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Cardenas NC, Valencio A, Sanchez F, O'Hara KC, Machado G. Analyzing the intrastate and interstate swine movement network in the United States. Prev Vet Med 2024; 230:106264. [PMID: 39003835 DOI: 10.1016/j.prevetmed.2024.106264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/10/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024]
Abstract
Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.
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Affiliation(s)
- Nicolas C Cardenas
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Arthur Valencio
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Felipe Sanchez
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
| | - Kathleen C O'Hara
- US Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.
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2
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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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3
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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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4
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Andraud M, Hammami P, Hayes BH, Galvis JA, Vergne T, Machado G, Rose N. Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision-makers. Transbound Emerg Dis 2022; 69:e2132-e2144. [PMID: 35390229 DOI: 10.1111/tbed.14550] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
Abstract
African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mathieu Andraud
- ANSES, EPISABE Unit, Ploufragan-Plouzané-Niort Laboratory, Ploufragan, France
| | - Pachka Hammami
- ANSES, EPISABE Unit, Ploufragan-Plouzané-Niort Laboratory, Ploufragan, France
| | | | - Jason Ardila Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Timothée Vergne
- UMR ENVT-INRAE IHAP, National Veterinary School of Toulouse, Toulouse, France
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Nicolas Rose
- ANSES, EPISABE Unit, Ploufragan-Plouzané-Niort Laboratory, Ploufragan, France
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Fanelli A, Pellegrini F, Camero M, Catella C, Buonavoglia D, Fusco G, Martella V, Lanave G. Genetic Diversity of Porcine Circovirus Types 2 and 3 in Wild Boar in Italy. Animals (Basel) 2022; 12:953. [PMID: 35454199 PMCID: PMC9031215 DOI: 10.3390/ani12080953] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 01/27/2023] Open
Abstract
Porcine circovirus (PCV) infection is associated with relevant economic impact to the pig industry. To date, four species of PCV (PCV1 to 4) have been identified but only PCV2 has been associated firmly with disease in pigs. The objective of this study was to assess the prevalence of PCV2 and PCV3 in the wild boar population in Basilicata region, Southern Italy, since this region is characterized by large forested and rural areas and the anthropic pressure is lower than in other Italian regions. Liver samples from 82 hunted wild boar were collected in 2021 from 3 different hunting districts. Sixty (73%, 95%CI: 63-82) samples tested positive for PCVs by quantitative PCR. In detail, 22 (27%, 95%CI: 18-37) were positive for PCV2, 58 (71%, 95%CI: 60-79) for PCV3, and 20 (24.4%, 95%CI 16-35) for both PCV2 and PCV3. On genome sequencing, different types and sub-types of PCV2 and PCV3 were identified, remarking a genetic diversity and hinting to a global circulation for the identified PCV strains. Overall, the high prevalence suggests that PCV2 and PCV3 infections are endemic in the wild boar population, posing risks for semi-intensive and free-range pig farming, typical of this region, due to contact with PCV-infected wild boar.
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Affiliation(s)
- Angela Fanelli
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Francesco Pellegrini
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Michele Camero
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Cristiana Catella
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Domenico Buonavoglia
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Giovanna Fusco
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, NA, Italy;
| | - Vito Martella
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
| | - Gianvito Lanave
- Department of Veterinary Medicine, University Aldo Moro of Bari, 70010 Valenzano, BA, Italy; (F.P.); (M.C.); (C.C.); (D.B.); (V.M.)
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Ishikawa K, Doneva R, Raichev EG, Peeva S, Doichev VD, Amaike Y, Nishita Y, Kaneko Y, Masuda R. Population genetic structure and diversity of the East Balkan Swine (Sus scrofa) in Bulgaria, revealed by mitochondrial DNA and microsatellite analyses. Anim Sci J 2021; 92:e13630. [PMID: 34520087 DOI: 10.1111/asj.13630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 08/06/2021] [Accepted: 08/23/2021] [Indexed: 11/28/2022]
Abstract
The East Balkan Swine (EBS) is the only indigenous pig breed in Bulgaria. We analyzed the mitochondrial DNA (mtDNA) control region and 21 microsatellite loci for 198 individuals from 11 farms in Bulgaria. Obtained 11 mtDNA haplotypes including three novel ones were grouped to two major clades, European clade E1 (146/198 individuals, 73.7%) and Asian clade A (52/198, 26.3%). The mixture of the two clades may have resulted from historical crossbreeding between the European and Asian pig breeds. Clade A was frequent in southeastern Bulgaria (Burgas Province), but less frequent or absent in northeastern Bulgaria (Varna and Shumen Provinces). The distribution of Europe- and Asia-specific haplotypes relative to EBS farm locations could be attributed to regional differences of breeding systems (e.g., crossbreeding with imported commercial pigs). A microsatellite analysis showed high heterozygosities for all the EBS farms, and negative inbreeding coefficients presumably due to crossing with commercial pigs or wild boars and/or efforts to reduce inbreeding by farmers. Bayesian clustering analyses showed that all farm populations are genetically well distinguishable from one another. Although diversity has been maintained by the efforts of farmers and a breeding association, the effective population size remains small, and conservation efforts should be continued.
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Affiliation(s)
- Keita Ishikawa
- Department of Natural History Sciences, Graduate School of Science, Hokkaido University, Sapporo, Japan
| | - Radostina Doneva
- Association for Breeding and Preserving of the East Balkan Swine, Shumen, Bulgaria
| | | | | | | | - Yosuke Amaike
- Department of Biological Sciences, Faculty of Science, Hokkaido University, Sapporo, Japan
| | - Yoshinori Nishita
- Department of Biological Sciences, Faculty of Science, Hokkaido University, Sapporo, Japan
| | - Yayoi Kaneko
- Wildlife Conservation Laboratory, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Ryuichi Masuda
- Department of Natural History Sciences, Graduate School of Science, Hokkaido University, Sapporo, Japan.,Department of Biological Sciences, Faculty of Science, Hokkaido University, Sapporo, Japan
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7
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Rolesu S, Mandas D, Loi F, Oggiano A, Dei Giudici S, Franzoni G, Guberti V, Cappai S. African Swine Fever in Smallholder Sardinian Farms: Last 10 Years of Network Transmission Reconstruction and Analysis. Front Vet Sci 2021; 8:692448. [PMID: 34395576 PMCID: PMC8361751 DOI: 10.3389/fvets.2021.692448] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/30/2021] [Indexed: 12/20/2022] Open
Abstract
African swine fever (ASF) is a viral disease of suids that frequently leads to death. There are neither licensed vaccines nor treatments available, and even though humans are not susceptible to the disease, the serious socio-economic consequences associated with ASF have made it one of the most serious animal diseases of the last century. In this context, prevention and early detection play a key role in controlling the disease and avoiding losses in the pig value chain. Target biosecurity measures are a strong strategy against ASF virus (ASFV) incursions in farms nowadays, but to be efficient, these measures must be well-defined and easy to implement, both in commercial holdings and in the backyard sector. Furthermore, the backyard sector is of great importance in low-income settings, mainly for social and cultural practices that are highly specific to certain areas and communities. These contexts need to be addressed when authorities decide upon the provisions that should be applied in the case of infection or decide to combine them with strict preventive measures to mitigate the risk of virus spread. The need for a deeper understanding of the smallholder context is essential to prevent ASFV incursion and spread. Precise indications for pig breeding and risk estimation for ASFV introduction, spread and maintenance, taking into account the fact that these recommendations would be inapplicable in some contexts, are the keys for efficient target control measures. The aim of this work is to describe the 305 outbreaks that occurred in domestic pigs in Sardinia during the last epidemic season (2010-2018) in depth, providing essential features associated with intensive and backyard farms where the outbreaks occurred. In addition, the study estimates the average of secondary cases by kernel transmission network. Considering the current absence of ASF outbreaks in domestic pig farms in Sardinia since 2018, this work is a valid tool to specifically estimate the risk associated with different farm types and update our knowledge in this area.
