Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Abstract

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Analyzing the intrastate and interstate swine movement network in the United States

“…The dynamic network analysis revealed that the GSCC ranged from 0.2% to 2.59%, while the GWCC varied from 91.04% to 99.62%. These results highlight the extreme levels of verticalization of North American commercial swine production, as sows typically send weaned pigs to the same nurseries or wean-to-finisher premises and then to a wider range of finishers (Kinsley et al, 2019; Machado et al, 2019; Passafaro et al, 2020), similar patterns have also been described in Austria (Puspitarani et al, 2023) and Brazil (Cardenas et al, 2022, 2021). Time-independent, connected component sizes provide an overall estimation of the lower and upper bounds of epidemic sizes (Kao et al, 2006; Omondi et al, 2021); for the U.S., the upper bound estimates above 90% of premises are at risk of infection.…”

“…An in-depth understanding of national movement patterns is a key element for epidemic control in highly integrated swine production systems. In the same vein, examining interstate and intrastate pig shipment data is crucial to informing national disease response plans, and developing novel tactics against endemic and emerging diseases (Cardenas et al, 2022; Hammami et al, 2022b; Puspitarani et al, 2023; Sykes et al, 2023). The median distance of pig shipment was 74 km, similar to what has been shown in other regional studies with shipment distances between 10 and 50 km (Kinsley et al, 2019).…”

“…Commingling has been previously linked to a heightened risk of disease transmission (Aragon et al, 2019). Movement flows that rely on many premises as sources to fill nursery or finisher premises result in contact networks with low animal movement loyalty, leading to a reduction in the number of regular connections between the same premises, thus increasing the chances for disease dissemination (Puspitarani et al, 2023; Hammami et al, 2022b; Schulz et al, 2017. )…”

“…The shipment of animals between premises has been widely recognized as the primary route involved in the transmission of infectious pathogens of swine such as African swine fever (ASF), porcine respiratory and reproductive syndrome virus (PRRSV), and foot-and-mouth disease (FMD) (Galvis et al, 2022a;Passafaro et al, 2020;Vinueza et al, 2022;Hammami et al, 2022a;Cardenas et al, , 2021Galvis et al, 2021;VanderWaal et al, 2020;Lee et al, 2017;White et al, 2017;Neumann et al, 2005;Pappaioanou and Gramer, 2010;Fèvre et al, 2006;. Disease intervention strategies involve reducing the number of between-premises animal movements; therefore, elucidating local and nationwide movement network patterns and identifying shipment hubs and road pathways used for pig shipment are expected to provide essential information about disease spread potential and efficient outbreak control (Puspitarani et al, 2023;Passafaro et al, 2020;Hammami et al, 2022a;Gorsich et al, 2019;Strano et al, 2018;Sykes et al, 2023. ) The U.S. swine industry is vertically integrated and comprised of approximately 64,871 premises, housing 72 million pigs in premises specializing in one or more production phases (e.g., farrow-to-wean, growing pig) (Passafaro et al, 2020;NASS et al, 2022;USDA and NASS, 2019;Reimer, 2006).…”

Assessing the impact of structural modifications in the construction of surveillance network for transboundary animal diseases: the role of backbone and sentinel nodes

“…Further, when stochastic network transmission models are used, information can be retrieved for early warning systems and to determine the spatial extent of the epidemic (5). Epidemic patterns depend on the interplay between the structure of the networks and the characteristics of the epidemic (6,7). The models’ assumptions, structure, complexity, and reliability depend on the data’s quality and degree of detail used to reconstruct the network and the information concerning the disease under study (4).…”