FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Transboundary and emerging diseases2022; 69(5); e2757-e2768; doi: 10.1111/tbed.14627

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.

Abstract: Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.
© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.
Get Full Text
Publication Date: 2022-06-25 PubMed ID: 35694801PubMed Central: PMC9796646DOI: 10.1111/tbed.14627Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research uses spatial statistics and social network analysis to understand animal disease transmission and identify high-risk areas, using data of equine infectious anemia virus (EIAV) outbreaks from 2015 to 2017. The results help in informed decision-making regarding the issuance of animal movement permits and reducing infection risk.

Methods Used in the Study

  • The researchers used spatial statistics and social network analysis to understand disease transmission and areas of risk. This approach differs from traditional methods that focus on blocking transmission pathways and investigating contact history.
  • The study is based on the outbreak data of EIAV, along with information about between-farm horse movements, collected between 2015 and 2017.
  • The team connected the EIAV occurrence and horse movements with climate variables that influence local disease spread.
  • A spatially explicit model was developed to illustrate the impact of changing climate variables on EIAV distribution across different locations (non-stationarity).

Key Findings of the Research

  • The study identified specific locations where incoming animal movements are more prone to result in EIAV infections and disease spread.
  • Different risk zones were determined: 56 municipalities (11.3%) were classified as having a high spatial risk, 48 (9.66%) with a medium risk, and 393 (79.1%) with a low risk.
  • Most animal movements occurred between low-risk zones, accounting for 68.68% of all movements. Conversely, 9.48% were within high-risk areas, 6.20% within medium-risk areas, and a minor 5.37% of incoming animals in low-risk areas were from high-risk zones. Within the high-risk zones, 4.91% of the animals originated from low- and medium-risk areas.

Conclusions and Implications

  • The results show that evaluating animal movements in conjunction with spatial risk mapping can lead to better decision making around animal movement permits, potentially reducing the risk of disease reintroduction in low-risk zones.
  • The new approach can improve the effectiveness of disease surveillance activities and inform disease prevention efforts, especially in areas where livestock movement is a key factor in disease transmission.

Cite This Article

APA
Cardenas NC, Sanchez F, Lopes FPN, Machado G. (2022). Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance. Transbound Emerg Dis, 69(5), e2757-e2768. https://doi.org/10.1111/tbed.14627

Publication

Transboundary and emerging diseases
ISSN: 1865-1682
NlmUniqueID: 101319538
Country: Germany
Language: English
Volume: 69
Issue: 5
Pages: e2757-e2768

Researcher Affiliations

Cardenas, Nicolas C
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Sanchez, Felipe
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina, USA.
Lopes, Francisco P N
  • Departamento de Defesa Agropecuária, Secretaria da Agricultura, Pecuária e Desenvolvimento Rural (SEAPDR), Porto Alegre, Brazil.
Machado, Gustavo
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.

MeSH Terms

  • Animals
  • Disease Outbreaks / prevention & control
  • Disease Outbreaks / veterinary
  • Equine Infectious Anemia / epidemiology
  • Farms
  • Horse Diseases / epidemiology
  • Horses
  • Infectious Anemia Virus, Equine
  • Social Network Analysis