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Affiliation(s)
- Sandro Rolesu
- Sardinian Regional Veterinary Epidemiological Observatory, Istituto Zooprofilattico Sperimentale della Sardegna “G. Pegreffi”, Cagliari, Italy
| | - Daniela Mandas
- Sardinian Regional Veterinary Epidemiological Observatory, Istituto Zooprofilattico Sperimentale della Sardegna “G. Pegreffi”, Cagliari, Italy
| | - Federica Loi
- Sardinian Regional Veterinary Epidemiological Observatory, Istituto Zooprofilattico Sperimentale della Sardegna “G. Pegreffi”, Cagliari, Italy
| | - Annalisa Oggiano
- Department of Animal Health, Istituto Zooprofilattico Sperimentale Della Sardegna, Sassari, Italy
| | - Silvia Dei Giudici
- Department of Animal Health, Istituto Zooprofilattico Sperimentale Della Sardegna, Sassari, Italy
| | - Giulia Franzoni
- Department of Animal Health, Istituto Zooprofilattico Sperimentale Della Sardegna, Sassari, Italy
| | - Vittorio Guberti
- ISPRA—Institute for Environmental Protection and Research, Rome, Italy
| | - Stefano Cappai
- Sardinian Regional Veterinary Epidemiological Observatory, Istituto Zooprofilattico Sperimentale della Sardegna “G. Pegreffi”, Cagliari, Italy
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Schettino DN, Korennoy FI, Perez AM. Risk of Introduction of Classical Swine Fever Into the State of Mato Grosso, Brazil. Front Vet Sci 2021; 8:647838. [PMID: 34277750 PMCID: PMC8280757 DOI: 10.3389/fvets.2021.647838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/28/2021] [Indexed: 11/13/2022] Open
Abstract
Classical swine fever (CSF) is considered one of the most important diseases of swine because of the far-reaching economic impact the disease causes to affected countries and regions. The state of Mato Grosso (MT) is part of Brazil's CSF-free zone. CSF status is uncertain in some of MT's neighboring States and countries, which has resulted in the perception that MT is at high risk for the disease. However, the risk for CSF introduction into MT has not been previously assessed. Here, we estimated that the risk for CSF introduction into the MT is highly heterogeneous. The risk associated with shipment of commercial pigs was concentrated in specific municipalities with intense commercial pig production, whereas the risk associated with movement of wild boars was clustered in certain municipalities located close to the state's borders, mostly in northern and southwestern MT. Considering the two pathways of possible introduction assessed here, these results demonstrate the importance of using alternative strategies for surveillance that target different routes and account for different likelihoods of introduction. These results will help to design, implement, and monitor surveillance activities for sustaining the CSF-free status of MT at times when Brazil plans to expand the recognition of disease-free status for other regions in the country.
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Affiliation(s)
- Daniella N Schettino
- Department of Veterinary Population Medicine, Center for Animal Health and Food Safety, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States.,Animal Health Coordination, Instituto de Defesa Agropecuária de Mato Grosso (INDEA-MT), Mato Grosso, Cuiabá, Brazil
| | - Fedor I Korennoy
- FGBI Federal Centre for Animal Health (FGBI ARRIAH), Vladimir, Russia
| | - Andres M Perez
- Department of Veterinary Population Medicine, Center for Animal Health and Food Safety, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
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9
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Spatial and network analysis of U.S. livestock movements based on Interstate Certificates of Veterinary Inspection. Prev Vet Med 2021; 193:105391. [PMID: 34091089 DOI: 10.1016/j.prevetmed.2021.105391] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/18/2021] [Accepted: 05/23/2021] [Indexed: 11/24/2022]
Abstract
Livestock movements are a common pathway for the spread infectious diseases in a population. An understanding of livestock movement patterns is needed to understand national transmission risks of highly infectious diseases during epidemics. Social Network Analysis (SNA) is an approach that helps to describe the relationships among individuals and the implications of those relationships. We used SNA to describe the contact structure of livestock movements throughout the contiguous U.S. from April 1st, 2015 to March 31st, 2016. We describe 4 network types: beef cattle, dairy cattle, swine, and small ruminant. Livestock movement data were sourced from Interstate Certificates of Veterinary Inspection (ICVI) while county-level farm demographic data were from the National Agricultural Statistics Service (NASS). In the described networks, nodes are represented by counties and arcs by shipments between nodes; the networks were weighted based on the number of shipments between nodes. For the analyses, movement data were aggregated at the county level and on an annual basis. Measures of centrality and cohesiveness were computed and identification of trade-communities in all networks was conducted. During the study period, a total of 219,042 movements were recorded and beef cattle movements accounted for 63 % of all movements. At least 70 % of U.S. counties were present in each of the networks, but the density of arcs was less than 2% in all networks. In the beef cattle network, counties with high out-degree were strongly correlated (0.8) with the number of beef cows per county while for the dairy cattle network a strong correlation (>0.86) was found with the number of dairy cattle per km2 at the county level. All networks were found to have between 4 and 6 large communities (50 counties or more per community), and were geographically clustered except for the communities in the small ruminant network. Outputs reported in these analyses can help to understand the structure of the contact networks for beef cattle, dairy cattle, swine, and small ruminants. They may also be used in conjunction with simulation modeling to evaluate spread of highly infectious disease such as foot-and-mouth disease at the national level and to evaluate the application of intervention strategies.
