ArticlePloS one2016
Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Risk of exotic disease introduction and propagation in the Austrian swine trade network.iScience · 2026Article
- The tip of the iceberg: high-risk contacts for hemorrhagic fevers of swine in the Caribbean.Veterinary research · 2026Article
- Assessing the impact of mitigation measures on cattle movement patterns in Austria: bluetongue virus outbreaks as a case study.Frontiers in veterinary science · 2026Article
- Article
- Data quality assessment of pig movements in the Norwegian Livestock Register reveals challenges for outbreak preparedness.BMC veterinary research · 2025Article
- Evolution of the Swiss pork production systems and logistics: the impact on infectious disease resilience.Scientific reports · 2025Article
- Article
- Mapping Small Ruminant Trade Networks in Ethiopia's Somali Region and Borena Zone: Implications for the Spread of Peste des Petits Ruminants Virus.Transboundary and emerging diseases · 2025Article
- Network analysis of farmed Atlantic salmon movements in British Columbia, Canada.Frontiers in veterinary science · 2025Article
- An overlooked poultry trade network of the smallholder farms in the border provinces of Thailand, 2021: implications for avian influenza surveillance.Frontiers in veterinary science · 2024Article
- Simulation design to find the welfare impacts of livestock trading and disease transmission.PloS one · 2024Article
- Identifying target areas for risk-based surveillance and control of transboundary animal diseases: a seasonal analysis of slaughter and live-trade cattle movements in Uganda.Scientific reports · 2023Article
- Active querying approach to epidemic source detection on contact networks.Scientific reports · 2023Article
- Network analysis of pig movement data as an epidemiological tool: an Austrian case study.Scientific reports · 2023Article
- Analysis of patterns of livestock movements in the Cattle Corridor of Uganda for risk-based surveillance of infectious diseases.Frontiers in veterinary science · 2023Article
- Description of the Cattle and Small Ruminants Trade Network in Senegal and Implication for the Surveillance of Animal Diseases.Transboundary and emerging diseases · 2023Article
- Disentangling transport movement patterns of trucks either transporting pigs or while empty within a swine production system before and during the COVID-19 epidemic.Frontiers in veterinary science · 2023Article
- Modelling and assessing additional transmission routes for porcine reproductive and respiratory syndrome virus: Vehicle movements and feed ingredients.Transboundary and emerging diseases · 2022Article
- Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.Transboundary and emerging diseases · 2022Article
- Direct and indirect pathways for the spread of African swine fever and other porcine infectious diseases: An application of the mental models approach.Transboundary and emerging diseases · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnimal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. METHODOLOGY: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account.
findingsWe find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume do barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.
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