Dynamic network measures reveal the impact of cattle markets and alpine summering on the risk of epidemic outbreaks in the Swiss cattle population

Abstract

BackgroundLivestock herds are interconnected with each other via an intricate network of transports of animals which represents a potential substrate for the spread of epidemic diseases. We analysed four years (2012–2015) of daily bovine transports to assess the risk of disease transmission and identify times and locations where monitoring would be most effective. Specifically, we investigated how the seasonal dynamics of transport networks, driven by the alpine summering and traditional cattle markets, affect the risk of epidemic outbreaks.ResultsWe found strong and consistent seasonal variation in several structural network measures as well as in measures for outbreak risk. Analysis of the consequences of excluding markets, dealers and alpine pastures from the network shows that markets contribute much more to the overall outbreak risk than alpine summering. Static descriptors of monthly transport networks were poor predictors of outbreak risk emanating from individual holdings; a dynamic measure, which takes the temporal structure of the network into account, gave better risk estimates. A stochastic simulation suggests that targeted surveillance based on this dynamic network allows a higher detection rate and smaller outbreak size at detection than compared to other sampling schemes.ConclusionsDynamic measures based on time-stamped data—the outgoing contact chain—can give better risk estimates and could help to improve surveillance schemes. Using this measure we find evidence that even in a country with intense summering practice, markets continue being the prime risk factor for the spread of contagious diseases.

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

“…In the monthly networks, two peaks in the frequency of animal movements were apparent during late spring to early summer and in autumn, which is due to the grazing period. Similar seasonal patterns were described also in other countries with grazing practices (40,51,52). In our cattle movement networks, this seasonal pattern was reflected in all monthly global network measures except diameter and was consistent through time.…”

“…We chose these aggregation windows because it was previously argued that networks aggregated over longer time periods may adequately address chronic or slowly spreading diseases (56). Even though the Slovenian cattle movement network is in general smaller and less active compared to other European cattle trade networks, it shares a number of properties with them, such as similar degree distributions, average shortest path length, and diameter (40,41,51,52,57).…”

“…We explored the characteristics of cattle movement networks through global measures, namely by the number of active premises, sizes of giant weakly and strongly connected components (GWCC and GSCC, respectively), bow-tie partition, density, average shortest path length, diameter, average degree, clustering coefficient, and degree assortativity (46,47). In local network measures or node centrality measures, we focused on measures that were recognized as important for disease transmission in previously published studies (36,37,39,40,(48)(49)(50)(51)(52). We calculated the unweighted and weighted total, in-and out-degree, betweenness centrality and total, in-and out-degree closeness centrality (46).…”

“…Correlations of node level measures across years and between different measures were assessed with Spearman's rank correlation coefficients (36,37,48,52). We assessed the heterogeneity of premises contributions to the overall network connectivity utilizing degree distributions (46).…”

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

“…Consequently, during this period, we observed an increase in the number of nodes and 386 connections; therefore, a given node would require more steps to reach another farm, within the 387 network which could alone reduce the speed of disease transmission (Vidondo and Voelkl, 388 2018 However, these followed the assumption that movement aggregation would not overestimate or 397 introduce edge biases (Vidondo and Voelkl, 2018). It has been suggested that to some extent, 398 static networks provide useful information about disease related risks (Mohr et al, 2018).…”