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

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

doi:10.1038/s41598-019-53352-6

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.

Highlights

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks
The emergence of new diseases[1] and the increasing threat of bio-terrorism have motivated the development of syndromic surveillance systems in public health focused on the early detection of health threats that require effective public health action[2,3]
We apply our model to small companion animal electronic health records (EHRs) collected over two years by Small Animal Veterinary Surveillance Network (SAVSNET) from a large network of UK veterinary premises

Summary

Introduction

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 use a Markov Chain Monte Carlo (MCMC) algorithm to generate samples from the Bayesian predictive distribution of the underlying spatio-temporal surface These samples are used to compute predictive probabilities at given thresholds; a high predictive probability at a particular location and time gives an early warning of a possible disease outbreak. Current approaches to preventing and controlling GI disease in companion animals have focussed on individuals or small groups of animals This seems to have had little impact on GI disease, which remains one of the commonest reasons for presenting for veterinary care in the UK9,10,12–15, precise data to confirm this has been lacking. A more coordinated population-scale approach to GI disease surveillance in companion animals is needed

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