Association between the swine production areas and the human population in Pinar del Rio province, Cuba

Research (Published online: 18-07-2017)

6. Association between the swine production areas and the human population in Pinar del Rio province, Cuba

Osvaldo Fonseca, Kleber Regis Santoro, Pastor Alfonso, Joel Ayala, Maria Antonia Abeledo, Octavio Fernandez, Yosdany Centelles, Damarys de las Nieves Montano and Maria Irian Percedo

International Journal of One Health, 3: 36-41

Osvaldo Fonseca: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Kleber Regis Santoro: Postgraduate Program in Biometrics and Applied Statistics (PPGBEA), Federal Rural University of Pernambuco (UFRPE), Rua Manuel de Medeiros, s/n - Dois Irmaos, Recife, PE 52171-900, Brazil.

Pastor Alfonso: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Joel Ayala: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Maria Antonia Abeledo: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Octavio Fernandez: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Yosdany Centelles: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Damarys de las Nieves Montano: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

Maria Irian Percedo: Epidemiology Group, Department of Microbiology-Epidemiology, National Center for Animal and Plant Health (OIE Collaborating Center for Disaster Risk Reduction in Animal Health), Carretera de Jamaica y Autopista Nacional, San Jose de las Lajas, CP 32700, Mayabeque, Cuba.

doi: 10.14202/IJOH.2017.36-41

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Article history: Received: 22-05-2017, Accepted: 24-06-2017, Published online: 18-07-2017

Corresponding author: Osvaldo Fonseca

E-mail: osvaldo820601@gmail.com

Citation: Fonseca O, Santoro KR, Alfonso P, Ayala J, Abeledo MA, Fernandez O, Centelles Y, Montano DN, Percedo MI. Association between the swine production areas and the human population in Pinar del Rio province, Cuba. Int J One Health 2017;3:36-41.

Abstract

Aim: The aim of this study was to demonstrate the association between high human population density and high pig production in the province of Pinar del Rio, Cuba.

Materials and Methods: Records on pig movements at the district level in Pinar del Rio province from July 2010 to December 2012 were used in the study. A network analysis was carried out considering districts, as nodes, and movements of pigs between them represented the edges. The in-degree parameter was calculated using R 3.1.3 software. Graphical representation of the network was done with Gephi 0.8.2, and ArcGIS 10.2. was used for the spatial analysis to detect clusters by the Getis-Ord Gi* method and visualize maps as well.

Results: Significant spatial clusters of high values (hot spots) and low values (cold spots) of in-degree were identified. A cluster of high values was located in the central area of the province, and a cluster of low values involving municipalities of the Western zone was detected. Logistic regression demonstrated that a higher human population density per district was associated (odds ratio=16.020, 95% confidence interval: 1.692-151.682, p=0.016) with areas of high pork production.

Conclusion: Hot spot of swine production in Pinar del Rio is associated with human densely populated districts, which may suppose a risk of spillover of pathogens able to infect animals and humans. These results can be considered in strategy planning in terms of pork production increases and improvements of sanitary, commercial, and economic policies by decision-makers.

Keywords: cluster, Getis-Ord, logistic regression, network analysis, swine.

References

1. Thrusfield M. Veterinary Epidemiology. Canada: Elsevier; 2013.

2. Puerta JL. Network analysis and medicine: A new perspective. Med Clin (Barc) 2013;140:273-7. [Crossref] [PubMed]

3. Pfeiffer D, Robinson T, Stevenson M, Stevens K, Rogers D, Clements A, et al. Spatial Analysis in Epidemiology. New York, Italia: Oxford University Press, FAO; 2008. [Crossref]

4. Benschop J. Epidemiological Investigations of Surveillance Strategies of Zoonotic Salmonella: A Dissertation Presented in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy at Massey University; 2009.

5. Gautam R, Guptill LF, Wu CC, Potter A, Moore GE. Spatial and spatio-temporal clustering of overall and serovar-specific Leptospira microscopic agglutination test (MAT) seropositivity among dogs in the United States from 2000 through 2007. Prev Vet Med 2010;96:122-31. [Crossref] [PubMed]

6. De Nardi M, Hill A, Dobschuetz S, Munoz O, Kosmider R, Dewe T, et al. Development of a Risk Assessment Methodological Framework for Potentially Pandemic Influenza Strains (FLURISK). EFSA Supporting Publications 2014; 11(5).

7. Uddin Khan S, Atanasova KR, Krueger WS, Ramirez A, Gray GC. Epidemiology, geographical distribution, and economic consequences of swine zoonoses: A narrative review. Emerg Microbes Infect 2013;2:e92. [Crossref] [PubMed] [PMC]

8. Fonseca O. Caracterizacion Espaciotemporal y Factores de Riesgo del Comportamiento Endemico de la Peste Porcina Clasica en Cuba: Thesis Submitted for Doctor of Philosophy. Cuba: Agricultural University of Havana; 2016.

9. Martinez-Lopez B, Perez AM, Sanchez-Vizcaino JM. Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain. Prev Vet Med 2009;91:29-38. [Crossref] [PubMed]

10. ONEI. Oficina Nacional de Estadistica e Informacion. Republica de Cuba; 2015. Available from: http://www.one.cu. Accessed on 10-12-2015.