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 90 references
  1. Bachl E, Lindgren F F, Borchers D L, Illian J B. inlabru: An {R} package for Bayesian spatial modelling from ecological survey data. Methods in Ecology and Evolution 10, 760–766.
    doi: 10.1111/2041-210X.13168google scholar: lookup
  2. Bakka H, Rue H, Fuglstad G, Riebler A, Bolin D, Illian J, Krainski E, Simpson D, Lindgren F. Spatial modeling with R‐INLA: A review. WIREs Computational Statistics 10, e1443.
    doi: 10.1002/wics.1443google scholar: lookup
  3. Bakka H, Vanhatalo J, Illian J B, Simpson D, Rue H. Non‐stationary Gaussian models with physical barriers. Spatail Statistics 29, 268–288.
    doi: 10.1016/j.spasta.2019.01.002google scholar: lookup
  4. Barros AT, Foil LD. The influence of distance on movement of tabanids (Diptera: Tabanidae) between horses.. Vet Parasitol 2007 Mar 31;144(3-4):380-4.
    doi: 10.1016/j.vetpar.2006.09.041pubmed: 17112669google scholar: lookup
  5. Barzoni C S, Nogueira D M P, Marques G D, Diehl G N, Pellegrini D d C P, Brum M C S. Equine infectious anemia in the western region of Rio Grande do Sul, Brazil. Ciência Rural 48, 1–8.
    doi: 10.1590/0103-8478cr20170809google scholar: lookup
  6. Battle KE, Lucas TCD, Nguyen M, Howes RE, Nandi AK, Twohig KA, Pfeffer DA, Cameron E, Rao PC, Casey D, Gibson HS, Rozier JA, Dalrymple U, Keddie SH, Collins EL, Harris JR, Guerra CA, Thorn MP, Bisanzio D, Fullman N, Huynh CK, Kulikoff X, Kutz MJ, Lopez AD, Mokdad AH, Naghavi M, Nguyen G, Shackelford KA, Vos T, Wang H, Lim SS, Murray CJL, Price RN, Baird JK, Smith DL, Bhatt S, Weiss DJ, Hay SI, Gething PW. Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000-17: a spatial and temporal modelling study.. Lancet 2019 Jul 27;394(10195):332-343.
    doi: 10.1016/S0140-6736(19)31096-7pmc: PMC6675736pubmed: 31229233google scholar: lookup
  7. Blahó M, Egri A, Száz D, Kriska G, Akesson S, Horváth G. Stripes disrupt odour attractiveness to biting horseflies: battle between ammonia, CO₂, and colour pattern for dominance in the sensory systems of host-seeking tabanids.. Physiol Behav 2013 Jul 2;119:168-74.
    doi: 10.1016/j.physbeh.2013.06.013pubmed: 23810990google scholar: lookup
  8. Baylis M, Bouayoune H, Touti J, El Hasnaoui H. Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco.. Med Vet Entomol 1998 Jul;12(3):255-66.
    doi: 10.1046/j.1365-2915.1998.00109.xpubmed: 9737597google scholar: lookup
  9. Bell O, Jones ME, Cunningham CX, Ruiz-Aravena M, Hamilton DG, Comte S, Hamede RK, Bearhop S, McDonald RA. Isotopic niche variation in Tasmanian devils Sarcophilus harrisii with progression of devil facial tumor disease.. Ecol Evol 2021 Jun;11(12):8038-8053.
    doi: 10.1002/ece3.7636pmc: PMC8216929pubmed: 34188870google scholar: lookup
  10. Bivand R, Rundel C. Rgeos: Interface to Geometry Engine ‐ Open Source ('GEOS’). CRAN .
  11. Björnham O, Sigg R, Burman J. Multilevel model for airborne transmission of foot-and-mouth disease applied to Swedish livestock.. PLoS One 2020;15(5):e0232489.
    doi: 10.1371/journal.pone.0232489pmc: PMC7250458pubmed: 32453749google scholar: lookup
  12. Bolin D, Kirchner K. The rational SPDE approach for gaussian random fields with general smoothness. Journal of Computational and Graphical Statistics 29, 274–285.
    doi: 10.1080/10618600.2019.1665537google scholar: lookup
  13. Brin S, Page L. The anatomy of a large‐scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107–117.
    doi: 10.1016/S0169-7552(98)00110-Xgoogle scholar: lookup
