Small animal disease surveillance 2020/21: SARS-CoV-2, syndromic surveillance and an outbreak of acute vomiting in UK dogs

Stakeholder opinion-led study to identify canine priority diseases for surveillance and control in the UK

BACKGROUND

Many pathogens cause disease in dogs; however, meaningful surveillance in small companion animals is often only possible for the most impactful diseases. We describe the first stakeholder opinion-led approach to identify which canine infectious diseases should be prioritised for inclusion in surveillance and control strategies in the UK.

METHODS

Participants were identified through a stakeholder analysis. A multicriteria decision analysis was undertaken to establish and weight epidemiological criteria for evaluating diseases, and a Delphi technique was employed to achieve a consensus among participants on the top-priority canine diseases.

RESULTS

Nineteen stakeholders from multiple backgrounds participated in this study. Leptospirosis and parvovirus were identified as the top two endemic diseases of concern, while leishmaniosis and babesiosis were the top two exotic diseases. Respiratory and gastrointestinal diseases were identified as the top two syndromes of concern.

LIMITATIONS

Due to the COVID-19 pandemic, the number of participants was reduced. Despite this, a representative multidisciplinary sample of relevant stakeholders contributed to the present study.

CONCLUSIONS

Findings from this study are being used to inform the development of a future UK-wide epidemic response strategy. This methodology could provide a blueprint for other countries.

Veterinary Big Data: When Data Goes to the Dogs

Simple Summary

Big data has created many opportunities to improve both preventive medicine and medical treatments. In the field of veterinary medical big data, information collected from companion animals, primarily dogs, can be used to inform healthcare decisions in both dogs and other species. Currently, veterinary medical datasets are an underused resource for translational research, but recent advances in data collection in this population have helped to make these data more accessible for use in translational studies. The largest open access dataset in the United States is part of the Dog Aging Project and includes detailed information about individual dog participant’s physical and chemical environments, diet, exercise, behavior, and comprehensive health history. These data are collected longitudinally and at regular intervals over the course of the dog’s lifespan. Large-scale datasets such as this can be used to inform our understanding of health, disease, and how to increase healthy lifespan.

Abstract

Dogs provide an ideal model for study as they have the most phenotypic diversity and known naturally occurring diseases of all non-human land mammals. Thus, data related to dog health present many opportunities to discover insights into health and disease outcomes. Here, we describe several sources of veterinary medical big data that can be used in research. These sources include medical records from primary medical care centers or referral hospitals, medical claims data from animal insurance companies, and datasets constructed specifically for research purposes. No data source provides information that is without limitations, but large-scale, prospective, longitudinally collected data from dog populations are ideal for further research as they offer many advantages over other data sources.