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Puspitarani GA, Fuchs R, Fuchs K, Ladinig A, Desvars-Larrive A. Network analysis of pig movement data as an epidemiological tool: an Austrian case study. Sci Rep 2023; 13:9623. [PMID: 37316653 PMCID: PMC10267221 DOI: 10.1038/s41598-023-36596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
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.
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Affiliation(s)
- Gavrila A Puspitarani
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria.
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria.
| | - Reinhard Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
- Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, Merangasse 18/1, 8010, Graz, Austria
| | - Klemens Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
| | - Andrea Ladinig
- University Clinic for Swine, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
| | - Amélie Desvars-Larrive
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria
- VetFarm, University of Veterinary Medicine Vienna, Kremesberg 13, 2563, Pottenstein, Austria
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Koutsoumanis K, Allende A, Álvarez‐Ordóñez A, Bolton D, Bover‐Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Argüello‐Rodríguez H, Dohmen W, Magistrali CF, Padalino B, Tenhagen B, Threlfall J, García‐Fierro R, Guerra B, Liébana E, Stella P, Peixe L. Transmission of antimicrobial resistance (AMR) during animal transport. EFSA J 2022; 20:e07586. [PMID: 36304831 PMCID: PMC9593722 DOI: 10.2903/j.efsa.2022.7586] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
The transmission of antimicrobial resistance (AMR) between food-producing animals (poultry, cattle and pigs) during short journeys (< 8 h) and long journeys (> 8 h) directed to other farms or to the slaughterhouse lairage (directly or with intermediate stops at assembly centres or control posts, mainly transported by road) was assessed. Among the identified risk factors contributing to the probability of transmission of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), the ones considered more important are the resistance status (presence of ARB/ARGs) of the animals pre-transport, increased faecal shedding, hygiene of the areas and vehicles, exposure to other animals carrying and/or shedding ARB/ARGs (especially between animals of different AMR loads and/or ARB/ARG types), exposure to contaminated lairage areas and duration of transport. There are nevertheless no data whereby differences between journeys shorter or longer than 8 h can be assessed. Strategies that would reduce the probability of AMR transmission, for all animal categories include minimising the duration of transport, proper cleaning and disinfection, appropriate transport planning, organising the transport in relation to AMR criteria (transport logistics), improving animal health and welfare and/or biosecurity immediately prior to and during transport, ensuring the thermal comfort of the animals and animal segregation. Most of the aforementioned measures have similar validity if applied at lairage, assembly centres and control posts. Data gaps relating to the risk factors and the effectiveness of mitigation measures have been identified, with consequent research needs in both the short and longer term listed. Quantification of the impact of animal transportation compared to the contribution of other stages of the food-production chain, and the interplay of duration with all risk factors on the transmission of ARB/ARGs during transport and journey breaks, were identified as urgent research needs.
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O'Hara KC, Beltrán-Alcrudo D, Hovari M, Tabakovski B, Martínez-López B. Network analysis of live pig movements in North Macedonia: Pathways for disease spread. Front Vet Sci 2022; 9:922412. [PMID: 36016804 PMCID: PMC9396142 DOI: 10.3389/fvets.2022.922412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 07/19/2022] [Indexed: 11/27/2022] Open
Abstract
Globalization of trade, and the interconnectivity of animal production systems, continues to challenge efforts to control disease. A better understanding of trade networks supports development of more effective strategies for mitigation for transboundary diseases like African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). North Macedonia, bordered to the north and east by countries with ongoing ASF outbreaks, recently reported its first incursion of ASF. This study aimed to describe the distribution of pigs and pig farms in North Macedonia, and to characterize the live pig movement network. Network analyses on movement data from 2017 to 2019 were performed for each year separately, and consistently described weakly connected components with a few primary hubs that most nodes shipped to. In 2019, the network demonstrated a marked decrease in betweenness and increase in communities. Most shipments occurred within 50 km, with movements <6 km being the most common (22.5%). Nodes with the highest indegree and outdegree were consistent across years, despite a large turnover among smallholder farms. Movements to slaughterhouses predominated (85.6%), with movements between farms (5.4%) and movements to market (5.8%) playing a lesser role. This description of North Macedonia's live pig movement network should enable implementation of more efficient and cost-effective mitigation efforts strategies in country, and inform targeted educational outreach, and provide data for future disease modeling, in the region.