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10
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Busch F, Haumont C, Penrith ML, Laddomada A, Dietze K, Globig A, Guberti V, Zani L, Depner K. Evidence-Based African Swine Fever Policies: Do We Address Virus and Host Adequately? Front Vet Sci 2021; 8:637487. [PMID: 33842576 PMCID: PMC8024515 DOI: 10.3389/fvets.2021.637487] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/18/2021] [Indexed: 12/15/2022] Open
Abstract
African swine fever (ASF) is one of the most threatening diseases for the pig farming sector worldwide. Prevention, control and eradication remain a challenge, especially in the absence of an effective vaccine or cure and despite the relatively low contagiousness of this pathogen in contrast to Classical Swine Fever or Foot and Mouth disease, for example. Usually lethal in pigs and wild boar, this viral transboundary animal disease has the potential to significantly disrupt global trade and threaten food security. This paper outlines the importance of a disease-specific legal framework, based on the latest scientific evidence in order to improve ASF control. It compares the legal basis for ASF control in a number of pig-producing regions globally, considering diverse production systems, taking into account current scientific evidence in relation to ASF spread and control. We argue that blanket policies that do not take into account disease-relevant characteristics of a biological agent, nor the specifics under which the host species are kept, can hamper disease control efforts and may prove disproportionate.
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Affiliation(s)
- Frank Busch
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institute, Greifswald, Germany
| | - Céline Haumont
- National College of Veterinary Medicine, Food Science and Engineering, Oniris, Nantes, France
| | - Mary-Louise Penrith
- Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Pretoria, South Africa
| | | | - Klaas Dietze
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institute, Greifswald, Germany
| | - Anja Globig
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institute, Greifswald, Germany
| | - Vittorio Guberti
- Istituto Superiore per la Protezione e la Ricerca Ambientale, Epidemiology and Ecology Unit, Ozzano Emilia, Italy
| | - Laura Zani
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institute, Greifswald, Germany
| | - Klaus Depner
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institute, Greifswald, Germany
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11
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A Review of Risk Factors of African Swine Fever Incursion in Pig Farming within the European Union Scenario. Pathogens 2021; 10:pathogens10010084. [PMID: 33478169 PMCID: PMC7835761 DOI: 10.3390/pathogens10010084] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/15/2021] [Accepted: 01/18/2021] [Indexed: 12/21/2022] Open
Abstract
African swine fever (ASF) is a notifiable viral disease of pigs and wild boars that could lead to serious economic losses for the entire European pork industry. As no effective treatment or vaccination is available, disease prevention and control rely on strictly enforced biosecurity measures tailored to the specific risk factors of ASF introduction within domestic pig populations. Here, we present a review addressing the risk factors associated with different European pig farming systems in the context of the actual epidemiological scenario. A list of keywords was combined into a Boolean query, “African swine fever” AND (“Risk factors” OR “Transmission” OR “Spread” OR “Pig farming” OR “Pigs” OR “Wild boars”); was run on 4 databases; and resulted in 52 documents of interest being reviewed. Based on our review, each farming system has its own peculiar risk factors: commercial farms, where best practices are already in place, may suffer from unintentional breaches in biosecurity, while backyard and outdoor farms may suffer from poor ASF awareness, sociocultural factors, and contact with wild boars. In the literature selected for our review, human-related activities and behaviours are presented as the main risks, but we also stress the need to implement biosecurity measures also tailored to risks factors that are specific for the different pig farming practices in the European Union (EU).
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Crescio MI, Mastrantonio G, Bertolini S, Maurella C, Adkin A, Ingravalle F, Simons RRL, DeNardi M, Stark K, Estrada-Peña A, Ru G. Using network analysis to identify seasonal patterns and key nodes for risk-based surveillance of pig diseases in Italy. Transbound Emerg Dis 2020; 68:3541-3551. [PMID: 33338318 DOI: 10.1111/tbed.13960] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 11/26/2022]
Abstract
The description of the pattern of livestock movements between herds provides essential information for both improving risk-based surveillance and to understand the likely spread of infectious diseases. This study provides a description of the temporal pattern of pig movements recorded in Italy on a 4-year period (2013-2016). Data, provided by the National Livestock registry, were described by social network analysis and the application of a walk-trap algorithm for community detection. Our results show a highly populated community located in Northern Italy, which is the focal point of the Italian industrial pig production and as a general pattern an overall decline of medium and backyard farms and an increase in the number of large farms, in agreement with the trend observed by other EU pig-producing countries. A seasonal pattern of all the parameters evaluated, including the number of active nodes in both the intensive and smaller production systems, emerged: that is characterized by a higher number of movements in spring and autumn, linked with the breeding and production cycle as pigs moved from the growing to the finishing phase and with periods of increased slaughtering at Christmas and Easter. The same pattern was found when restricting the analysis to imported pig batches. Outbreaks occurring during these periods would have a greater impact on the spread of infectious diseases; therefore, targeted surveillance may be appropriate. Finally, potential super-spreader nodes have been identified and represent 0.47% of the total number of pig holdings (n = 477). Those nodes are present during the whole study period with a similar ranking in their potential of being super-spreaders. Most of them were in Northern Italy, but super-spreaders with high mean out-degree centrality were also located in other Regions. Seasonality, communities and super-spreaders should be considered when planning surveillance activity and when applying disease control strategies.
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Affiliation(s)
- Maria Ines Crescio
- Istituto Zooprofilattico Sperimentale di Piemonte, Liguria e Valle d'Aosta (IZSTO), Torino, Italy
| | | | - Silvia Bertolini
- Istituto Zooprofilattico Sperimentale di Piemonte, Liguria e Valle d'Aosta (IZSTO), Torino, Italy
| | - Cristiana Maurella
- Istituto Zooprofilattico Sperimentale di Piemonte, Liguria e Valle d'Aosta (IZSTO), Torino, Italy
| | | | - Francesco Ingravalle
- Istituto Zooprofilattico Sperimentale di Piemonte, Liguria e Valle d'Aosta (IZSTO), Torino, Italy
| | | | | | | | | | - Giuseppe Ru
- Istituto Zooprofilattico Sperimentale di Piemonte, Liguria e Valle d'Aosta (IZSTO), Torino, Italy
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13
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Henritzi D, Petric PP, Lewis NS, Graaf A, Pessia A, Starick E, Breithaupt A, Strebelow G, Luttermann C, Parker LMK, Schröder C, Hammerschmidt B, Herrler G, Beilage EG, Stadlbauer D, Simon V, Krammer F, Wacheck S, Pesch S, Schwemmle M, Beer M, Harder TC. Surveillance of European Domestic Pig Populations Identifies an Emerging Reservoir of Potentially Zoonotic Swine Influenza A Viruses. Cell Host Microbe 2020; 28:614-627.e6. [PMID: 32721380 DOI: 10.1016/j.chom.2020.07.006] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/22/2020] [Accepted: 07/07/2020] [Indexed: 12/31/2022]
Abstract
Swine influenza A viruses (swIAVs) can play a crucial role in the generation of new human pandemic viruses. In this study, in-depth passive surveillance comprising nearly 2,500 European swine holdings and more than 18,000 individual samples identified a year-round presence of up to four major swIAV lineages on more than 50% of farms surveilled. Phylogenetic analyses show that intensive reassortment with human pandemic A(H1N1)/2009 (H1pdm) virus produced an expanding and novel repertoire of at least 31 distinct swIAV genotypes and 12 distinct hemagglutinin/neuraminidase combinations with largely unknown consequences for virulence and host tropism. Several viral isolates were resistant to the human antiviral MxA protein, a prerequisite for zoonotic transmission and stable introduction into human populations. A pronounced antigenic variation was noted in swIAV, and several H1pdm lineages antigenically distinct from current seasonal human H1pdm co-circulate in swine. Thus, European swine populations represent reservoirs for emerging IAV strains with zoonotic and, possibly, pre-pandemic potential.