  14. Büttner K, Krieter J. Epidemic spreading in a weighted pig trade network.. Prev Vet Med 2021 Mar;188:105280.
    doi: 10.1016/j.prevetmed.2021.105280pubmed: 33548903google scholar: lookup
  15. Cárdenas NC, Galvis JOA, Farinati AA, Grisi-Filho JHH, Diehl GN, Machado G. Burkholderia mallei: The dynamics of networks and disease transmission.. Transbound Emerg Dis 2019 Mar;66(2):715-728.
    doi: 10.1111/tbed.13071pubmed: 30427593google scholar: lookup
  16. Cardenas NC, Pozo P, Lopes FPN, Grisi-Filho JHH, Alvarez J. Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil.. Microorganisms 2021 Jan 22;9(2).
    doi: 10.3390/microorganisms9020227pmc: PMC7912437pubmed: 33499225google scholar: lookup
  17. Cardenas NC, VanderWaal K, Veloso FP, Galvis JOA, Amaku M, Grisi-Filho JHH. Spatio-temporal network analysis of pig trade to inform the design of risk-based disease surveillance.. Prev Vet Med 2021 Apr;189:105314.
    doi: 10.1016/j.prevetmed.2021.105314pubmed: 33689961google scholar: lookup
  18. Chen S, Lanzas C. Distinction and connection between contact network, social network, and disease transmission network.. Prev Vet Med 2016 Sep 1;131:8-11.
    doi: 10.1016/j.prevetmed.2016.07.002pmc: PMC5490979pubmed: 27544246google scholar: lookup
  19. Coggins L, Norcross NL, Nusbaum SR. Diagnosis of equine infectious anemia by immunodiffusion test.. Am J Vet Res 1972 Jan;33(1):11-8.
    pubmed: 4333633
  20. Core Team R. R: A Language and Environment for Statistical Computing. CRAN .
  21. Duncan AJ, Reeves A, Gunn GJ, Humphry RW. Quantifying changes in the British cattle movement network.. Prev Vet Med 2022 Jan;198:105524.
    doi: 10.1016/j.prevetmed.2021.105524pubmed: 34775127google scholar: lookup
  22. Dunnington D. Ggspatial: Spatial Data Framework for Ggplot2. CRAN .
  23. Elias T, Virgilio K G-R, Haakon B, Amanda L, Daniela C-C, Daniel S, Finn L, Håvard R. Advanced spatial modeling with stochastic partial differential equations using R and INLA. Chapman & Hall/CRC Press .
  24. Emch M, Root ED, Giebultowicz S, Ali M, Perez-Heydrich C, Yunus M. Integration of Spatial and Social Network Analysis in Disease Transmission Studies.. Ann Assoc Am Geogr 2012;105(5):1004-1015.
    doi: 10.1080/00045608.2012.671129pmc: PMC3806718pubmed: 24163443google scholar: lookup
  25. Ezanno P, Andraud M, Beaunée G, Hoch T, Krebs S, Rault A, Touzeau S, Vergu E, Widgren S. How mechanistic modelling supports decision making for the control of enzootic infectious diseases.. Epidemics 2020 Sep;32:100398.
    doi: 10.1016/j.epidem.2020.100398pubmed: 32622313google scholar: lookup
  26. Fèvre EM, Bronsvoort BM, Hamilton KA, Cleaveland S. Animal movements and the spread of infectious diseases.. Trends Microbiol 2006 Mar;14(3):125-31.
    doi: 10.1016/j.tim.2006.01.004pmc: PMC7119069pubmed: 16460942google scholar: lookup
  27. 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 Sep 15;106(2):123-35.
    doi: 10.1016/j.prevetmed.2012.01.020pmc: PMC7126086pubmed: 22365721google scholar: lookup
  28. 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 Dec 1;102(3):185-95.
    doi: 10.1016/j.prevetmed.2011.07.006pubmed: 21852007google scholar: lookup
  29. Freeman L C. Centrality in social networks conceptual clarification. Social Networks 1, 215–239.
    doi: 10.1016/0378-8733(78)90021-7google scholar: lookup
  30. Fuglstad G-A, Simpson D, Lindgren F, Rue H. Constructing priors that penalize the complexity of Gaussian random fields. Journal of the American Statistical Association 114, 445–452.
    doi: 10.1080/01621459.2017.1415907google scholar: lookup
