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Lookup NU author(s): Professor Sarah O'Brien
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.
Author(s): Hale AC, Sánchez-Vizcaíno F, Rowlingson B, Radford AD, Giorgi E, O'Brien SJ, Diggle PJ
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Online publication date: 28/11/2019
Acceptance date: 29/10/2019
Date deposited: 21/06/2021
ISSN (electronic): 2045-2322
Publisher: Nature Publishing Group
PubMed id: 31780686
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