Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak
Autoři:
Joseph T. Hicks aff001; Dong-Hun Lee aff002; Venkata R. Duvuuri aff001; Mia Kim Torchetti aff003; David E. Swayne aff004; Justin Bahl aff001
Působiště autorů:
Center for Ecology of Infectious Diseases, Department of Infectious Diseases, Department of Ecology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
aff001; Department of Pathobiology and Veterinary Science, the University of Connecticut, Storrs, Connecticut, United States of America
aff002; U.S. Department of Agriculture, Ames, Iowa, United States of America
aff003; Exotic and Emerging Avian Viral Diseases Research Unit, U.S. National Poultry Research Center, Agricultural Research Service, U.S. Department of Agriculture, Athens, Georgia, United States of America
aff004; Duke-NUS Graduate Medical School, Singapore
aff005
Vyšlo v časopise:
Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak. PLoS Pathog 16(1): e32767. doi:10.1371/journal.ppat.1007857
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.ppat.1007857
Souhrn
The 2014–2015 highly pathogenic avian influenza (HPAI) H5NX outbreak represents the largest and most expensive HPAI outbreak in the United States to date. Despite extensive traditional and molecular epidemiological studies, factors associated with the spread of HPAI among midwestern poultry premises remain unclear. To better understand the dynamics of this outbreak, 182 full genome HPAI H5N2 sequences isolated from commercial layer chicken and turkey production premises were analyzed using evolutionary models able to accommodate epidemiological and geographic information. Epidemiological compartmental models embedded in a phylogenetic framework provided evidence that poultry type acted as a barrier to the transmission of virus among midwestern poultry farms. Furthermore, after initial introduction, the propagation of HPAI cases was self-sustainable within the commercial poultry industries. Discrete trait diffusion models indicated that within state viral transitions occurred more frequently than inter-state transitions. Distance and sample size were very strongly supported as associated with viral transition between county groups (Bayes Factor > 30.0). Together these findings indicate that the different types of midwestern poultry industries were not a single homogenous population, but rather, the outbreak was shaped by poultry industries and geographic factors.
Klíčová slova:
Birds – Farms – Chicken models – Chickens – Livestock – Poultry – Turkeys – Viral evolution
Zdroje
1. Lee YJ, Kang HM, Lee EK, Song BM, Jeong J, Kwon YK, et al. Novel reassortant influenza A(H5N8) viruses, South Korea, 2014. Emerg Infect Dis. 2014;20: 1087–1089. doi: 10.3201/eid2006.140233 24856098
2. Dalby AR, Iqbal M. The European and Japanese outbreaks of H5N8 derive from a single source population providing evidence for the dispersal along the long distance bird migratory flyways. PeerJ. 2015. p. e934. doi: 10.7717/peerj.934 25945320
3. Harder T, Maurer-Stroh S, Pohlmann A, Starick E, Höreth-Böntgen D, Albrecht K, et al. Influenza A(H5N8) Virus Similar to Strain in Korea Causing Highly Pathogenic Avian Influenza in Germany. Emerg Infect Dis. 2015;21: 860–863. doi: 10.3201/eid2105.141897 25897703
4. Ip HS, Torchetti MK, Crespo R, Kohrs P, DeBruyn P, Mansfield KG, et al. Novel Eurasian highly pathogenic avian influenza A H5 viruses in wild birds, Washington, USA, 2014. Emerg Infect Dis. 2015;21: 886–890. doi: 10.3201/eid2105.142020 25898265
5. Pasick J, Berhane Y, Joseph T, Bowes V, Hisanaga T, Handel K, et al. Reassortant highly pathogenic influenza A H5N2 virus containing gene segments related to Eurasian H5N8 in British Columbia, Canada, 2014. Sci Rep. 2015;5: 9484. doi: 10.1038/srep09484 25804829
6. Lee DH, Bahl J, Torchetti MK, Killian ML, Ip HS, DeLiberto TJ, et al. Highly Pathogenic Avian Influenza Viruses and Generation of Novel Reassortants, United States, 2014–2015. Emerg Infect Dis. 2016;22: 1283–1285. doi: 10.3201/eid2207.160048 27314845
7. APHIS U. Final Report for the 2014–2015 Outbreak of Highly Pathogenic Avian Influenza (HPAI) in the United States. 2016 Aug.