  31. Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize Implements and enhances circular visualization in R.. Bioinformatics 2014 Oct;30(19):2811-2.
    pubmed: 24930139doi: 10.1093/bioinformatics/btu393google scholar: lookup
  32. 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 Jun 30;6:28429.
    doi: 10.1038/srep28429pmc: PMC4928095pubmed: 27357836google scholar: lookup
  33. Hemming-Schroeder E, Lo E, Salazar C, Puente S, Yan G. Landscape genetics: A toolbox for studying vector‐borne diseases. Frontiers in Ecology and Evolution 6, 21.
    doi: 10.3389/fevo.2018.00021google scholar: lookup
  34. Issel CJ, Foil LD. Studies on equine infectious anemia virus transmission by insects.. J Am Vet Med Assoc 1984 Feb 1;184(3):293-7.
    pubmed: 6321420
  35. Issel CJ, Foil LD. Equine infectious anaemia and mechanical transmission: man and the wee beasties.. Rev Sci Tech 2015 Aug;34(2):513-23.
    doi: 10.20506/rst.34.2.2376pubmed: 26601453google scholar: lookup
  36. Hijmans R J. Raster: Geographic Data Analysis and Modeling. CRAN .
  37. Jackson M O. Social and economic networks. Princeton University Press .
  38. Jones A E, Turner J, Caminade C, Heath A E, Wardeh M, Kluiters G, Diggle P J, Morse A P, Baylis M. Bluetongue risk under future climates. Nature Climate Change 9, 153–157.
    doi: 10.1038/s41558-018-0376-6google scholar: lookup
  39. nJRCn. Copernicus Global Land Service (CGLS), Copernicus Global Land Service. .
  40. Krcmar S. Seasonal abundance of horse flies (Diptera: Tabanidae) from two locations in eastern Croatia.. J Vector Ecol 2005 Dec;30(2):316-21.
    pubmed: 16599170
  41. Lentz HH, Koher A, Hövel P, Gethmann J, Sauter-Louis C, Selhorst T, Conraths FJ. Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany.. PLoS One 2016;11(5):e0155196.
    doi: 10.1371/journal.pone.0155196pmc: PMC4859575pubmed: 27152712google scholar: lookup
  42. Leroux C, Cadoré JL, Montelaro RC. Equine Infectious Anemia Virus (EIAV): what has HIV's country cousin got to tell us?. Vet Res 2004 Jul-Aug;35(4):485-512.
    doi: 10.1051/vetres:2004020pubmed: 15236678google scholar: lookup
  43. Lindgren F, Bolin D, Rue H. The SPDE approach for Gaussian and non‐Gaussian fields: 10 years and still running. Spatial Statistics 50, 100599.
    doi: 10.1016/j.spasta.2022.100599google scholar: lookup
  44. Lucas M, Krolow TK, Riet-Correa F, Barros ATM, Krüger RF, Saravia A, Miraballes C. Diversity and seasonality of horse flies (Diptera: Tabanidae) in Uruguay.. Sci Rep 2020 Jan 15;10(1):401.
    doi: 10.1038/s41598-019-57356-0pmc: PMC6962385pubmed: 31942013google scholar: lookup
  45. Machado G, Corbellini LG, Frias-De-Diego A, Dieh GN, Dos Santos DV, Jara M, de Freitas Costa E. Impact of changes of horse movement regulations on the risks of equine infectious anemia: A risk assessment approach.. Prev Vet Med 2021 May;190:105319.
    doi: 10.1016/j.prevetmed.2021.105319pubmed: 33713963google scholar: lookup
  46. Machado G, Galvis JA, Lopes FPN, Voges J, Medeiros AAR, Cárdenas NC. Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans.. Transbound Emerg Dis 2021 May;68(3):1663-1675.
    doi: 10.1111/tbed.13841pubmed: 32965771google scholar: lookup
  47. Machado G, Vilalta C, Recamonde-Mendoza M, Corzo C, Torremorell M, Perez A, VanderWaal K. Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods.. Sci Rep 2019 Jan 24;9(1):457.
    doi: 10.1038/s41598-018-36934-8pmc: PMC6345879pubmed: 30679594google scholar: lookup
  48. nMAPAn. IBGE ‐ Censo Agro 2017, IBGE ‐ Censo Agro 2017 [Online]. .
