Using network science in veterinary epidemiology [Poster]

Poster presented at the International School and Conference on Network Science (NetSci) on 12 July 2023 in Vienna, Austria.  The content of the poster is adapted from the paper "Network analysis of pig movement data as an epidemiological tool: an Austrian case study" https://doi.org/10.1038/s41598-023-36596-1 Abstract:  Infectious disease outbreaks in livestock populations compromise animal health and welfare and may lead to high economic losses. Historical epidemics have been associated with movements of animals; therefore, several European countries have developed livestock registration and movement databases to trace animal movements.  In this study, we analyze seven years (2015-2021) of daily records of pig movement in Austria and select epidemiologically relevant network metrics that have practical applications for surveillance and control of infectious disease outbreaks.  We first explore the topology of the network and its structural changes over time, including seasonal and long-term trends in the pig production activities. We then investigate the temporal dynamics of the network community structure using InfoMap algorithm. We show that the Austrian pig trade network exhibits a scale-free topology but is very sparse, suggesting a moderate impact of infectious disease outbreaks. However, our findings highlight that two regions (Upper Austria and Styria), presenting higher farm density and a significant number of hubs. These regions also host 77% of pig holdings in the largest strongly connected component, making them more vulnerable to infectious disease outbreaks. Dynamic community detection revealed a stable behavior of the clusters over the study period. This study provides important insight on the interplay between network theory and veterinary epidemiology and valuable information to designing cost-effective infectious disease surveillance and control plans. Notably, we argue that enhancing biosecurity and surveillance in the highly connected holdings (hubs) would greatly reduce the structural risk and favor timely detection of pathogens. Similarly, trade communities may offer an optimized approach to managing infectious diseases through data-driven zoning.</p