title: Vetsyn: An R package for veterinary syndromic surveillance.
abstract: A recent inventory showed that several countries are exploring varied types of animal health sources for the development of veterinary syndromic surveillance (VSS), however, few systems have documented success after the phase of development or exploratory analysis of data. We suggest there are three main challenges in the current development of animal syndromic surveillance: (i) the lack of standards in disease recording and classification; (ii) the development of statistical methods appropriate to deal with animal data; and (iii) the creation of ready-to-use tools that employ these statistical methods. We address the latter two challenges and present an R package - vetsyn - which covers the steps of VSS implementation from classified data to interface. Detailed tutorials are included with the package. The goal is to provide ready-to-use codes to automatize the process of converting pre-classified animal health data into epidemiological information. The package functions are illustrated using real data and simulated outbreaks. Functions to monitor data daily and weekly are available. The main innovation offered by the package is ability to manage data streams, analyses, alarms and user interface in a continuous flow. We expect that this will facilitate the implementation of syndromic surveillance systems by veterinary epidemiologists and surveillance practitioners.
authors.affiliation: Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden.
authors.forename: F C
authors.initials: FC
authors.lastname: Dórea
authors.affiliation: Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden.
authors.forename: S
authors.initials: S
authors.lastname: Widgren
authors.affiliation: Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden. Electronic address: fernanda.dorea@sva.se.
authors.forename: A
authors.initials: A
authors.lastname: Lindberg
mesh_terms: D000820:Animal Diseases / Q000453:epidemiology* / Q000209:etiology; D000818:Animals; D004196:Disease Outbreaks / Q000662:veterinary; D062665:Epidemiological Monitoring / Q000662:veterinary*; D011159:Population Surveillance / Q000379:methods*; D012984:Software
keywords: Animal health; R programming; Syndromic surveillance