  49. nMAPAn. Manual de Legislação ‐ Saúde Animal [Online]. .
  50. Martínez-López B, Ivorra B, Fernández-Carrión E, Perez AM, Medel-Herrero A, Sánchez-Vizcaíno F, Gortázar C, Ramos AM, Sánchez-Vizcaíno JM. A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: the example of FMD in Peru.. Prev Vet Med 2014 Apr 1;114(1):47-63.
    doi: 10.1016/j.prevetmed.2014.01.013pubmed: 24485278google scholar: lookup
  51. Martínez-Minaya J, Cameletti M, Conesa D, Pennino M G. Species distribution modeling: A statistical review with focus in spatio‐temporal issues. Stochastic Environmental Research and Risk Assessment 32, 3227–3244.
    doi: 10.1007/s00477-018-1548-7google scholar: lookup
  52. Minervino AHH, Torres AC, Moreira TR, Vinholte BP, Sampaio BM, Bianchi D, Portela JM, Sarturi C, Marcili A, Barrêto Júnior RA, Gennari SM, Machado RZ. Factors associated with the prevalence of antibodies against Theileria equi in equids of Western Pará, Brazil.. Transbound Emerg Dis 2020 Jul;67 Suppl 2:100-105.
    doi: 10.1111/tbed.13268pubmed: 31286674google scholar: lookup
  53. Monnahan C C, Thorson J T, Kotwicki S, Lauffenburger N, Ianelli J N, Punt A E. Incorporating vertical distribution in index standardization accounts for spatiotemporal availability to acoustic and bottom trawl gear for semi‐pelagic species. Ices Journal of Marine Science 78, 1826–1839.
    doi: 10.1093/icesjms/fsab085google scholar: lookup
  54. Moraes Filho J, de Sousa A O, de Carvalho T R V, Labruna M B. Brazilian spotted fever serological investigation among equids at the Guarapiranga Dam area in the city of São Paulo, Brazil. Brazilian Journal of Veterinary Research and Animal Science 56, e158601.
    doi: 10.11606/issn.1678-4456.bjvras.2019.158601google scholar: lookup
  55. Mullens B A. Horse flies and deer flies (Tabanidae). Medical and veterinary entomology pp. 327–343.
  56. Napp S, Ciaravino G, Pérez de Val B, Casal J, Saéz JL, Alba A. Evaluation of the effectiveness of the surveillance system for tuberculosis in cattle in Spain.. Prev Vet Med 2019 Dec 1;173:104805.
    doi: 10.1016/j.prevetmed.2019.104805pubmed: 31715496google scholar: lookup
  57. Nogueira M F, Oliveira J M, Santos C J S, Petzold H V, Aguiar D M, Juliano R S, Reis J K P, Abreu U G P. Equine infectious anaemia in equids of Southern Pantanal, Brazil: Seroprevalence and evaluation of the adoption of a control programme. Pesquisa Veterinária Brasileira 37, 227–233.
    doi: 10.1590/s0100-736X2017000300005google scholar: lookup
  58. Notsu K, Wiratsudakul A, Mitoma S, El Daous H, Kaneko C, El-Khaiat HM, Norimine J, Sekiguchi S. Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis.. Pathogens 2020 Oct 28;9(11).
    doi: 10.3390/pathogens9110903pmc: PMC7693104pubmed: 33126749google scholar: lookup
  59. Oliveira FG, Cook RF, Naves JHF, Oliveira CHS, Diniz RS, Freitas FJC, Lima JM, Sakamoto SM, Leite RC, Issel CJ, Reis JKP. Equine infectious anemia prevalence in feral donkeys from Northeast Brazil.. Prev Vet Med 2017 May 1;140:30-37.
    doi: 10.1016/j.prevetmed.2017.02.015pubmed: 28460747google scholar: lookup
  60. Pebesma E. Simple features for R: Standardized support for spatial vector data. The R Journal 10, 439–446.
    doi: 10.32614/RJ-2018-009google scholar: lookup
  61. Pozo P, VanderWaal K, Grau A, de la Cruz ML, Nacar J, Bezos J, Perez A, Minguez O, Alvarez J. Analysis of the cattle movement network and its association with the risk of bovine tuberculosis at the farm level in Castilla y Leon, Spain.. Transbound Emerg Dis 2019 Jan;66(1):327-340.
