Publikationen
Hier finden Sie Links zu Publikationen und Ergebnissen, die aus den Forschungsvorhaben der akkreditierten Einrichtungen unter Verwendung der im Rahmen der Datenplattform bereitgestellten Daten erstellt wurden.
Im Sinne des wissenschaftlichen Diskurses werden ab dem Jahr 2021 die Forschungsarbeiten im Rahmen der Beiratssitzungen präsentiert.
In der jeweiligen Zitation der unten stehenden Publikationen ist daher zusätzlich vermerkt, ob bzw. wann die Präsentation im Beirat erfolgt ist, sowie gegebenenfalls ein Link zu den Foliensätzen.
Aziz, F., et al.(2021):
COVID-19 In-Hospital Mortality in People with Diabetes Is Driven by Comorbidities and Age—Propensity Score-Matched Analysis of Austrian National Public Health Institute Data.
Viruses 2021, 13, 2401. https://doi.org/10.3390/v13122401
Abstract | Full Text
It is a matter of debate whether diabetes alone or its associated comorbidities are responsible for severe COVID-19 outcomes. This study assessed the impact of diabetes on intensive care unit (ICU) admission and in-hospital mortality in hospitalized COVID-19 patients. Methods: A retrospective analysis was performed on a countrywide cohort of 40,632 COVID-19 patients hospitalized between March 2020 and March 2021. Data were provided by the Austrian data platform. The association of diabetes with outcomes was assessed using unmatched and propensity-score matched (PSM) logistic regression. Results: 12.2% of patients had diabetes, 14.5% were admitted to the ICU, and 16.2% died in the hospital. Unmatched logistic regression analysis showed a significant association of diabetes (odds ratio [OR]: 1.24, 95% confidence interval [CI]: 1.15–1.34, p < 0.001) with in-hospital mortality, whereas PSM analysis showed no significant association of diabetes with in-hospital mortality (OR: 1.08, 95%CI: 0.97–1.19, p = 0.146). Diabetes was associated with higher odds of ICU admissions in both unmatched (OR: 1.36, 95%CI: 1.25–1.47, p < 0.001) and PSM analysis (OR: 1.15, 95%CI: 1.04–1.28, p = 0.009). Conclusions: People with diabetes were more likely to be admitted to ICU compared to those without diabetes. However, advanced age and comorbidities rather than diabetes itself were associated with increased in-hospital mortality in COVID-19 patients.
Banabak, S., Kalasek, R., Pühringer, F., Soteropoulos, A., Zhang, Y. (2021):
Regionalökonomische Strukturen und COVID-19 in Österreich
Work in progress
Media | Präsentation im Beirat am 3.März 2021
Bathke, A., Happ, M. (2021):
Nowcasting von COVID-19 Todesfällen.
Work in progress | Präsentation im Beirat am 10.Februar 2021
Bauer, P., Brugger, J., König, F., Posch, M. (2021):
An international comparison of age and sex dependency of COVID-19 Deaths in 2020 - a descriptive analysis
Sci Rep 11, 19143 (2021). https://doi.org/10.1038/s41598-021-97711-8.
Abstract | Full Text
The number of reported coronavirus disease (COVID-19) deaths per 100,000 persons observed so far in 2020 is described in 15 European countries and the USA as dependent on age groups and sex. It is compared with the corresponding historic all-cause mortality per year depending on age and sex observed in these countries. Some common features exist although substantial differences in age and sex dependency of COVID-19 mortality were noted between countries. An exponential increase with age is a good model to describe and analyze both COVID-19 and all-cause mortality above 40 years old, where almost all COVID-19 deaths occur. Moreover, age dependency is stronger for COVID-19 mortality than for all-cause mortality, and males have an excess risk compared with women, which is less pronounced in the higher age groups. Additionally, concerning calendar time, differences in the age and sex dependency between countries were noted with the common tendency that male excess risk for COVID-19 mortality was smaller in the second half of the year.
Gleiss, A., Henderson, R., Schemper, M. (2020):
Degrees of necessity and of sufficiency: further results and extensions, with an application to COVID-19 mortality in Austria.
accepted by Statistics in Medicine.
