Multi-species temporal network of livestock movements for disease spread

Abstract

Introduction The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread.

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

“…This is because the connectivity of single host farm networks allows for the spread of disease quickly, infecting more farms of the specific host species in the 30-day time frame than when the disease is started in a multi-host farm. Similar patterns have been observed in past epidemics, such as the FMD [ 14 , 15 , 26 , 42 , 43 ] where multi-host farms had a role in infection propagation.…”

“…Previous studies that also utilized large datasets and similar node-removal approaches but in single host networks, reported similar results as ours, especially for bovine networks [ 33 , 35 , 44 , 45 ] and swine networks [ 21 , 37 , 41 ]. Studies that considered more than one host species and network-based target control actions reported a larger reduction in the expected number of cases directly generated by one case in a population, size of the connected components, and the number of infected farms using degree-based interventions [ 10 , 14 ]. An important limitation of the studies listed above is the use of static networks which could have overestimated the connectivity of those networks [ 14 , 20 , 45 ].…”

“…Studies that considered more than one host species and network-based target control actions reported a larger reduction in the expected number of cases directly generated by one case in a population, size of the connected components, and the number of infected farms using degree-based interventions [ 10 , 14 ]. An important limitation of the studies listed above is the use of static networks which could have overestimated the connectivity of those networks [ 14 , 20 , 45 ]. Therefore, we argue for the need to consider temporal models, such as our contact chain analysis, when developing network-based target approaches, because it considers the temporal order of the connections and avoids over-connectivity of the static networks [ 20 , 45 ].…”

“…Here, we implemented analysis that allows for the description of the possible risks of animal movements, and identify key farms in the contact network using a multispecies approach. Social network analysis and mathematical models considering more than one host can deliver insights into the quantification of potential outbreak paths and provide useful data for the identification of key farms that can be used to break the direct contact transmission of animal diseases [ 8 , 14 , 26 – 29 ]. These insights are invaluable in supporting the design and implementation of efficient control and eradication strategies.…”

Assessing epidemiological parameters and dissemination characteristics of the 2000 and 2001 foot-and-mouth disease outbreaks in Rio Grande do Sul, Brazil

“…Other useful models to estimate transmission dynamics and epidemiological parameters are based on the retrospective reconstruction of previous outbreak networks (Firestone et al, 2020; Hayama et al, 2019; Jombart et al, 2014). Such network modeling exercises emulate the characteristics of spread and allow the identification of potential super spreading events as well as epidemics sizes by describing interactions between individuals using animal movement data (Cabezas et al, 2021; Cardenas et al, 2021, 2022; Dubé et al, 2009; Fèvre et al, 2006; Machado et al, 2021; Ruget et al, 2021; VanderWaal et al, 2016).…”