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Affiliation(s)
- Dinah Henritzi
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Philipp Peter Petric
- Institute of Virology, Medical Center, University of Freiburg, 79104 Freiburg, Germany; Spemann Graduate School of Biology and Medicine, University of Freiburg, 79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Nicola Sarah Lewis
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, Hertfordshire AL9 7TA, UK; OIE/FAO International Reference Laboratory for avian influenza, swine influenza and Newcastle Disease, Animal and Plant Health Agency (APHA) - Weybridge, Addlestone, Surrey KT15 3NB, UK
| | - Annika Graaf
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Alberto Pessia
- Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
| | - Elke Starick
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Angele Breithaupt
- Department of Experimental Animal Facilities and Biorisk Management, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Günter Strebelow
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Christine Luttermann
- Institute of Immunology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Larissa Mareike Kristin Parker
- Institute of Virology, Medical Center, University of Freiburg, 79104 Freiburg, Germany; Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Charlotte Schröder
- Department of Experimental Animal Facilities and Biorisk Management, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Bärbel Hammerschmidt
- Department of Experimental Animal Facilities and Biorisk Management, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Georg Herrler
- Institute of Virology, Department of Infectious Diseases, University of Veterinary Medicine Hannover, Bünteweg 2, 30559 Hannover, Germany
| | - Elisabeth Große Beilage
- Field Station for Epidemiology, University of Veterinary Medicine Hannover, Büscheler Str. 9, 49456 Bakum, Germany
| | - Daniel Stadlbauer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Viviana Simon
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Silke Wacheck
- Ceva Santé Animale (former IDT Biologika GmbH), 06861 Dessau-Rosslau, Germany
| | - Stefan Pesch
- Ceva Santé Animale (former IDT Biologika GmbH), 06861 Dessau-Rosslau, Germany
| | - Martin Schwemmle
- Institute of Virology, Medical Center, University of Freiburg, 79104 Freiburg, Germany; Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany.
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany.
| | - Timm Clemens Harder
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut (FLI), Suedufer 10, 17493 Greifswald-Insel Riems, Germany.
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14
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Baron JN, Aznar MN, Monterubbianesi M, Martínez-López B. Application of network analysis and cluster analysis for better prevention and control of swine diseases in Argentina. PLoS One 2020; 15:e0234489. [PMID: 32555649 PMCID: PMC7299388 DOI: 10.1371/journal.pone.0234489] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/26/2020] [Indexed: 11/19/2022] Open
Abstract
RATIONALE/BACKGROUND Though much smaller than the bovine industry, the porcine sector in Argentina involves a large number of farms and represents a significant economic sector. In recent years Argentina has implemented a national registry of swine movements amongst other measures, in an effort to control and eventually eradicate endemic Aujesky's disease. Such information can prove valuable in assessing the risk of transmission between farms for endemic diseases but also for other diseases at risk of emergence. METHODS Shipment data from 2011 to 2016 were analyzed in an effort to define strategic locations and times at which control and surveillance efforts should be focused to provide cost-effective interventions. Social network analysis (SNA) was used to characterize the network as a whole and at the individual farm and market level to help identify important nodes. Spatio-temporal trends of pig movements were also analyzed. Finally, in an attempt to classify farms and markets in different groups based on their SNA metrics, we used factor analysis for mixed data (FAMD) and hierarchical clustering. RESULTS The network involved approximate 136,000 shipments for a total of 6 million pigs. Over 350 markets and 17,800 production units participated in shipments with another 83,500 not participating. Temporal data of shipments and network metrics showed peaks in shipments in September and October. Most shipments where within provinces, with Buenos Aires, Cordoba and Santa Fe concentrating 61% of shipments. Network analysis showed that markets are involved in relatively few shipments but hold strategic positions with much higher betweenness compared to farms. Hierarchical clustering yielded four groups based on SNA metrics and node characteristics which can be broadly described as: 1. small and backyard farms; 2. industrial farms; 3. markets; and 4. a single outlying market with extreme centrality values. CONCLUSION Characterizing the network structure and spatio-temporal characteristics of Argentine swine shipments provides valuable information that can guide targeted and more cost-effective surveillance and control programs. We located key nodes where efforts should be prioritized. Pig network characteristics and patterns can be used to create dynamic disease transmission models, which can both be used in assessing the impact of emerging diseases and guiding efforts to eradicate endemic ones.
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Affiliation(s)
- Jerome N. Baron
- Department of Medicine and Epidemiology, School of Veterinary Medicine, Center for Animal Disease Modeling and Surveillance (CADMS), University of California Davis, Davis, California, United States of America
| | - Maria N. Aznar
- Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
| | - Mariela Monterubbianesi
- Servicio Nacional de Sanidad y Calidad Agroalimentaria de la Republica Argentina (SENASA), Buenos Aires, Argentina
| | - Beatriz Martínez-López
- Department of Medicine and Epidemiology, School of Veterinary Medicine, Center for Animal Disease Modeling and Surveillance (CADMS), University of California Davis, Davis, California, United States of America
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15
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Brzoska L, Fischer M, Lentz HHK. Hierarchical Structures in Livestock Trade Networks-A Stochastic Block Model of the German Cattle Trade Network. Front Vet Sci 2020; 7:281. [PMID: 32537461 PMCID: PMC7266987 DOI: 10.3389/fvets.2020.00281] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/27/2020] [Indexed: 12/16/2022] Open
Abstract
Trade of cattle between farms forms a complex trade network. We investigate partitions of this network for cattle trade in Germany. These partitions are groups of farms with similar properties and they are inferred directly from the trade pattern between farms. We make use of a rather new method known as stochastic block modeling (SBM) in order to divide the network into smaller units. SBM turns out to outperform the more established community detection method in the context of disease control in terms of trade restriction. Moreover, SBM is also superior to geographical based trade restrictions and could be a promising approach for disease control.
