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Joint use of Disparate Data for the Surveillance of Zoonoses: A Feasibility Study for a One Health Approach in Germany
Authors:A Wendt  L Kreienbrock  A Campe
Institution:Department of Biometry, Epidemiology and Information Processing, University of Veterinary Medicine, Hannover, Germany
Abstract:Zoonotic diseases concern human and animal populations and are transmitted between both humans and animals. Nevertheless, surveillance data on zoonoses are collected separately for the most part in different databases for either humans or animals. Bearing in mind the concept of One Health, it is assumed that a global view of these data might help to prevent and control zoonotic diseases. In following this approach, we wanted to determine which zoonotic data are routinely collected in Germany and whether these data could be integrated in a useful way to improve surveillance. Therefore, we conducted an inventory of the existing data collections and gathered information on possible One Health surveillance areas in Germany by approaching experts through a scoping survey, personal interviews and during a workshop. In matching the information between the status quo for existing data collections and the possible use cases for One Health surveillance, this study revealed that data integration is currently hindered by missing data, missing pathogen information or a lack of timeliness, depending on the surveillance purpose. Therefore, integrating the existing data would require substantial efforts and changes to adapt the collection procedures for routine databases. Nevertheless, during this study, we observed a need for different stakeholders from the human and animal health sectors to share information to improve the surveillance of zoonoses. Therefore, our findings suggest that before the data sets from different databases are integrated for joint analyses, the surveillance could be improved by the sharing of information and knowledge through a collaboration of stakeholders from different sectors and institutions.
Keywords:Interdisciplinary collaboration  data integration  zoonotic disease surveillance  joint use  early detection  routine data
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