Development of algorithms for syndromic surveillance
Development of algorithms for real-time syndromic surveillance to enhance early detection of emerging and re-emerging epizooties and zoonoses
contact person: Rahel Struchen
The overall goal of this project was to contribute to the development of a system for early detection of emerging and re-emerging diseases in Switzerland by using syndromic data already available for livestock, and by developing pattern recognition algorithms to produce alerts when such pre-selected events occur more often than expected by chance. In a first instance, several animal health datasets (both public and private), in which we identified syndromes that illustrate various types of diseases and different stages of the food production chain, were screened and assessed with respect to current availability and eligibility to produce early warning signals. Methods to exploit data on mortalities, abortions, meat inspection results, data from rendering plants etc. were developed and aberration detecting algorithms were tested. Algorithms were evaluated in terms of sensitivity, specificity and timeliness and validated through outbreak simulations. Finally, in close collaboration with the potential implementing bodies of a Swiss livestock syndromic surveillance system, solutions for technical implementations of algorithms in existing databases for real-time screening as well as a framework for evaluating such a system have been discussed.
This project serves as a stepping stone towards bridging the gaps between the Swiss animal health main stakeholders and encouraging the exchange of ideas on how to improve Switzerland’s capacity for disease surveillance and management through the harmonisation of data collection procedure and the development of database linkages for example.