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Affiliation(s)
- Laura Brzoska
- Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany.,Institute of Epidemiology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, Germany
| | - Mareike Fischer
- Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany
| | - Hartmut H K Lentz
- Institute of Epidemiology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, Germany
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16
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Marti E, Gros M, Boy-Roura M, Ovejero J, Busquets AM, Colón J, Petrovic M, Ponsá S. Pharmaceuticals removal in an on-farm pig slurry treatment plant based on solid-liquid separation and nitrification-denitrification systems. WASTE MANAGEMENT (NEW YORK, N.Y.) 2020; 102:412-419. [PMID: 31734552 DOI: 10.1016/j.wasman.2019.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/30/2019] [Accepted: 11/02/2019] [Indexed: 06/10/2023]
Abstract
The fate and degradation of 28 multiple-class veterinary pharmaceuticals in an on-farm pig slurry treatment plant based on solid-liquid separation and a nitrification-denitrification (NDN) sequence batch reactor (SBR) were evaluated for the first time. The pharmaceuticals detected at the highest concentrations in raw pig slurries belonged to the group of tetracycline antibiotics. Fluoroquinolone, lincosamide and pleuromutilin antibiotics and other drugs such as flubendazole and flunixin were also frequently detected. After solid-liquid separation, target compounds were distributed in an average of 64% onto the liquid fraction. Pharmaceuticals distributed in this fraction were removed in an average of almost 50% after being treated in NDN-SBR. Lincomycin was the compound with the highest removal percentage, reaching 100% reduction, while tetracyclines and fluoroquinolones showed moderate removal percentages (50 and 40%, respectively). Regarding nitrogen removal, NDN-SBR reduced a 77% of the content of this nutrient in the liquid slurry fraction.
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Affiliation(s)
- Elisabet Marti
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain
| | - Meritxell Gros
- Catalan Institute for Water Research (ICRA), C/Emili Grahit 101, 17003 Girona, Spain
| | - Mercè Boy-Roura
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain
| | - Jonatan Ovejero
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain
| | - Anna M Busquets
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain
| | - Joan Colón
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain
| | - Mira Petrovic
- Catalan Institute for Water Research (ICRA), C/Emili Grahit 101, 17003 Girona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Sergio Ponsá
- BETA Tech Center, Universitat de Vic-Universitat Central de Catalunya, C/de la Laura 13, 08500 Vic, Spain.
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17
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Porphyre T, Bronsvoort BMDC, Gunn GJ, Correia-Gomes C. Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry. Transbound Emerg Dis 2020; 67:1231-1246. [PMID: 31880086 DOI: 10.1111/tbed.13459] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 12/20/2019] [Accepted: 12/21/2019] [Indexed: 11/29/2022]
Abstract
When assessing the role of live animal trade networks in the spread of infectious diseases in livestock, attention has focused mainly on direct movements of animals between premises, whereas the role of haulage vehicles used during transport, an indirect route for disease transmission, has largely been ignored. Here, we have assessed the impact of sharing haulage vehicles from livestock transport service providers on the connectivity between farms as well as on the spread of swine infectious diseases in Great Britain (GB). Using all pig movement records between April 2012 and March 2014 in GB, we built a series of directed and weighted static multiplex networks consisting of two layers of identical nodes, where nodes (farms) are linked either by (a) the direct movement of pigs and (b) the shared use of haulage vehicles. The haulage contact definition integrates the date of the move and the duration Δ s that lorries are left contaminated by pathogens, hence accounting for the temporal aspect of contact events. For increasing Δ s , descriptive network analyses were performed to assess the role of haulage on network connectivity. We then explored how viruses may spread throughout the GB pig sector by computing the reproduction number R . Our results showed that sharing haulage vehicles increases the number of contacts between farms by >50% and represents an important driver of disease transmission. In particular, sharing haulage vehicles, even if Δ s < 1 day, will limit the benefit of the standstill regulation, increase the number of premises that could be infected in an outbreak, and more easily raise R above 1. This work confirms that sharing haulage vehicles has significant potential for spreading infectious diseases within the pig sector. The cleansing and disinfection process of haulage vehicles is therefore a critical control point for disease transmission risk mitigation.
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Affiliation(s)
- Thibaud Porphyre
- The Roslin Institute, University of Edinburgh, Midlothian, Scotland
| | | | - George J Gunn
- Epidemiology Research Unit, Department of Veterinary and Animal Science, Scotland's Rural College (SRUC), Inverness, Scotland
| | - Carla Correia-Gomes
- Epidemiology Research Unit, Department of Veterinary and Animal Science, Scotland's Rural College (SRUC), Inverness, Scotland
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18
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Alarcón LV, Cipriotti PA, Monterubbianessi M, Perfumo C, Mateu E, Allepuz A. Network analysis of pig movements in Argentina: Identification of key farms in the spread of infectious diseases and their biosecurity levels. Transbound Emerg Dis 2019; 67:1152-1163. [PMID: 31785089 DOI: 10.1111/tbed.13441] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 11/29/2022]
Abstract
This study uses network analysis to evaluate how swine movements in Argentina could contribute to disease spread. Movement data for the 2014-2017 period were obtained from Argentina's online livestock traceability registry and categorized as follows: animals of high genetic value sent to other farms, animals to or from markets, animals sent to finisher operations and slaughterhouse. A network analysis was carried out considering the first three movement types. First, descriptive, centrality and cohesion measures were calculated for each movement type and year. Next, to determine whether networks had a small-world topology, these were compared with the results from random Erdös-Rényi network simulations. Then, the basic reproductive number (R0 ) of the genetic network, the group of farms with higher potential for disease spread standing at the top of the production chain, was calculated to identify farms acting as super-spreaders. Finally, their external biosecurity scores were evaluated. The genetic network in Argentina presented a scale-free and small-world topology. Thus, we estimate that disease spread would be fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super-spreaders in the genetic network for all years, while other 55 were super-spreaders at least once, from an average of 1,613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R0 indicating that few farms have a key role in disease spread. When biosecurity scores of the most relevant super-spreaders were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina's pig production network. These results highlight the usefulness of establishing targeted surveillance and intervention programmes, emphasizing the need for better biosecurity scores in Argentinean swine production units, especially in super-spreader farms.
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Affiliation(s)
- Laura V Alarcón
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Pablo A Cipriotti
- Facultad de Agronomía - IFEVA, Universidad de Buenos Aires/CONICET, Buenos Aires, Argentina
| | - Mariela Monterubbianessi
- National Service for Health and AgriFood Quality (SENASA), Ministerio de Producción y Trabajo, Buenos Aires, Argentina
| | - Carlos Perfumo
- Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Enric Mateu
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alberto Allepuz
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, Barcelona, Spain
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19
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Gorsich EE, Miller RS, Mask HM, Hallman C, Portacci K, Webb CT. Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection. Sci Rep 2019; 9:3915. [PMID: 30850719 PMCID: PMC6408505 DOI: 10.1038/s41598-019-40556-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 01/24/2019] [Indexed: 11/10/2022] Open
Abstract
Domestic swine production in the United States is a critical economic and food security industry, yet there is currently no large-scale quantitative assessment of swine shipments available to support risk assessments. In this study, we provide a national-level characterization of the swine industry by quantifying the demographic (i.e. age, sex) patterns, spatio-temporal patterns, and the production diversity within swine shipments. We characterize annual networks of swine shipments using a 30% stratified sample of Interstate Certificates of Veterinary Inspection (ICVI), which are required for the interstate movement of agricultural animals. We used ICVIs in 2010 and 2011 from eight states that represent 36% of swine operations and 63% of the U.S. swine industry. Our analyses reflect an integrated and spatially structured industry with high levels of spatial heterogeneity. Most shipments carried young swine for feeding or breeding purposes and carried a median of 330 head (range: 1–6,500). Geographically, most shipments went to and were shipped from Iowa, Minnesota, and Nebraska. This work, therefore, suggests that although the swine industry is variable in terms of its size and type of swine, counties in states historically known for breeding and feeding operations are consistently more central to the shipment network.