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Affiliation(s)
- Kathleen C. O'Hara
- Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Daniel Beltrán-Alcrudo
- Food and Agriculture Organization of the United Nations (FAO), Regional Office for Europe and Central Asia, Budapest, Hungary
| | - Mark Hovari
- Food and Agriculture Organization of the United Nations (FAO), Regional Office for Europe and Central Asia, Budapest, Hungary
| | - Blagojcho Tabakovski
- Food and Veterinary Agency, Republic of North Macedonia, Skopje, North Macedonia
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
- *Correspondence: Beatriz Martínez-López
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Acosta AJ, Cespedes N, Pisuna LM, Galvis JO, Vinueza RL, Vasquez KS, Grisi-Filho JH, Amaku M, Gonçalves VS, Ferreira F. Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever. Transbound Emerg Dis 2022; 69:e2898-e2912. [PMID: 35737848 DOI: 10.1111/tbed.14640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/14/2022] [Accepted: 06/17/2022] [Indexed: 11/29/2022]
Abstract
The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National veterinary service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges. Furthermore, we also created a network of parishes as its nodes by aggregating their premises movements as edges. The annual network metrics showed an average diameter of 20.33, a number of neighbours of 2.61, a shortest path length of 4.39 and a clustering coefficient of 0.38 (small-world structure). The most frequent movements were to or from markets (55%). Backyard producers made up 89% of the network premises, and the top 2% of parishes (highest degree) contributed to 50% of the movements. The highest frequencies of movements between parishes were in the centre of the country, while the highest frequency of movements to abattoirs was in the south-west. Finally, the pattern of CSF disease outbreaks within the Ecuador network was likely the result of network transmission processes. In conclusion, our results represented the first exploratory analysis of domestic pig movements at premise and parish levels. The surveillance system could consider these results to improve its procedures and update the disease control and management policy, and allow the implementation of targeted or risk-based surveillance. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Alfredo Javier Acosta
- Preventive Veterinary Medicine Department. School of Veterinary Medicine and Animal Science, University of São Paulo, Sao Paulo, Brazil
| | - Nicolas Cespedes
- Population Health and Pathobiology Department. College of Veterinary Medicine, North Carolina State University, Raleigh, USA
| | - Luis Miguel Pisuna
- General coordination of animal health, Phytozoosanitary Regulation and Control Agency, Quito, Ecuador
| | - Jason Onell Galvis
- Population Health and Pathobiology Department. College of Veterinary Medicine, North Carolina State University, Raleigh, USA
| | - Rommel Lenin Vinueza
- Veterinary Medicine School. College of health sciences, San Francisco de Quito University, Quito, Ecuador.,Social medicine and global challenges Institute. College of health sciences, San Francisco de Quito University, Quito, Ecuador
| | - Kleber Stalin Vasquez
- General coordination of animal health, Phytozoosanitary Regulation and Control Agency, Quito, Ecuador
| | - Jose Henrique Grisi-Filho
- Preventive Veterinary Medicine Department. School of Veterinary Medicine and Animal Science, University of São Paulo, Sao Paulo, Brazil
| | - Marcos Amaku
- Preventive Veterinary Medicine Department. School of Veterinary Medicine and Animal Science, University of São Paulo, Sao Paulo, Brazil
| | | | - Fernando Ferreira
- Preventive Veterinary Medicine Department. School of Veterinary Medicine and Animal Science, University of São Paulo, Sao Paulo, Brazil
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