Abstract | Präsentation im Beirat am 7.April 2021
The purpose of this paper is to extend to ordinal and nominal outcomes the measures of degree of necessity and sufficiency defined for dichotomous and survival time outcomes by Gleiss and Schemper (2019). A cause, represented by certain values of prognostic factors, is considered necessary for an event if, without the cause, the event cannot develop. It is considered sufficient for an event if the event is unavoidable in the presence of the cause. The degrees of necessity and sufficiency, ranging from zero to one, are simple, intuitive functions of unconditional and conditional probabilities of an event such as disease or death. These probabilities often will be derived from (proportional odds) logistic regression models; the measures, however, do not require any particular model. In addition, we study in detail the relationship between the proposed measures and the related explained variation summary for dichotomous outcomes, which are the common root for the developments for ordinal, nominal and survival outcomes. We introduce and analyse the Austrian covid-19 data, with the aim of quantifying any effects of age and other potentially prognostic factors on covid-19 mortality. This is achieved by standard regression methods but also in terms of the newly proposed measures. It is shown how they complement the toolbox of prognostic factor studies, in particular when comparing the importance of prognostic factors of different types and measured on different scales. In order to simplify application of these intuitive measures R functions and SAS macros are made available
Heiler, G., Reisch, T., Hurt, J., Forghani, M., Omani, A., Hanbury, A., Karimipour, F. (2020):
Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic.
arXiv:2008.10064 [cs.CY] (Preprint).
Abstract | Preprint | Präsentation im Beirat am 13.Jänner 2021
In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian public and private life came to a sudden halt. Here we assess the effect of the lock-down quantitatively for all regions in Austria and present an analysis of daily changes of human mobility throughout Austria using near-real-time anonymized mobile phone data. We describe an efficient data aggregation pipeline and analyze the mobility by quantifying mobile-phone traffic at specific point of interest (POI), analyzing individual trajectories and investigating the cluster structure of the origin-destination graph. We found a reduction of commuters at Viennese metro stations of over 80\% and the number of devices with a radius of gyration of less than 500 m almost doubled. The results of studying crowd-movement behavior highlight considerable changes in the structure of mobility networks, revealed by a higher modularity and an increase from 12 to 20 detected communities. We demonstrate the relevance of mobility data for epidemiological studies by showing a significant correlation of the outflow from the town of Ischgl (an early COVID-19 hotspot) and the reported COVID-19 cases with an 8-day time lag. This research indicates that mobile phone usage data permits the moment-by-moment quantification of mobility behavior for a whole country. We emphasize the need to improve the availability of such data in anonymized form to empower rapid response to combat COVID-19 and future pandemics.
Paetzold, J., Kimpel, J., Bates, K., Hummer, M., Krammer, F., von Laer, D., & Winner, H. (2022):
Impacts of rapid mass vaccination against SARS-CoV2 in an early variant of concern hotspot.
Nature Communications, 13, 612 (2022).
Abstract | Full Text
We study the real-life effect of an unprecedented rapid mass vaccination campaign. Following a large outbreak of the Beta variant in the district of Schwaz/Austria, 100,000 doses of BNT162b2 (Pfizer/BioNTech) were procured to mass vaccinate the entire adult population of the district between the 11th and 16th of March 2021. This made the district the first widely inoculated region in Europe. We examine the effect of this campaign on the number of infections, cases of variants of concern, hospital and ICU admissions. We compare Schwaz with (i) a control group of highly similar districts, and (ii) with populations residing in municipalities along the border of Schwaz which were just excluded from the campaign. We find large and significant decreases for all outcomes after the campaign. Our results suggest that rapid mass vaccination is an effective tool to curb the spread of SARS-CoV-2.
Popper N., Zechmeister M., Brunmeier D., Rippinger C., Weibrecht N., Urach C., Bicher M., Schneckenreither G., Rauber A. (2020):
Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation
Preprint, medRxiv 2020.11.07.20227462; doi: https://doi.org/10.1101/2020.11.07.20227462
Abstract | Preprint
We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.
