The transmission of infectious diseases is intricately linked to spatiotemporal proximity. Spatial data used in veterinary epidemiology to depict the distribution of disease events typically encompasses pairwise distances between two locations. The Euclidean method is commonly employed to compute such distances. We have developed a tool capable of calculating pairwise distances, considering factors such as orography, the road network, or the land use, given a set of points representing farms or outbreak sites where samples have been collected. The outcome is a three dimensional object comprising six distinct pairwise matrix distances: Euclidean, Haversine, route, route with elevation, orographic/elevation and cost/friction. These distances offers opportunities for exploring novel approaches to integrating the spatial component into epidemiological applications.
This paper presents the work of the CLOSE group, a team of students from the University of Padua, Italy, for the Conference and Labs of the Evaluation Forum (CLEF) LongEval LAB 2023 Task 1 [1]. Our work involved developing an Information Retrieval (IR) system that can handle changes in data over time while maintaining high performance. We first introduce the problem as stated by CLEF and then describe our system, explaining the different methodologies we implemented. We provide the results of our experiments and analyze them based on the choices we made regarding various techniques. Finally, we propose potential avenues for future improvement of our system.