and regional organizations recently went through turbulent times due to a lack of support from several of their member states. Next to this crisis of multilateralism, the COVID-19 pandemic now seems to question the added value of international organizations for addressing global governance issues more specifically. This article analyses this double challenge that several organizations are facing and compares their ways of managing the crisis by looking at their institutional and political context, their governance structure, and their behaviour during the pandemic until June 2020. More specifically, it will explain the different and fragmented responses of the World Health Organization, the European Union and the International Monetary Fund/World Bank. With the aim of understanding the old and new problems that these international organizations are trying to solve, this article argues that the level of autonomy vis-a-vis the member states is crucial for understanding the politics of crisis management. <br /> Points for practitioners <br /> As intergovernmental bodies, international organizations require authorization by their member states. Since they also need funding for their operations, different degrees of autonomy also matter for reacting to emerging challenges, such as the COVID-19 pandemic. The potential for international organizations is limited, though through proactive and bold initiatives, they can seize the opportunity of the crisis and partly overcome institutional and political constraints.
Yes, we can (?)
(2021)
The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.
Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
(2021)
Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
Die vorliegende Studie zeigt, dass Daten in der Krise eine herausragende Bedeutung für die wissenschaftliche Politikberatung, administrative Entscheidungsvorbereitung und politische Entscheidungsfindung haben. In der Krise gab es jedoch gravierende Kommunikationsprobleme und Unsicherheiten in der wechselseitigen Erwartungshaltung von wissenschaftlichen Datengebern und politisch-administrativen Datennutzern. Die Wissensakkumulation und Entscheidungsabwägung wurde außerdem durch eine unsichere und volatile Datenlage zum Pandemiegeschehen, verbunden mit einer dynamischen Lageentwicklung, erschwert. Nach wie vor sind das Bewusstsein und wechselseitige Verständnis für die spezifischen Rollenprofile der am wissenschaftlichen Politikberatungsprozess beteiligten Akteure sowie insbesondere deren Abgrenzung als unzureichend einzuschätzen.
Die Studie hat darüber hinaus vielfältige Defizite hinsichtlich der Verfügbarkeit, Qualität, Zugänglichkeit, Teilbarkeit und Nutzbarkeit von Daten identifiziert, die Datenproduzenten und -verwender vor erhebliche Herausforderungen stellen und einen umfangreichen Reformbedarf aufzeigen, da zum einen wichtige Datenbestände für eine krisenbezogene Politikberatung fehlen. Zum anderen sind die Tiefenschärfe und Differenziertheit des verfügbaren Datenbestandes teilweise unzureichend. Dies gilt z.B. für sozialstrukturelle Daten zur Schwere der Pandemiebetroffenheit verschiedener Gruppen oder für kleinräumige Daten über Belastungs- und Kapazitätsparameter, etwa zur Personalabdeckung auf Intensivstationen, in Gesundheitsämtern und Pflegeeinrichtungen. Datendefizite sind ferner im Hinblick auf eine ganzheitliche Pandemiebeurteilung festzustellen, zum Beispiel bezüglich der Gesundheitseffekte im weiteren Sinne, die aufgrund der ergriffenen Maßnahmen entstanden sind (Verschiebung oder Wegfall von Operationen, Behandlungen und Prävention, aber auch häusliche Gewalt und psychische Belastungen). Mangels systematischer Begleitstudien und evaluativer Untersuchungen, u.a. auch zu lokalen Pilotprojekten und Experimenten, bestehen außerdem Datendefizite im Hinblick auf die Wirkungen von Eindämmungsmaßnahmen oder deren Aufhebung auf der gebietskörperschaftlichen Ebene.
Insgesamt belegt die Studie, dass es zur Optimierung der datenbasierten Politikberatung und politischen Entscheidungsfindung in und außerhalb von Krisen nicht nur darum gehen kann, ein „Mehr“ an Daten zu produzieren sowie deren Qualität, Verknüpfung und Teilung zu verbessern. Vielmehr müssen auch die Anreizstrukturen und Interessenlagen in Politik, Verwaltung und Wissenschaft sowie die Kompetenzen, Handlungsorientierungen und kognitiv-kulturellen Prägungen der verschiedenen Akteure in den Blick genommen werden. Es müssten also Anreize gesetzt und Strukturen geschaffen werden, um das Interesse, den Willen und das Können (will and skill) zur Datennutzung auf Seiten politisch-administrativer Entscheider und zur Dateneinspeisung auf Seiten von Wissenschaftlern zu stärken. Neben adressatengerechter Informationsaufbereitung geht es dabei auch um die Gestaltung eines normativen und institutionellen Rahmens, innerhalb dessen die Nutzung von Daten für Entscheidungen effektiver, qualifizierter, aber auch transparenter, nachvollziehbarer und damit demokratisch legitimer erfolgen kann.
Vor dem Hintergrund dieser empirischen Befunde werden acht Cluster von Optimierungsmaßnahmen vorgeschlagen:
(1) Etablierung von Datenstrecken und Datenteams,
(2) Schaffung regionaler Datenkompetenzzentren,
(3) Stärkung von Data Literacy und Beschleunigung des Kulturwandels in der öffentlichen Verwaltung,
(4) Datenstandardisierung, Interoperabilität und Registermodernisierung,
(5) Ausbau von Public Data Pools und Open Data Nutzung,
(6) Effektivere Verbindung von Datenschutz und Datennutzung,
(7) Entwicklung eines hochfrequenten, repräsentativen Datensatzes,
(8) Förderung der europäischen Daten-Zusammenarbeit.
The hospitality industry worldwide is among the hardest-hit industries from the COVID-19 lockdowns. Initial theoretical and practical observations in the hospitality industry indicate that business model innovation (BMI) might be a solution to recover from and successfully cope with the COVID-19 crisis. Interestingly, some firms in the hospitality industry already started to successfully adapt their business models. This study explores the why and how of these successful recovery attempts through BMI by conducting a multiple case study of six hospitality firms in Austria. We rely on interview data from managers together with one of their main stammgasts for each case, which we triangulate with secondary data for the analysis. Findings show that BMI is applied during and after the crisis to create new revenue streams and secure a higher level of liquidity, with an important role of stammgasts.