ARPHA Conference Abstracts :
Conference Abstract
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Corresponding author: Jakub Fusiak (jakub.fusiak@bfr.bund.de)
Received: 20 May 2021 | Published: 28 May 2021
© 2021 Jakub Fusiak, Kyrre Kausrud, Marion Gottschald, Dominic Tölle, Marco Rügen, Birgit Lewicki, Isaak Gerber, Michele Kayser, Alexander Falenski, Armin Weiser, Solveig Jore, Madelaine Norström, Katja Alt
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Fusiak J, Kausrud K, Gottschald M, Tölle D, Rügen M, Lewicki B, Gerber I, Kayser M, Falenski A, Weiser A, Jore S, Norström M, Alt K (2021) Food products identified as source of a foodborne disease outbreak by a fast and robust likelihood estimation. ARPHA Conference Abstracts 4: e68945. https://doi.org/10.3897/aca.4.e68945
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Identifying a specific product causing a foodborne disease outbreak can be difficult, especially when dealing with a large amounts of suspicious food items and weak epidemiological evidence. A previously described likelihood model (
The model improved by Kausrud et al. in R (
We integrated the module into the FCL web application for tracing (FCL Web; https://fcl-portal.bfr.berlin) to provide an intuitive and user-friendly solution. This solution combines a simple data input with extended data wrangling to make the calculation of the NOVA model as easy as possible. Since the model can be executed directly inside the web browser and therefore does not rely on any server environment, the possibility of data leakage can be highly reduced. The implementation of the advanced likelihood model into FCL Web increase the availability of this model and provides investigators easy, fast and reliable usage to improve outbreak investigation workflows.
foodborne outbreak investigation, FoodChain-Lab, web application, model, WebAssembly
Jakub Fusiak
One Health EJP NOVA
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.