A real-time spatio-temporal syndromic surveillance system with application to small companion animals

Alison C. Hale*, Fernando Sánchez-Vizcaíno, Barry Rowlingson, Alan D. Radford, Emanuele Giorgi, Sarah J. O’Brien, Peter J. Diggle

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
191 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number17738 (2019)
Number of pages14
JournalScientific Reports
Volume9
DOIs
Publication statusPublished - 28 Nov 2019

Keywords

  • companion animals
  • syndromic surveillance
  • early detection
  • Bayesian inference
  • gastrointestinal disease
  • SAVSNET

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