Linked avian influenza epidemiological and genomic data in EMPRES-i for epidemic intelligence (2012–2021)
Arınık, Nejat | Interdonato, Roberto | Roche, Mathieu | Teisseire, Maguelonne | Centre de Recherche en Informatique de Lens (CRIL) ; Université d'Artois (UA)-Centre National de la Recherche Scientifique (CNRS) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | European Project: 874850,H2020-SC1-2019-Single-Stage-RTD,MOOD(2020)
International audience
Показать больше [+] Меньше [-]Английский. Due to its highly contagious nature, Avian Influenza (AI) is considered an animal health emergency affecting commercial sector and wild bird populations. Several genome sequencing databases have been created to help researchers understand how AI viruses evolve, spread, and cause disease. However, for a global epidemic monitoring approach, they need to be combined to public health surveillance systems, the well-one being EMPRES-i from the World Organisation for Animal Health (WOAH) and the Food and Agriculture Organization of the United Nations (FAO).This paper presents a new AI dataset, in which EMPRES-i is enriched thanks to the genome sequence data of Avian Influenza cases affecting bird species from 2012 to 2021, publicly provided by the Bacterial and Viral Bioinformatics Resource Center (BV-BRC). This dataset is obtained by automatically linking sequence information in BV-BRC to the AI events in EMPRES-i, which results in “putatively” linked events between these two sources. The collected data is structured by nature, but it is preprocessed and normalized for the purpose of high-quality data linkage. Moreover, several data linkage strategies and missing information handling are introduced. To show the usefulness of our dataset, we quantitatively evaluate the proposed strategies in randomly sampled events and present in the end a diffusion network inference task.
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