ArticlePLoS computational biology2022
Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors.Transboundary and emerging diseases · 2026Article
- Allocating limited surveillance effort for outbreak detection of endemic foot and mouth disease.PLoS computational biology · 2025Article
- Current trends and challenges in the management of foot and mouth disease in Saudi Arabia: A review.Open veterinary journal · 2025Review
- Network analysis of farmed Atlantic salmon movements in British Columbia, Canada.Frontiers in veterinary science · 2025Article
- Assessment of Foot-and-Mouth Disease Trends in Türkiye Between 2005 and 2025.Transboundary and emerging diseases · 2025Article
- Network analysis of pig movement data as an epidemiological tool: an Austrian case study.Scientific reports · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed-weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.
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