11. R_Development_CoreTeam. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2012. Available from: . Accessed on 06-15-2015.

12. Gephi_Consortium. Gephi. Computer Program (Version 08 2 Beta). Vol. 14; 2014. Available from: https://www.gephi.org . Accessed on 03-21-2015.

13. ESRI. ArcGIS E. Release 10.2. Redlands, CA: ESRI; 2011.

14. Vidal XH, Barbeito GN, Perez MI, Lengua OJ, Fernandez EV, Hernandez RM, et al. Epidat: Programa Para Analisis Epidemiologico de Datos. Conselleria de Sanidade. 42nd ed. Espa-a, Colombia: Organizacion Panamericana de la Salud (OPS-OMS); Universidad CES, Xunta de Galicia; 2016.

15. Noremark M, Hakansson N, Lewerin SS, Lindberg A, Jonsson A. Network analysis of cattle and pig movements in Sweden: Measures relevant for disease control and risk based surveillance. Prev Vet Med 2011;99:78-90. [Crossref] [PubMed]

16. Molia S, Boly IA, Duboz R, Coulibaly B, Guitian J, Grosbois V, et al. Live bird markets characterization and trading network analysis in Mali: Implications for the surveillance and control of avian influenza and Newcastle disease. Acta Trop 2016;155:77-88. [Crossref] [PubMed]

17. Smith RP, Cook AJ, Christley RM. Descriptive and social network analysis of pig transport data recorded by quality assured pig farms in the UK. Prev Vet Med 2013;108:167-77. [Crossref] [PubMed]

18. Dorjee S, Revie CW, Poljak Z, McNab WB, Sanchez J. Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management. Prev Vet Med 2013;112:118-27. [Crossref] [PubMed]

19. Frossling J, Ohlson A, Bjorkman C, Hakansson N, Noremark M. Application of network analysis parameters in risk-based surveillance-examples based on cattle trade data and bovine infections in Sweden. Prev Vet Med 2012;105:202-8. [Crossref] [PubMed]

20. Harun SM, Ogneva-Himmelberger Y. Distribution of industrial farms in the United States and socioeconomic, health, and environmental characteristics of counties. Geogr J 2013;2013:1-12. [Crossref]

21. Smith C, Skelly C, Kung N, Roberts B, Field H. Flying-fox species density - A spatial risk factor for Hendra virus infection in horses in eastern Australia. PLoS One 2014;9:e99965. [Crossref] [PubMed] [PMC]

22. Premashthira S, Salman MD, Hill AE, Reich RM, Wagner BA. Epidemiological simulation modeling and spatial analysis for foot-and-mouth disease control strategies: A comprehensive review. Anim Health Res Rev 2011;12:225-34. [Crossref] [PubMed]

23. Firestone SM, Ward MP, Christley RM, Dhand NK. The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis. Prev Vet Med 2011;102:185-95. [Crossref] [PubMed]

24. Firestone SM, Christley RM, Ward MP, Dhand NK. Adding the spatial dimension to the social network analysis of an epidemic: Investigation of the 2007 outbreak of equine influenza in Australia. Prev Vet Med 2012;106:123-35. [Crossref] [PubMed]

25. Ellis F, Sumberg J. Food production, urban areas and policy responses. World Dev 1998;26:213-25. [Crossref]

26. Martinez-Lopez B, Perez AM, De la Torre A, Rodriguez JM. Quantitative risk assessment of foot-and-mouth disease introduction into Spain via importation of live animals. Prev Vet Med 2008;86:43-56. [Crossref] [PubMed]

27. Bigras-Poulin M, Thompson RA, Chriel M, Mortensen S, Greiner M. Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. Prev Vet Med 2006;76:11-39. [Crossref] [PubMed]

28. Taylor LH, Latham SM, Woolhouse ME. Risk factors for human disease emergence. Philos Trans R Soc Lond B Biol Sci 2001;356:983-9. [Crossref] [PubMed] [PMC]

29. Noremark M. Infection through the Farm Gate: Studies on Movements of Livestock and On-Farm Biosecurity. Uppsala: Department of Clinical Sciences, Swedish University of Agricultural Sciences; 2010.

30. Postel A, Schmeiser S, Perera CL, Rodriguez LJ, Frias-Lepoureau MT, Becher P. Classical swine fever virus isolates from Cuba form a new subgenotype 1.4. Vet Microbiol 2013;161:334-8. [Crossref] [PubMed]

31. Moennig V, Floegel-Niesmann G, Greiser-Wilke I. Clinical signs and epidemiology of classical swine fever: A review of new knowledge. Vet J 2003;165:11-20. [Crossref]

32. Martinez-Lopez B, Alexandrov T, Mur L, Sanchez-Vizcaino F, Sanchez-Vizcaino JM. Evaluation of the spatial patterns and risk factors, including backyard pigs, for classical swine fever occurrence in Bulgaria using a Bayesian model. Geospat Health 2014;8:489-501. [Crossref] [PubMed]

33. Mur Gil L. Nuevas Estrategias Para la Prevencion y Control de la Peste Porcina Africana. Espa-a: Universidad Complutense de Madrid; 2015.