8. Johansson RC, Preston WP, Seitzinger AH. Government Spending to Control Highly Pathogenic Avian Influenza. Choices. 2016;31.
9. Swayne DE, Hill RE, Clifford J. Safe application of regionalization for trade in poultry and poultry products during highly pathogenic avian influenza outbreaks in the USA. Avian Pathol. Taylor & Francis; 2017;46: 125–130. doi: 10.1080/03079457.2016.1257775 27817200
10. Dargatz D, Beam A, Wainwright S, McCluskey B. Case Series of Turkey Farms from the H5N2 Highly Pathogenic Avian Influenza Outbreak in the United States During 2015. Avian Dis. 2016;60: 467–472. doi: 10.1637/11350-121715-Reg 27309289
11. Wells SJ, Kromm MM, VanBeusekom ET, Sorley EJ, Sundaram ME, VanderWaal K, et al. Epidemiologic Investigation of Highly Pathogenic H5N2 Avian Influenza Among Upper Midwest U.S. Turkey Farms, 2015. Avian Dis. 2017;61: 198–204. doi: 10.1637/11543-112816-Reg.1 28665726
12. Grear DA, Hall JS, Dusek RJ, Ip HS. Inferring epidemiologic dynamics from viral evolution: 2014–2015 Eurasian/North American highly pathogenic avian influenza viruses exceed transmission threshold, R0 = 1, in wild birds and poultry in North America. Evol Appl. 2017;11: 547–557. doi: 10.1111/eva.12576 29636805
13. Bonney PJ, Malladi S, Boender GJ, Weaver JT, Ssematimba A, Halvorson DA, et al. Spatial transmission of H5N2 highly pathogenic avian influenza between Minnesota poultry premises during the 2015 outbreak. PLoS One. 2018;13: e0204262. doi: 10.1371/journal.pone.0204262 30240402
14. Lee D-H, Torchetti MK, Hicks J, Killian ML, Bahl J, Pantin-Jackwood M, et al. Transmission dynamics of highly pathogenic avian influenzvirus a(H5Nx) clade 2.3.4.4, North America, 2014–2015. Emerg Infect Dis. 2018;24. doi: 10.3201/eid2410.171891 30226167
15. USDA APHIS. Epidemiologic and Other Analyses of HPAI-Affected Poultry Flocks: September 9, 2015 Report. 2015.
16. Lemey P, Rambaut A, Bedford T, Faria N, Bielejec F, Baele G, et al. Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2. PLOS Pathog. 2014;10: e1003932. doi: 10.1371/journal.ppat.1003932 24586153
17. Baele G A. MS, Rambaut A, Lemey P. Emerging Concepts of Data Integration in Pathogen Phylodynamics. Syst Biol. 2016;66: e65. doi: 10.1093/sysbio/syw054 28173504
18. Dellicour S, Rose R, Pybus OG. Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data. BMC Bioinformatics. BioMed Central; 2016;17: 82. doi: 10.1186/s12859-016-0924-x 26864798
19. Dellicour S, Vrancken B, Trovão NS, Fargette D, Lemey P. On the importance of negative controls in viral landscape phylogeography. Virus Evol. Narnia; 2018;4. doi: 10.1093/ve/vey023 30151241
20. Jacquot M, Nomikou K, Palmarini M, Mertens P, Biek R. Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference. Proc R Soc B Biol Sci. The Royal Society; 2017;284: 20170919. doi: 10.1098/rspb.2017.0919 29021180
21. P. DM, Murrell B, Golden M, Khoosal A, Muhire B. RDP4: Detection and analysis of recombination patterns in virus genomes. Virus Evol. 2015;1: vev003. doi: 10.1093/ve/vev003 27774277
22. Volz EM, Siveroni I. Bayesian phylodynamic inference with complex models. PLoS Comput Biol. 2018;14: e1006546. doi: 10.1371/journal.pcbi.1006546 30422979