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Affiliation(s)
- Erin E Gorsich
- Department of Biology, Colorado State University, Fort Collins, CO, USA. .,Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA. .,The Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK. .,School of Life Sciences, University of Warwick, Coventry, UK.
| | - Ryan S Miller
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Holly M Mask
- Department of Biology, Colorado State University, Fort Collins, CO, USA
| | - Clayton Hallman
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Katie Portacci
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
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20
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Gómez-Vázquez JP, Quevedo-Valle M, Flores U, Portilla Jarufe K, Martínez-López B. Evaluation of the impact of live pig trade network, vaccination coverage and socio-economic factors in the classical swine fever eradication program in Peru. Prev Vet Med 2019; 162:29-37. [PMID: 30621896 DOI: 10.1016/j.prevetmed.2018.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 09/26/2018] [Accepted: 10/25/2018] [Indexed: 12/01/2022]
Abstract
Classical swine fever (CSF) is a viral infectious disease of swine with significant economic impact in the affected countries due to the limitation of trade, culling of infected animals and production losses. In Latin America, CSF is endemic in several countries including Ecuador, Bolivia, Brazil and Peru. Since 2010, the National Veterinary Services of Peru have been working to better control and eradicate the disease with an intensive vaccination program. The aim of this study was to evaluate the effectiveness of the vaccination program and determine which factors are still contributing to the persistence of the disease in certain regions of Peru. We integrated the data from the vaccination campaign, the live pig movement network and other socioeconomic indicators into a multilevel logistic regression model to evaluate their association with CSF occurrence at district level. The results revealed that high vaccination coverage significantly reduces the risk of CSF occurrence (OR = 0.07), supporting the effectiveness of the vaccination program. Districts belonging to large and medium pig trade network communities (as identified with walktrap algorithm) had higher probability to CSF occurrence (OR = 2.83 and OR = 5.83, respectively). The human development index (HDI) and the presence of a slaughterhouse in the district was also significantly associated with an increased likelihood of CSF occurrence (OR = 1.52 and OR = 3.25, respectively). Districts receiving a high proportion of the movements from districts that were infected in the previous year were also at higher risk of CSF occurrence (OR = 3.30). These results should be useful to guide the prioritization of vaccination strategies and may help to design other intervention strategies (e.g., target education, movement restrictions, etc.) in high-risk areas to more rapidly advance in the eradication of CSF in Peru.
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Affiliation(s)
- J P Gómez-Vázquez
- Center of Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, United States
| | | | - U Flores
- Dirección de Sanidad Animal SENASA, Lima, Peru
| | | | - B Martínez-López
- Center of Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, United States.
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Jurado C, Martínez-Avilés M, De La Torre A, Štukelj M, de Carvalho Ferreira HC, Cerioli M, Sánchez-Vizcaíno JM, Bellini S. Relevant Measures to Prevent the Spread of African Swine Fever in the European Union Domestic Pig Sector. Front Vet Sci 2018; 5:77. [PMID: 29713637 PMCID: PMC5912175 DOI: 10.3389/fvets.2018.00077] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 03/26/2018] [Indexed: 12/30/2022] Open
Abstract
During the past decade, African swine fever (ASF) has spread from the Caucasus region to eastern European Union countries affecting domestic pig and wild boar populations. In order to avert ASF spread, mitigation measures targeting both populations have been established. However, despite these efforts, ASF has been reported in thirteen different countries (Georgia, Azerbaijan, Armenia, the Russian Federation, Ukraine, Belarus, Estonia, Latvia, Lithuania, Poland, Moldova, Czech Republic, and Romania). In the absence of an effective vaccine or treatment to ASF, introduction and spread of ASF onto domestic pig farms can only be prevented by strict compliance to control measures. This study systematically reviewed available measures to prevent the spread of ASF in the EU domestic pig sector distinguishing between commercial, non-commercial, and outdoor farms. The search was performed in PubMed and using a common browser. A total of 52 documents were selected for the final review process, which included scientific articles, reports, EU documents and official recommendations, among others. From this literature review, 37 measures were identified as preventive measures for the introduction and spread of ASF. Subsequently, these measures were assessed by ASF experts for their relevance in the mitigation of ASF spread on the three mentioned types of farms. All experts agreed that some of the important preventive measures for all three types of farms were: the identification of animals and farm records; strict enforcement of the ban on swill feeding; and containment of pigs, so as to not allow direct or indirect pig–pig and/or pig–wild boar contacts. Other important preventive measures for all farms were education of farmers, workers, and operators; no contact between farmers and farm staff and external pigs; appropriate removal of carcasses, slaughter residues, and food waste; proper disposal of manure and dead animals, and abstaining from hunting activities during the previous 48 h (allowing a 48 h interval between hunting and being in contact with domestic pigs). Finally, all experts identified that the important preventive measures for non-commercial and outdoor farms is to improve access of those farms to veterinarians and health services.
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Affiliation(s)
- Cristina Jurado
- VISAVET Health Surveillance Centre, Animal Health Department, Veterinary Faculty, Complutense University of Madrid, Madrid, Spain
| | - Marta Martínez-Avilés
- Animal Health Research Centre, National Institute for Agricultural and Food Research and Technology (INIA-CISA), Madrid, Spain
| | - Ana De La Torre
- Animal Health Research Centre, National Institute for Agricultural and Food Research and Technology (INIA-CISA), Madrid, Spain
| | - Marina Štukelj
- Veterinary Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - Monica Cerioli
- Istituto Zooprofilattico Sperimentale della Lombardia ed Emilia Romagna (IZSLER), Brescia, Italy
| | - José Manuel Sánchez-Vizcaíno
- VISAVET Health Surveillance Centre, Animal Health Department, Veterinary Faculty, Complutense University of Madrid, Madrid, Spain
| | - Silvia Bellini
- Istituto Zooprofilattico Sperimentale della Lombardia ed Emilia Romagna (IZSLER), Brescia, Italy
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22
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Effective surveillance for early classical swine fever virus detection will utilize both virus and antibody detection capabilities. Vet Microbiol 2018. [DOI: 10.1016/j.vetmic.2018.01.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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23
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Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs. Prev Vet Med 2017; 149:125-131. [PMID: 29290293 PMCID: PMC7126927 DOI: 10.1016/j.prevetmed.2017.11.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/14/2017] [Accepted: 11/16/2017] [Indexed: 01/13/2023]
Abstract
A method is proposed to explore the role of pig movements on pathogen epidemiology. Pig farm centrality in the network is associated with higher HEV seroprevalence. Some local areas are more at risk for HEV due to incoming pig movements. Animal movements should be included in risk-based surveillance strategies.
Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p < 0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.
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24
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Salines M, Andraud M, Rose N. Pig movements in France: Designing network models fitting the transmission route of pathogens. PLoS One 2017; 12:e0185858. [PMID: 29049305 PMCID: PMC5648108 DOI: 10.1371/journal.pone.0185858] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 09/20/2017] [Indexed: 11/23/2022] Open
Abstract
Pathogen spread between farms results from interaction between the epidemiological characteristics of infectious agents, such as transmission route, and the contact structure between holdings. The objective of our study was to design network models of pig movements matching with epidemiological features of pathogens. Our first model represents the transmission of infectious diseases between farms only through the introduction of animals to holdings (Animal Introduction Model AIM), whereas the second one also accounts for pathogen spread through intermediate transit of trucks through farms even without any animal unloading (i.e. indirect transmission–Transit Model TM). To take the pyramidal organisation of pig production into consideration, these networks were studied at three different scales: the whole network and two subnetworks containing only breeding or production farms. The two models were applied to pig movement data recorded in France from June 2012 to December 2014. For each type of model, we calculated network descriptive statistics, looked for weakly/strongly connected components (WCCs/SCCs) and communities, and analysed temporal patterns. Whatever the model, the network exhibited scale-free and small-world topologies. Differences in centrality values between the two models showed that nucleus, multiplication and post-weaning farms played a key role in the spread of diseases transmitted exclusively by the introduction of infected animals, whereas farrowing and farrow-to-finish herds appeared more vulnerable to the introduction of infectious diseases through indirect contacts. The second network was less fragmented than the first one, a giant SCC being detected. The topology of network communities also varied with modelling assumptions: in the first approach, a huge geographically dispersed community was found, whereas the second model highlighted several small geographically clustered communities. These results underline the relevance of developing network models corresponding to pathogen features (e.g. their transmission route), and the need to target specific types of holdings/areas for surveillance depending on the epidemiological context.
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Affiliation(s)
- Morgane Salines
- ANSES-Ploufragan-Plouzané Laboratory, Ploufragan, France
- Université Bretagne-Loire, Rennes, France
| | - Mathieu Andraud
- ANSES-Ploufragan-Plouzané Laboratory, Ploufragan, France
- Université Bretagne-Loire, Rennes, France
| | - Nicolas Rose
- ANSES-Ploufragan-Plouzané Laboratory, Ploufragan, France
- Université Bretagne-Loire, Rennes, France
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25
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Correia-Gomes C, Henry MK, Auty HK, Gunn GJ. Exploring the role of small-scale livestock keepers for national biosecurity—The pig case. Prev Vet Med 2017; 145:7-15. [DOI: 10.1016/j.prevetmed.2017.06.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 06/12/2017] [Accepted: 06/12/2017] [Indexed: 11/29/2022]
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26
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Vergne T, Chen-Fu C, Li S, Cappelle J, Edwards J, Martin V, Pfeiffer DU, Fusheng G, Roger FL. Pig empire under infectious threat: risk of African swine fever introduction into the People's Republic of China. Vet Rec 2017; 181:117. [PMID: 28754737 DOI: 10.1136/vr.103950] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 05/30/2017] [Accepted: 06/12/2017] [Indexed: 11/04/2022]
Abstract
Pig production and pork consumption are very important to the People's Republic of China for both economic and cultural reasons. The incursion and spread of a disease such as African swine fever (ASF), which emerged in Eastern Europe in 2007, could have devastating socioeconomic consequences for both the Chinese and the global pig industry. The Chinese government consequently attributes a very high priority to ASF and is actively seeking to improve its preparedness. This paper discusses different drivers and pathways of potential emergence of ASF in China in light of the country's specificities, including international movements of people, pigs and pig products, swill feeding practices and wild boar populations. It suggests that effective ASF risk management in China will require a comprehensive and integrated approach linking science and policy and will need to involve all relevant stakeholders to develop realistic policies.
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Affiliation(s)
- Timothée Vergne
- Department of Veterinary Clinical Sciences, Royal Veterinary College, Hatfield, UK
| | - Cao Chen-Fu
- Shenzhen Entry Exit Inspection and Quarantine Bureau, Shenzhen, China
| | - Shuo Li
- Food and Agriculture Organization of the United Nations, Beijing, China.,China Animal Disease Control Center, Beijing, China
| | - Julien Cappelle
- Animal, Health, Territories, Risks and Ecosystems, CIRAD, Montpellier, France
| | - John Edwards
- School of Veterinary and Life Sciences, Murdoch University, Perth, Australia
| | - Vincent Martin
- Food and Agriculture Organization of the United Nations, Beijing, China
| | - Dirk Udo Pfeiffer
- Department of Veterinary Clinical Sciences, Royal Veterinary College, Hatfield, UK.,College of Veterinary Medicine & Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Guo Fusheng
- Food and Agriculture Organization of the United Nations, Beijing, China
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27
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Schulz J, Boklund A, Halasa THB, Toft N, Lentz HHK. Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark. PLoS One 2017; 12:e0179915. [PMID: 28662077 PMCID: PMC5491064 DOI: 10.1371/journal.pone.0179915] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 06/06/2017] [Indexed: 12/02/2022] Open
Abstract
Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60–90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network measurements needs to be adapted. Using these adapted values for loyalty and contact chains might help to identify holdings with high potential of spreading diseases and thus limit the outbreak size or support control or eradication of the considered pathogen.
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Affiliation(s)
- Jana Schulz
- Technical University of Denmark, National Veterinary Institute, Kgs. Lyngby, Denmark
- * E-mail:
| | - Anette Boklund
- Technical University of Denmark, National Veterinary Institute, Kgs. Lyngby, Denmark
| | - Tariq H. B. Halasa
- Technical University of Denmark, National Veterinary Institute, Kgs. Lyngby, Denmark
| | - Nils Toft
- Technical University of Denmark, National Veterinary Institute, Kgs. Lyngby, Denmark
| | - Hartmut H. K. Lentz
- Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Epidemiology, Greifswald, Insel Riems, Germany
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28
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Kukielka EA, Martínez-López B, Beltrán-Alcrudo D. Modeling the live-pig trade network in Georgia: Implications for disease prevention and control. PLoS One 2017; 12:e0178904. [PMID: 28599000 PMCID: PMC5466301 DOI: 10.1371/journal.pone.0178904] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 05/19/2017] [Indexed: 11/18/2022] Open
Abstract
Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant.