Prager, L. (2021):
Examining Austrian COVID-19 Data in the Context of Linear Regression
Diplomarbeit, TU Wien, Institut für Stochastik und Wirtschaftsmathematik
Abstract
Background: The novel coronavirus SARS-CoV-2 has been a constant companion in our daily lives for more than a year now. Due to the economic and health damage of the pandemic, an analysis of the corona figures is inevitable.
Method: Linear regression models are used to quantify the relationship between the 7 day incidence in Austria and other variables. To obtain a ranking of explanatory variables by importance, the p-value of a t-test, the adjusted coefficient of determination R2adj and LASSO paths are considered. The statistic software R is used for calculation.
Results: For describing the 7 day incidence, important explanatory variables are the number of tests performed, outdoor temperature, the severity of lockdown measures, homeschooling, and the presence of holidays, while mobility data from Apple show lower importance. Overall, the goodness of fit is R2adj = 0.9555.
Conclusions: Although the linear regression model reaches its limits due to complex relationships among certain variables, the overall fit turns out to be very good. However, the choice of explanatory variables is difficult.
Scheiner, S., Ukaj, N., Hellmich, C. (2020):
Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule.
Chaos, Solitons & Fractals, 136, 109891.
Abstract | Full Text
The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SEIR models has remained largely unaltered—presuming that COVID-19 may be just “another epidemic”. We here take an alternative approach, by investigating the relevance of one key ingredient of the SEIR models, namely the death kinetics law. The latter is compared to an alternative approach, which we call infection-to-death delay rule. For that purpose, we check how well these two mathematical formulations are able to represent the publicly available country-specific data on recorded fatalities, across a selection of 57 different nations. Thereby, we consider that the model-governing parameters—namely, the death transmission coefficient for the death kinetics model, as well as the apparent fatality-to-case fraction and the characteristic fatal illness period for the infection-to-death delay rule—are time-invariant. For 55 out of the 57 countries, the infection-to-death delay rule turns out to represent the actual situation significantly more precisely than the classical death kinetics rule. We regard this as an important step towards making SEIR-approaches more fit for the COVID-19 spreading prediction challenge.
Ukaj, N., Scheiner, S., and Hellmich, C.(2021):
Toward “hereditary epidemiology”: A temporal Boltzmann approach to COVID-19 fatality trends.
Applied Physics Reviews 8, 041417 (2021) https://doi.org/10.1063/5.0062867.
Abstract | Full Text
Countless research contributions reflect two major concepts for modeling the spread of the COVID-19 pandemic: (i) ordinary differential equations for population compartments, such as infected or deceased persons (these approaches often exhibit limited predictive capabilities); and (ii) rules applied to digitally realized agents in the populations (these approaches often lack reliable input data and may become computationally overly expensive). As a remedy, we here introduce and discuss convolutional integrodifferential equations adapted from Boltzmann's hereditary mechanics, so as to predict COVID-19 fatality trends from the evolutions of newly infected persons. Replacing the classical statistical reasoning by deliberations arising from the notion of “virus loads” and the corresponding compliance of the infected population to these loads, model errors with respect to data recorded in 102 countries, territories, or US states can be drastically reduced, namely, up to 98% when compared to the traditional kinetics equation of Kermack and McKendrick. The coefficients of determination between model predictions and recorded data range from 94% to 100%, a precision hitherto unachieved in equation-based epidemic modeling.
Valka, F., Mishra, S., Scott, J., Flaxman, S., Bhatt, S., Gandy, A. (2020):
COVID-19 Model Austria
Interactive Model published online: https://covid19model.at
Abstract | Model
The results on this page have been computed using epidemia 0.6.0. Epidemia extends the Bayesian semi-mechanistic model proposed in Flaxman, S., Mishra, S., Gandy, A. et al. Nature 2020. The model is based on a self-renewal equation which uses time-varying reproduction number Rt to calculate the infections. However, due to a lot of uncertainty around reported cases in early part of epidemics, we use reported deaths to back-calculate the infections as a latent variable. Then the model utilizes these latent infections together with probabilistic lags related to SARS-CoV-2 to calibrate against the reported deaths and the reported cases since the beginning of June 2020.