23. Dudas G, Carvalho LM, Rambaut A, Bedford T. MERS-CoV spillover at the camel-human interface. eLife. 2018. doi: 10.7554/eLife.31257 29336306
24. Drummond AJ, Nicholls GK, Rodrigo AG, Solomon W. Estimating Mutation Parameters, Population History and Genealogy Simultaneously From Temporally Spaced Sequence Data. Genetics. 2002;161: 1307–1320. 12136032
25. Dearlove B, Wilson DJ. Coalescent inference for infectious disease: meta-analysis of hepatitis C. Philos Trans R Soc Lond B Biol Sci. 2013;368: 20120314. doi: 10.1098/rstb.2012.0314 23382432
26. Biek R, Drummond AJ, Poss M. A virus reveals population structure and recent demographic history of its carnivore host. Science. 2006;311: 538–541. doi: 10.1126/science.1121360 16439664
27. Möller S, du Plessis L, Stadler T. Impact of the tree prior on estimating clock rates during epidemic outbreaks. Proc Natl Acad Sci. 2018;115: 4200–4205. doi: 10.1073/pnas.1713314115 29610334
28. Streicker DG, Altizer SM, Velasco-Villa A, Rupprecht CE. Variable evolutionary routes to host establishment across repeated rabies virus host shifts among bats. Proc Natl Acad Sci U S A. 2012;109: 19715–20. doi: 10.1073/pnas.1203456109 23150575
29. Vaughan TG, Kühnert D, Popinga A, Welch D, Drummond AJ. Efficient Bayesian inference under the structured coalescent. Bioinformatics. 2014;30: 2272–2279. doi: 10.1093/bioinformatics/btu201 24753484
30. Aldunate F, Gámbaro F, Fajardo A, Soñora M, Cristina J. Evidence of increasing diversification of Zika virus strains isolated in the American continent. J Med Virol. John Wiley & Sons, Ltd; 2017;89: 2059–2063. doi: 10.1002/jmv.24910 28792064
31. Alkhamis MA, Perez AM, Murtaugh MP, Wang X, Morrison RB. Applications of Bayesian Phylodynamic Methods in a Recent U.S. Porcine Reproductive and Respiratory Syndrome Virus Outbreak. Front Microbiol. 2016;7: 67. doi: 10.3389/fmicb.2016.00067 26870024
32. Carrington CVF, Foster JE, Pybus OG, Bennett SN, Holmes EC. Invasion and Maintenance of Dengue Virus Type 2 and Type 4 in the Americas. J Virol. 2005;79: 14680–14687. doi: 10.1128/JVI.79.23.14680-14687.2005 16282468
33. Drummond AJ, Rambaut A, Shapiro B, Pybus OG. Bayesian coalescent inference of past population dynamics from molecular sequences. Molecular biology and evolution. 2005. pp. 1185–1192. doi: 10.1093/molbev/msi103 15703244
34. Minin VN, Bloomquist EW, Suchard MA. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol Biol Evol. 2008;25: 1459–1471. doi: 10.1093/molbev/msn090 18408232
35. Heled J, Drummond AJ. Bayesian inference of population size history from multiple loci. BMC Evol Biol. BioMed Central; 2008;8: 289. doi: 10.1186/1471-2148-8-289 18947398
36. Gill MS, Lemey P, Faria NR, Rambaut A, Shapiro B, Suchard MA. Improving Bayesian Population Dynamics Inference: A Coalescent-Based Model for Multiple Loci. Mol Biol Evol. Narnia; 2013;30: 713–724. doi: 10.1093/molbev/mss265 23180580
37. Heller R, Chikhi L, Siegismund HR. The Confounding Effect of Population Structure on Bayesian Skyline Plot Inferences of Demographic History. Mailund T, editor. PLoS One. Public Library of Science; 2013;8: e62992. doi: 10.1371/journal.pone.0062992 23667558