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Affiliation(s)
- Esther Andrea Kukielka
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, California, United States of America
- * E-mail:
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, California, United States of America
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29
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Motta P, Porphyre T, Handel I, Hamman SM, Ngu Ngwa V, Tanya V, Morgan K, Christley R, Bronsvoort BMD. Implications of the cattle trade network in Cameroon for regional disease prevention and control. Sci Rep 2017; 7:43932. [PMID: 28266589 PMCID: PMC5339720 DOI: 10.1038/srep43932] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/31/2017] [Indexed: 11/09/2022] Open
Abstract
Movement of live animals is a major risk factor for the spread of livestock diseases and zoonotic infections. Understanding contact patterns is key to informing cost-effective surveillance and control strategies. In West and Central Africa some of the most rapid urbanization globally is expected to increase the demand for animal-source foods and the need for safer and more efficient animal production. Livestock trading points represent a strategic contact node in the dissemination of multiple pathogens. From October 2014 to May 2015 official transaction records were collected and a questionnaire-based survey was carried out in cattle markets throughout Western and Central-Northern Cameroon. The data were used to analyse the cattle trade network including a total of 127 livestock markets within Cameroon and five neighboring countries. This study explores for the first time the influence of animal trade on infectious disease spread in the region. The investigations showed that national borders do not present a barrier against pathogen dissemination and that non-neighbouring countries are epidemiologically connected, highlighting the importance of a regional approach to disease surveillance, prevention and control. Furthermore, these findings provide evidence for the benefit of strategic risk-based approaches for disease monitoring, surveillance and control, as well as for communication and training purposes through targeting key regions, highly connected livestock markets and central trading links.
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Affiliation(s)
- Paolo Motta
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
- Royal (Dick) School of Veterinary Science, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
| | - Thibaud Porphyre
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
| | - Ian Handel
- Royal (Dick) School of Veterinary Science, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
| | - Saidou M. Hamman
- Institute of Agricultural Research for Development, Regional Centre of Wakwa, Ngaoundere, B.P. 454, Cameroon
| | - Victor Ngu Ngwa
- School of Veterinary Medicine and Sciences, University of Ngaoundere, Ngaoundere, B.P. 454, Cameroon
| | - Vincent Tanya
- Cameroon Academy of Sciences, Yaound´e, B.P. 1457, Cameroon
| | - Kenton Morgan
- Institute of Ageing and Chronic Diseases, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - Rob Christley
- Institute of Infection and Global Health, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, L69 3BX, United Kingdom
| | - Barend M. deC. Bronsvoort
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
- Royal (Dick) School of Veterinary Science, University of Edinburgh, Easter Bush Campus, EH25 9RG, United Kingdom
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30
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Lee K, Polson D, Lowe E, Main R, Holtkamp D, Martínez-López B. Unraveling the contact patterns and network structure of pig shipments in the United States and its association with porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks. Prev Vet Med 2017; 138:113-123. [PMID: 28237226 DOI: 10.1016/j.prevetmed.2017.02.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 01/31/2017] [Accepted: 02/01/2017] [Indexed: 10/20/2022]
Abstract
The analysis of the pork value chain is becoming key to understanding the risk of infectious disease dissemination in the swine industry. In this study, we used social network analysis to characterize the swine shipment network structure and properties in a typical multisite swine production system in the US. We also aimed to evaluate the association between network properties and porcine respiratory and reproductive syndrome virus (PRRSV) transmission between production sites. We analyzed the 109,868 swine shipments transporting over 93 million swine between more than 500 production sites from 2012 to 2014. A total of 248 PRRSV positive occurrences were reported from 79 production sites during those 3 years. The temporal dynamics of swine shipments was evaluated by computing network properties in one-month and three-month networks. The association of PRRS occurrence in sow farms with centrality properties from one-month and three-month networks was assessed by using the multilevel logistic regression. All monthly networks showed a scale-free network topology with positive degree assortativity. The regression model revealed that out-degree centrality had a negative association with PRRS occurrence in sow farms in both one-month and three-month networks [OR=0.79 (95% CI, 0.63-0.99) in one-month network and 0.56 (95% CI, 0.36, 0.88) in three-month network] and in-closeness centrality model was positively associated with PRRS occurrence in sow farms in the three-month network [OR=2.45 (95% CI, 1.14-5.26)]. We also describe how the occurrence of porcine epidemic diarrheac (PED) outbreaks severely affected the network structure as well as the PRRS occurrence reports and its association with centrality measures in sow farms. The structure of the swine shipment network and the connectivity between production sites influenced on the PRRSV transmission. The use of network topology and characteristics combining with spatial analysis based on fine scale geographical location of production sites will be useful to inform the design of more cost-efficient, risk-based surveillance and control measures for PRRSV as well as other diseases in the US swine industry.
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Affiliation(s)
- Kyuyoung Lee
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA, USA.
| | - Dale Polson
- Boehringer - Ingelheim Vetmedica, Inc., St. Joseph, MO, USA
| | - Erin Lowe
- Boehringer - Ingelheim Vetmedica, Inc., St. Joseph, MO, USA
| | - Rodger Main
- Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Derald Holtkamp
- Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA, USA
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31
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Guinat C, Relun A, Wall B, Morris A, Dixon L, Pfeiffer DU. Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies. Sci Rep 2016; 6:28429. [PMID: 27357836 PMCID: PMC4928095 DOI: 10.1038/srep28429] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 04/22/2016] [Indexed: 11/09/2022] Open
Abstract
An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)'s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry. The findings also provide suggestions for how risk-based surveillance strategies could be optimised in the country by taking account of highly connected holdings, geographical regions and time periods with the greatest number of movements and pigs moved, as these are likely to be at higher risk for disease introduction. This study is also the first attempt to identify trade communities in the country, information which could be used to facilitate the pig trade and maintain disease-free status across the country in the event of an outbreak.
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Affiliation(s)
- C. Guinat
- Department of Production and Population Health, Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, United Kingdom
- The Pirbright Institute, Ash Road, Pirbright, Surrey, GU24 0NF, United Kingdom
| | - A. Relun
- Centre de coopération international en recherche agronomique pour le développement (CIRAD), UPR AGIRs, Campus international de Baillarguet, F-34398 Montpellier, France
| | - B. Wall
- Department of Production and Population Health, Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, United Kingdom
| | - A. Morris
- Department of Production and Population Health, Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, United Kingdom
- Department of Epidemiological Sciences, Animal and Plant Health Agency (APHA) Weybridge, Woodham Lane, Addlestone, Surrey, KT15 3NB, United Kingdom
| | - L. Dixon
- The Pirbright Institute, Ash Road, Pirbright, Surrey, GU24 0NF, United Kingdom
| | - D. U. Pfeiffer
- Department of Production and Population Health, Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, United Kingdom
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