    doi: 10.1111/tbed.13025pubmed: 30270505google scholar: lookup
  62. Prado Siqueira R. Brazilmaps: Brazilian Maps from Different Geographic Levels. CRAN .
  63. Prem K, Lau MSY, Tam CC, Ho MZJ, Ng LC, Cook AR. Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore-a spatio-temporal model.. J R Soc Interface 2019 Jun 28;16(155):20180604.
    doi: 10.1098/rsif.2018.0604pmc: PMC6597776pubmed: 31213175google scholar: lookup
  64. Redding DW, Lucas TCD, Blackburn TM, Jones KE. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.. PLoS One 2017;12(11):e0187602.
    doi: 10.1371/journal.pone.0187602pmc: PMC5708625pubmed: 29190296google scholar: lookup
  65. Resende CF, Santos AM, Cook RF, Victor RM, Câmara RJF, Gonçalves GP, Lima JG, Maciel E Silva AG, Leite RC, Dos Reis JKP. Low transmission rates of Equine infectious anemia virus (EIAV) in foals born to seropositive feral mares inhabiting the Amazon delta region despite climatic conditions supporting high insect vector populations.. BMC Vet Res 2022 Jul 22;18(1):286.
    pmc: PMC9306203pubmed: 35869474doi: 10.1186/s12917-022-03384-4google scholar: lookup
  66. Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society Series B Statistical Methodology 71, 319–392.
    doi: 10.1111/j.1467-9868.2008.00700.xgoogle scholar: lookup
  67. Sandrigo G, Martínez D E, Cipolini M F, Storani C A, Espasandin A G. Comportamiento de la técnica ELISA de competición en el diagnóstico de anemia infecciosa equina (AIE). Revista Veterinaria 32, 192.
    doi: 10.30972/vet.3225741google scholar: lookup
  68. Santos Baquero O. INLAOutputs: Process Selected Outputs from the “INLA” Package. CRAN .
  69. Sanz-Alonso D, Yang R. Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. .
    doi: 10.48550/ARXIV.2109.02777google scholar: lookup
  70. Schiller I, Waters WR, Vordermeier HM, Jemmi T, Welsh M, Keck N, Whelan A, Gormley E, Boschiroli ML, Moyen JL, Vela C, Cagiola M, Buddle BM, Palmer M, Thacker T, Oesch B. Bovine tuberculosis in Europe from the perspective of an officially tuberculosis free country: trade, surveillance and diagnostics.. Vet Microbiol 2011 Jul 5;151(1-2):153-9.
    doi: 10.1016/j.vetmic.2011.02.039pubmed: 21439740google scholar: lookup
  71. nSEAPAn. (17. June): Secretaria da Agricultura, Pecuária e Desenvolvimento Rural, Anemia Infecciosa Equina [Online]. .
  72. SEAPI. Secretaria Da Agricultura Pecuaria e Irrigação. .
  73. Shamoun-Baranes J, Alves J A, Bauer S, Dokter A M, Hüppop O, Koistinen J, Leijnse H, Liechti F, van Gasteren H, Chapman J W. Continental‐scale radar monitoring of the aerial movements of animals. Movement Ecology 2, 9.
    doi: 10.1186/2051-3933-2-9google scholar: lookup
  74. Simpson D, Rue H, Riebler A, Martins T G, Sørbye S H. Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science 32, 1–28.
    doi: 10.1214/16-STS576google scholar: lookup
  75. Sintayehu DW, Prins HH, Heitkönig IM, de Boer WF. Disease transmission in animal transfer networks.. Prev Vet Med 2017 Feb 1;137(Pt A):36-42.
    doi: 10.1016/j.prevetmed.2016.12.017pubmed: 28107879google scholar: lookup
  76. Siqueira R F D, Hansen V S, Martins M d F M, Leal M L D R, Bondan E F. West Nile fever virus infection in horses in São Paulo State, Brazil. Acta Scientiae Veterinariae 50, 1–6.
    doi: 10.22456/1679-9216.117796google scholar: lookup
  77. Spence KL, O'Sullivan TL, Poljak Z, Greer AL. Descriptive and network analyses of the equine contact network at an equestrian show in Ontario, Canada and implications for disease spread.. BMC Vet Res 2017 Jun 21;13(1):191.