38. Hall MD, Woolhouse MEJ, Rambaut A. The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study. Virus Evol. Narnia; 2016;2. doi: 10.1093/ve/vew003 27774296
39. Lee DH, Bertran K, Kwon JH, Swayne DE. Evolution, global spread, and pathogenicity of highly pathogenic avian influenza H5Nx clade 2.3.4.4. J Vet Sci. 2017;18: 269–280. doi: 10.4142/jvs.2017.18.S1.269 28859267
40. Lu L, Brown AJL, Lycett SJ. Quantifying predictors for the spatial diffusion of avian influenza virus in China. BMC Evol Biol. 2017;17: 16. doi: 10.1186/s12862-016-0845-3 28086751
41. Magee D, Beard R, Suchard MA, Lemey P, Scotch M. Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus diffusion. Arch Virol. Austria; 2015;160: 215–224. doi: 10.1007/s00705-014-2262-5 25355432
42. Lemey P, Rambaut A, Bedford T, Faria N, Bielejec F, Baele G, et al. Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. PLoS Pathog. 2014;10: e1003932. doi: 10.1371/journal.ppat.1003932 24586153
43. Magee D, Scotch M. The effects of random taxa sampling schemes in Bayesian virus phylogeography. Infect Genet Evol. Elsevier; 2018;64: 225–230. doi: 10.1016/j.meegid.2018.07.003 29991455
44. Loth L, Gilbert M, Osmani MG, Kalam AM, Xiao X. Risk factors and clusters of Highly Pathogenic Avian Influenza H5N1 outbreaks in Bangladesh. Prev Vet Med. NIH Public Access; 2010;96: 104–13. doi: 10.1016/j.prevetmed.2010.05.013 20554337
45. Paul M, Tavornpanich S, Abrial D, Gasqui P, Charras-Garrido M, Thanapongtharm W, et al. Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model. Vet Res. BioMed Central; 2010;41: 28. doi: 10.1051/VETRES/2009076 20003910
46. Ward MP, Maftei D, Apostu C, Suru A. Environmental and anthropogenic risk factors for highly pathogenic avian influenza subtype H5N1 outbreaks in Romania, 2005–2006. Vet Res Commun. 2008;32: 627–34. doi: 10.1007/s11259-008-9064-8 18528778
47. Loth L, Gilbert M, Wu J, Czarnecki C, Hidayat M, Xiao X. Identifying risk factors of highly pathogenic avian influenza (H5N1 subtype) in Indonesia. Prev Vet Med. 2011;102: 50–8. doi: 10.1016/j.prevetmed.2011.06.006 21813198
48. Yupiana Y, de Vlas SJ, Adnan NM, Richardus JH. Risk factors of poultry outbreaks and human cases of H5N1 avian influenza virus infection in West Java Province, Indonesia. Int J Infect Dis. 2010;14: e800–e805. doi: 10.1016/j.ijid.2010.03.014 20637674
49. RIVAS AL, CHOWELL G, SCHWAGER SJ, FASINA FO, HOOGESTEIJN AL, SMITH SD, et al. Lessons from Nigeria: the role of roads in the geo-temporal progression of avian influenza (H5N1) virus. Epidemiol Infect. Cambridge University Press; 2010;138: 192. doi: 10.1017/S0950268809990495 19653927
50. Gilbert M, Pfeiffer DU. Risk factor modelling of the spatio-temporal patterns of highly pathogenic avian influenza (HPAIV) H5N1: A review. Spat Spatiotemporal Epidemiol. 2012;3: 173–183. doi: 10.1016/j.sste.2012.01.002 22749203
51. Martin V, Pfeiffer DU, Zhou X, Xiao X, Prosser DJ, Guo F, et al. Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China. PLoS Pathogens. 2011. p. e1001308. doi: 10.1371/journal.ppat.1001308 21408202
52. Müller NF, Dudas G, Stadler T. Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations. Virus Evol. 2019;5. doi: 10.1093/ve/vez030 31428459