Winner, H., Kimpel, J., Krammer, F., von Laer, D., Paetzold, J. (2021):
Can high SARS-CoV-2 adult vaccination rates help protect unvaccinated children? Evidence from a unique rapid mass vaccination campaign.
Research Square: https://www.researchsquare.com/article/rs-958479/v1
Abstract | Full Text
Background: Mass vaccination has the potential to bring the COVID-19 pandemic to a halt by not only protecting individuals who have been vaccinated but also by providing cross-protection to unvaccinated individuals, such as children. However, this indirect protection effect from a vaccinated population onto an unvaccinated group is extremely difficult to observe in real-world situations.
Methods: We studied cross-protection to unvaccinated individuals following an unprecedented rapid mass vaccination campaign in Europe. After a large outbreak of B.1.351 (Beta) in the district of Schwaz in Austria, the government offered every adult (16+) citizen of the district a vaccination with BNT162b2 between the 11th and 16th of March 2021. After this week, around 70% of the adult population of Schwaz had received their first dose, which made Schwaz the first widely inoculated region in Europe. The cohort of children under the age of 16 remained entirely unvaccinated (EMA only approved the vaccine for 12-15 year-olds on the 28th of May). This local mass vaccination campaign created a situation in which the vaccination coverage of the adult population sharply differed at the district border of Schwaz, while the coverage of those below the age of 16 remained the exact same. We compared SARS-CoV-2 cases among the adult population as well as children in Schwaz with case numbers of the same age cohorts from control regions. First, we compared Schwaz with a control group of other Austrian districts highly similar to Schwaz in many socio-demographic characteristics as well as in infection spread prior to the mass vaccination campaign. Second, we compared local populations residing along the border of Schwaz which live in the very same geographic area but with different vaccination coverage because they were not included in the vaccination campaign.
Interpretation: Prior the mass vaccination campaign, we observed very similar infection spread across all age cohorts in Schwaz and the control regions. Around 3-4 weeks after the campaign, infections started to diverge between Schwaz and the control regions. While the difference was largest among the population aged 16–50 years (which was offered vaccination in the campaign), we also observed a statistically significant reduction in cases among the group of unvaccinated children. Our findings are robust to changes in the control group, as well as controls of a rich set of time and region specific effects.
Policy implications: Our results constitute one of the first evidence of an indirect cross-protection effect from a group of vaccinated individuals to an unvaccinated group (in our case children). Given that in many countries the proposition to keep schools open during the academic year 2021/22 is a top priority, this evidence of community-protection is highly policy relevant.
Wolfinger, D., Gansterer, M., Doerner, K.F., Popper, N. (2020):
A Large Neighbourhood Search Metaheuristic for the Contagious Disease Testing Problem.
Working Paper
Abstract
In late 2019 a new coronavirus disease (COVID-19) emerged, causing a global pandemic within only a few weeks. A crucial factor in the public health response to COVID-19 is achieving a short turnaround time between a potential case becoming known, specimen collection and availability of a test result. In this article we address a logistics problem that arises in the context of testing potential cases. We assume that specimens can be collected in two ways: either by means of a mobile test-team or by means of a stationary test-team in a so called (drive-in) test center. After the specimens have been collected they must be delivered to a laboratory in order to be analyzed. The problem we address in this article is to decide how many test centers to open and where, how many mobile test-teams to use, which suspected cases to assign to a test center and which to visit with a mobile test-team, which specimen to assign to which laboratory, and planning the routes of the mobile test-teams. We introduce this new problem, which we call the contagious disease testing problem (CDTP), and present a mix-integer linear-programming formulation for it. We propose a large neighbourhood search (LNS) metaheuristic for solving the CDTP and present an extensive computational study to illustrate its performance. Furthermore, we present a case study of two Austrian provinces, giving managerial insights regarding COVID-19 test logistics.
Publikationen mit Österreichbezug
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