Emerging vector-borne diseases are a growing concern, especially for horse populations, which are at particular risk for disease spread. In general, horses travel widely and frequently and, despite the health and economic impacts of equine diseases, effective health regulations and biosecurity systems to ensure safe equine movements are not always in place. The present work proposes to improve the surveillance of vector-borne diseases in horses through the use of different approaches that assess the probability of occurrence of a newly introduced epidemic.
First, we developed a spatiotemporal quantitative model which combined various probabilities in order to estimate the risk of introduction of African horse sickness and equine encephalosis in The Netherlands and in France. Such combinations of risk provided more a detailed picture of the true risk posed by these pathogens. Second, we assessed syndromic surveillance systems using two approaches: a classical approach with the alarm threshold based on the standard error of prediction, and a Bayesian approach based on a likelihood ratio. We focused particularly on the early detection of West Nile virus using reports of nervous symptoms in horses. Both approaches provided interesting results but Bayes’ rule was especially useful as it provided a quantitative output and was able to combine different epidemiological information.
Finally, a Bayesian approach was also used to quantitatively combine various sources of risk estimation in a multivariate syndromic surveillance system (applied to West Nile virus in South of France).
Combining evidence provided promising results. This work, based on risk estimations, strengthens the surveillance of VBDs in horses and can support public health decision making. It also, however, highlights the need to improve data collection and data sharing, to implement full performance assessments of complex surveillance systems, and to use effective communication and training to promote the adoption of these approaches.
Key words: syndromic surveillance, West Nile, quantitative risk analysis, African horse sickness, equine encephalosis, vector-borne diseases, risk-based surveillance