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Ass.-Prof. Amelie Desvars, PhD.
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DVM, PhD in Veterinary Epidemiology
Assistant Professor in Infection Epidemiology
Die Leptospirose ist eine Zoonose, die durch Spirochäten der Gattung Leptospira verursacht wird. Es hat eine weltweite Verbreitung, ist aber besonders in tropischen Klimazonen verbreitet. Die Krankheit gilt in Europa als “(re)emerging” als Folge komplexer ökologischer und gesellschaftlicher Veränderungen, einschließlich Klimawandel, Urbanisierung und Globalisierung. Die Krankheit betrifft Menschen und Tiere und führt zu leichten bis sehr schwerwiegenden klinischen Symptomen.
Genaue, verläßliche Daten zur Leptospirose in Niederösterreich sind jedoch spärlich, und bis heute stützt sich die serologische Diagnose auf „fremde“ Referenzstämme, während die Weltgesundheitsorganisation WHO die Verwendung lokal isolierter Stämme zur Erhöhung der Empfindlichkeit des Tests nachdrücklich empfiehlt.
Das Projekt LeptOspirose bei Rindern in Niederösterreich (LORN) zielt darauf ab, lokale pathogene Leptospira-Stämme in Niederösterreich (Niederösterreich) aus infizierten Rindern in Betrieben und auf Schlachthöfen zu isolieren, um die Sensitivität der serologischen Diagnostik für Mensch und Tier zu verbessern. Darüber hinaus wird das Genom der isolierten niederösterreichischen („lokalen“) Stämme charakterisiert.
Durch die Verbesserung der serologischen Diagnostik für Mensch und Tier soll LORN nachhaltig positive Auswirkungen auf die Medizin und die öffentliche Gesundheit in Österreich haben. Darüber hinaus wird das LORN-Projekt das Verständnis der molekularen Epidemiologie der Leptospirose in Niederösterreich verbessern und einen besseren Einblick in die Rolle des Rindes als Reservoir für pathogene Leptospiren geben.
Projektpartner
VetFarm der Veterinärmedizinischen Universität
Amélie Desvars-Larrive (PI), Cynthia Sohm
Wissenschaftliche Partner
Universitätsklinik für Wiederkäuer, Department für Nutztiere und öffentliches Gesundheitswesen in der Veterinärmedizin, Vetmeduni
Thomas Wittek
Abteilung für Öffentliches Veterinärwesen und Epidemiologie, Department für Nutztiere und öffentliches Gesundheitswesen in der Veterinärmedizin, Vetmeduni
Clair Firth
Die Österreichische Agentur für Gesundheit- und Ernährungssicherheit (AGES), Fachbereich Integrative Risikobewertung, Daten und Statistik
Corina Schleicher, Reinhard Fuchs, Marcel Schwarz
Abteilung Biologie der Spirochäten, Institut Pasteur, Paris, Frankreich
Pascale Bourhy
In response to the COVID-19 pandemic, governments have implemented a wide range of public health and social measures (PHSMs), such as border closures, school closures, stay-at-home orders, and face mask mandates, as well as economic measures (EMs) to support the impact of PHSMs and, later, to support recovery plans. This was unique in human history. From the very beginning of the COVID-19 crisis, there was a urgent need for PHSMs & EMs data that are crucial to understand the social impact of the COVID-19 pandemic, e.g., on human rights, disadvantaged minority populations, domestic violence; its wider public health impact, e.g., on health equity, women’s health, mental health, suicide rates; its environmental impact, e.g., greenhouse gas emission and pandemic-induced environmental pollution, or its economic impact.
This data was also critical to evaluate the effectiveness of already implemented measures and inform evidence-based policymaking during the pandemic. However, this data presented important challenges. First, it was novel. If, at the very beginning of the pandemic, COVID-19 PHSMs could have been compared to non-pharmaceutical interventions implemented in response to influenza or severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1), very quickly, the nature of the measures, their stringency, their incredibly high number, and their diversity, have made this data original and complex. Secondly, there was limited opportunity to capture such information. Indeed, such data were not always easily and continuously accessible, they were dispersed, not organized (i.e. mostly gathered from text sources), while the available information was unsystematic (depending on the country and or the epidemic stage). Thirdly, no guidelines or reference method was available to manage such a surge of data, organize, validate, code, and curate it. In March 2020, we were facing a completely novel field of research.
This project is the result of a collaborative work between the Complexity Science Hub (CSH) Vienna and the University of Veterinary Medicine Vienna, Austria.
Started in mid-March 2020, this project aimed to generate a comprehensive open-access dataset on PHSMs & EMs implemented in response to COVID-19. Our objective was to compile COVID-19 PHSMs and EMs in a structured, hierarchically organized, intelligible, and easily accessible format. One of our major focus was to provide (early in the pandemic) fine-grained (sub-national) data on COVID-19 PHSMs & EMs that could be readily usable for modelling and machine-learning analysis.
Desvars-Larrive, A. et al. A structured open dataset of government interventions in response to COVID-19. Scientific Data 7, 285 (2020).
Read the paper
Haug, N. et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour. 10.1038/s41562-020-01009-0 (2020).
Read the paper
GitHub repository of the project. Read more about it
Joining a global effort to fight the pandemic, our dataset has been into the Global Dataset of Public Health and Social Measures (PHSM) aggregated by the World Health Organization (WHO).
This project is part of the COVID-19 PHSM Network.