53. Bertran K, Lee D-H, Balzli C, Pantin-Jackwood MJ, Spackman E, Swayne DE. Age is not a determinant factor in susceptibility of broilers to H5N2 clade 2.3.4.4 high pathogenicity avian influenza virus. Vet Res. BioMed Central; 2016;47: 116. doi: 10.1186/s13567-016-0401-6 27871330
54. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H, Xie D, et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 2014;10: e1003537. doi: 10.1371/journal.pcbi.1003537 24722319
55. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14: 587–589. doi: 10.1038/nmeth.4285 28481363
56. Kimura M. Estimation of evolutionary distances between homologous nucleotide sequences. Proc Natl Acad Sci U S A. National Academy of Sciences; 1981;78: 454–8. doi: 10.1073/pnas.78.1.454 6165991
57. Yang Z. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods. J Mol Evol. Springer-Verlag; 1994;39: 306–314. doi: 10.1007/bf00160154 7932792
58. Rambaut A, Lam TT, Carvalho LM, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2016;2: vew007. doi: 10.1093/ve/vew007 27774300
59. Lartillot N, Philippe H. Computing Bayes Factors Using Thermodynamic Integration. Syst Biol. 2006;55: 195–207. doi: 10.1080/10635150500433722 16522570
60. Kass RE, Raftery AE. Bayes Factors. J Am Stat Assoc. 1995;90: 773–795. doi: 10.1080/01621459.1995.10476572
61. Lemey P, Rambaut A, Drummond AJ, Suchard MA. Bayesian phylogeography finds its roots. PLoS Comput Biol. 2009;5: e1000520. doi: 10.1371/journal.pcbi.1000520 19779555
62. Edwards CJ, Suchard MA, Lemey P, Welch JJ, Barnes I, Fulton TL, et al. Ancient Hybridization and an Irish Origin for the Modern Polar Bear Matriline. Curr Biol. 2011;21: 1251–1258. doi: 10.1016/j.cub.2011.05.058 21737280
63. Raftery AE, Newton M, Satagopan J, Krivitsky P, Raftery G. Estimating the integrated likelihood via posterior simulation using the harmonic mean identity. 2007.
64. Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ, Rambaut A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 2018;4: vey016. doi: 10.1093/ve/vey016 29942656
65. Bielejec F, Lemey P, Baele G, Rambaut A, Suchard MA. Inferring Heterogeneous Evolutionary Processes Through Time: from Sequence Substitution to Phylogeography. Syst Biol. Narnia; 2014;63: 493–504. doi: 10.1093/sysbio/syu015 24627184
66. National Audubon Society. Important Bird Areas Database, Boundary Digital Data Set. 2018.
67. U.S. Geological Survey. NLCD 2011 Land Cover (2011 Edition, amended 2014). U.S. Geological Survey; 2014.
68. Kim Y, Kimball JS, Glassy J, McDonald KC. No TitleMEaSUREs Northern Hemisphere Polar EASE-Grid 2.0 Daily 6 km Land Freeze/Thaw Status from AMSR-E and AMSR2, Version 1. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center; 2018. https://doi.org/10.5067/WM9R9LQ2SA85
69. Kim Y, Kimball JS, Glassy J, Du J. An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing. Earth Syst Sci Data. 2017;9: 133–147. doi: 10.5194/essd-9-133-2017
70. Salvatier J, Wiecki T V., Fonnesbeck C. Probabilistic programming in Python using PyMC3. PeerJ Comput Sci. 2016;2: e55. doi: 10.7717/peerj-cs.55
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