Abstract
The equine sector in Italy is of great importance and contributes significantly to the country's economy. However, it faces numerous challenges in preventing and controlling infectious diseases, which can have devastating effects on horse health, welfare, and the equine industry as a whole. The Italian Ministry of Health-funded research project (2021-2022) aimed to assess the vulnerability of the equine sector to disease incursions such as the African Horse Sickness Virus (AHSV) or the spread of Equine Infectious Anaemia Virus (EIAV), and others ( Dominguez et al., 2016; Fairbanks et al., 2022).
Through network analysis, spatial-temporal analysis, and machine learning techniques, we identified high-risk areas, periods, and strategic nodes within the Italian equine network to be targeted for preventing and controlling the risk of incursions from abroad and the internal spread of diseases.
Machine learning approaches combined national entomological surveillance data with satellite-derived temperature time series to determine if establishments introducing animals from abroad were suitable for AHSV vectors. The Random Forest algorithm achieved an 84% accuracy in this task.
Spatio-temporal clustering identified significant clusters of importing establishments, primarily in northern Italy, central Italy, and the Lazio Region, with equestrian sport, recreational equestrian, and riding schools being the most at-risk sectors (comprising 76% of high-risk activities). Spring and summer were identified as the most at-risk seasons (Martínez-López et al., 2011). To assess the size and speed of a possible epidemic's spread on the internal movement network, we calculated the Giant Strongly Connected Component (GSCC). This encompassed 33% of the network's nodes, evenly distributed throughout the country. Within the GSCC, approximately 2.5% of the nodes were identified as super hubs, i.e. nodes exhibiting high values of both degree and betweenness. These establishments play a crucial role, as they can infect many nodes directly connected or act as bridges between groups of nodes, thus facilitating rapid and vast hypothetical epidemics. Moreover, 1% of all establishments proved to be spatial super-spreaders and or super-susceptibles, that is to say, they can spread the infection over long distances.
The analysis conducted to prevent the spread and introduction of equine diseases in Italy has been complemented by EquiTracing, a disease control web tool. This integration significantly enhances efforts to contain the spread of equine diseases. By utilizing cutting-edge technology, it enables real-time access to data from the National Animal Disease Reporting System (SIMAN) and the Equine Movement Database (BDN-E).
EquiTracing empowers authorities to trace equine movements, identifying infection sources and contact chains. It offers interactive tables, maps, graphs, querying and analyzing tools, tracing animals or batches, and generating dynamic reports.
In conclusion, these results start an ongoing research journey. The data-driven approach and knowledge aid informed decisions and efficient prevention plans for equine diseases.
References
Dominguez, M., Münstermann, S., & Timoney, P. (2016). Equine disease events resulting from international horse movements: Systematic review and lessons learned. Equine Veterinary Journal, 48(5), 641-653. https://doi.org/10.1111/evj.12523
Fairbanks, E. L., Baylis, M., Daly, J. M., & Tildesley, M. J. (2022). Inference for a spatio-temporal model with partial spatial data: African horse sickness virus in Morocco. Epidemics, 39, 100566. https://doi-org.bibliosan.idm.oclc.org/10.1016/j.epidem.2022.100566
Martínez-López, B., Perez, A. M., & Sánchez-Vizcaíno, J. M. (2011). Identifying equine premises at high risk of introduction of vector-borne diseases using geo-statistical and space-time analyses. Preventive veterinary medicine, 100(2), 100–108. https://doi-org.bibliosan.idm.oclc.org/10.1016/j.prevetmed.2011.02.002