    doi: 10.1186/s12917-017-1103-7pmc: PMC5480143pubmed: 28637457google scholar: lookup
  78. Spence KL, O'Sullivan TL, Poljak Z, Greer AL. Estimating the potential for disease spread in horses associated with an equestrian show in Ontario, Canada using an agent-based model.. Prev Vet Med 2018 Mar 1;151:21-28.
    doi: 10.1016/j.prevetmed.2017.12.013pubmed: 29496102google scholar: lookup
  79. Spence KL, O'Sullivan TL, Poljak Z, Greer AL. Descriptive analysis of horse movement networks during the 2015 equestrian season in Ontario, Canada.. PLoS One 2019;14(7):e0219771.
    doi: 10.1371/journal.pone.0219771pmc: PMC6622551pubmed: 31295312google scholar: lookup
  80. Squarzoni-Diaw C, Arsevska E, Kalthoum S, Hammami P, Cherni J, Daoudi A, Karim Laoufi M, Lezaar Y, Rachid K, Seck I, Ould Elmamy B, Yahya B, Dufour B, Hendrikx P, Cardinale E, Muñoz F, Lancelot R, Coste C. Using a participatory qualitative risk assessment to estimate the risk of introduction and spread of transboundary animal diseases in scarce-data environments: A Spatial Qualitative Risk Analysis applied to foot-and-mouth disease in Tunisia 2014-2019.. Transbound Emerg Dis 2021 Jul;68(4):1966-1978.
    doi: 10.1111/tbed.13920pubmed: 33174371google scholar: lookup
  81. Sultaire S M, Humphreys J M, Zuckerberg B, Pauli J N, Roloff G J. Spatial variation in bioclimatic relationships for a snow‐adapted species along a discontinuous southern range boundary. Journal of Biogeography 49, 66–78.
    doi: 10.1111/jbi.14279google scholar: lookup
  82. Tao Y, Probert WJM, Shea K, Runge MC, Lafferty K, Tildesley M, Ferrari M. Causes of delayed outbreak responses and their impacts on epidemic spread.. J R Soc Interface 2021 Mar;18(176):20200933.
    doi: 10.1098/rsif.2020.0933pmc: PMC8086880pubmed: 33653111google scholar: lookup
  83. Tigre DM, Brandão CF, de Paula FL, Chinalia FA, Campos GS, Sardi SI. Characterization of isolates of equine infectious anemia virus in Brazil.. Arch Virol 2017 Mar;162(3):873-877.
    doi: 10.1007/s00705-016-3172-5pubmed: 27896562google scholar: lookup
  84. van Niekerk J, Bakka H, Rue H, Schenk O. New frontiers in bayesian modeling using the INLA package in R. Journal of Statistical Software 100, 1–28.
    doi: 10.18637/jss.v100.i02google scholar: lookup
  85. van Niekerk J, Krainski E, Rustand D, Rue H. A new avenue for Bayesian inference with INLA. .
    doi: 10.48550/ARXIV.2204.06797google scholar: lookup
  86. Wasserman S, Faust K. Social network analysis: Methods and applications. Cambridge University Press, Cambridge .
  87. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks.. Nature 1998 Jun 4;393(6684):440-2.
    doi: 10.1038/30918pubmed: 9623998google scholar: lookup
  88. Wickham H, Averick M, Bryan J, Chang W, D'Agostino McGowan L, Franois R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T L, Miller E, Milton Bache S, Mller K, Ooms J, Robinson D, Seidel D P, Spinu V, Yutani H. Welcome to the tidyverse. Journal Open Source Software 4, 1686–1686.
    doi: 10.21105/joss.01686google scholar: lookup
  89. nworldclimn. WorldClim, Global climate and weather data [Online]. .
  90. Zhang N, Huang D, Wu W, Liu J, Liang F, Zhou B, Guan P. Animal brucellosis control or eradication programs worldwide: A systematic review of experiences and lessons learned.. Prev Vet Med 2018 Nov 15;160:105-115.
    doi: 10.1016/j.prevetmed.2018.10.002pubmed: 30388992google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,780 horse owners like you who receive our email updates on horse health and nutrition.