Application of network analysis and cluster analysis for better prevention and control of swine diseases in Argentina

Autor: Jerome N. Baron, María Natalia Aznar, Beatriz Martínez-López, Mariela Monterubbianesi
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Diseases Control
Swine
Transportation
Geographical locations
Animal Diseases
Análisis de Redes
0403 veterinary science
0302 clinical medicine
Risk Factors
Regional science
Medicine and Health Sciences
Cluster Analysis
Centrality
Animal Husbandry
Control de Enfermedades
Swine Diseases
Mammals
Multidisciplinary
Social network analysis (criminology)
Eukaryota
Agriculture
04 agricultural and veterinary sciences
Infectious Diseases
Veterinary Diseases
Vertebrates
Medicine
Network Analysis
Network analysis
Research Article
Computer and Information Sciences
Farms
Livestock
Infectious Disease Control
040301 veterinary sciences
Science
030231 tropical medicine
Argentina
Enfermedades de los Animales
Disease Prevention
Disease cluster
03 medical and health sciences
Spatio-Temporal Analysis
Betweenness centrality
Animals
Cerdo
Pseudorabies
Economic sector
Node (networking)
Organisms
Biology and Life Sciences
South America
Hierarchical clustering
Amniotes
Veterinary Science
Business
People and places
Prevención de Enfermedades
Zdroj: PLoS ONE, Vol 15, Iss 6, p e0234489 (2020)
PLoS ONE
PLoS ONE 15 (6) : e0234489 (2020)
INTA Digital (INTA)
Instituto Nacional de Tecnología Agropecuaria
instacron:INTA
ISSN: 1932-6203
Popis: 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. Instituto de Patobiología Fil: Baron, Jerome N. University of California Davis. School of Veterinary Medicine. Center for Animal Disease Modeling and Surveillance (CADMS). Department of Medicine and Epidemiology; Estados Unidos Fil: Aznar, Maria Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patobiología; Argentina Fil: Monterubbianesi, Mariela. Servicio Nacional de Sanidad y Calidad Agroalimentaria de la Republica Argentina (SENASA); Argentina Fil: Martínez-López, Beatriz. University of California Davis. School of Veterinary Medicine. Center for Animal Disease Modeling and Surveillance (CADMS). Department of Medicine and Epidemiology; Estados Unidos
Databáze: OpenAIRE
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