Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom

English version

Autoři: Robin R. L. Simons ^aff001; Simon Croft ^aff002; Eleanor Rees ^aff001; Oliver Tearne ^aff001; Mark E. Arnold ^aff001; Nicholas Johnson ^aff001
Působiště autorů: Animal and Plant Health Agency, New Haw, Surrey, United Kingdom ^aff001; National Wildlife Management Centre, Animal and Plant Health Agency, Sand Hutton York, United Kingdom ^aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225250

Souhrn

Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

Klíčová slova:

Europe – Invasive species – Livestock – Mosquitoes – Rift Valley fever virus – Sheep – Veterinary diseases – Rift Valley fever

Zdroje

1. WHO. Number of malaria cases; Estimated cases 200–2015 2015 [cited 2018 Apr 2018]. Available from: http://www.who.int/gho/malaria/epidemic/cases/en/.

2. Spiteri G, Sudre B, Septfons A, Beaute J, The European Zika Surveillance N. Surveillance of Zika virus infection in the EU/EEA, June 2015 to January 2017. Euro Surveill. 2017;22(41). doi: 10.2807/1560-7917.ES.2017.22.41.17–00254 29043960; PubMed Central PMCID: PMC5710121.

3. Roberts H, Moir R, Matt C, Spray M, Boden L, Bessell P. Risk assessment for Bluetongue Virus (BTV-8): risk assessment of entry into the United Kingdom. 2016.

4. Gale P, Stephenson B, Brouwer A, Martinez M, de la Torre A, Bosch J, et al. Impact of climate change on risk of incursion of Crimean-Congo haemorrhagic fever virus in livestock in Europe through migratory birds. Journal of Applied Microbiology. 2012;112(2):246–57. doi: 10.1111/j.1365-2672.2011.05203.x WOS:000299069200002. 22118269

5. Baylis M. Potential impact of climate change on emerging vector-borne and other infections in the UK. Environ Health. 2017;16(Suppl 1):112. doi: 10.1186/s12940-017-0326-1 29219091; PubMed Central PMCID: PMC5773876.

6. Simons RRL, Gale P, Horigan V, Snary EL, Breed AC. Potential for Introduction of Bat-Borne Zoonotic Viruses into the EU: A Review. Viruses-Basel. 2014;6(5):2084–121. doi: 10.3390/v6052084 WOS:000337160900012. 24841385

7. Simons RRL, Horigan V, Gale P, Kosmider RD, Breed AC, Snary EL. A Generic Quantitative Risk Assessment Framework for the Entry of Bat-Borne Zoonotic Viruses into the European Union. PLoS One. 2016;11(10). doi: 10.1371/journal.pone.0165383 WOS:000389604900072. 27788234

8. Gale P, Estrada-Pena A, Martinez M, Ulrich RG, Wilson A, Capelli G, et al. The feasibility of developing a risk assessment for the impact of climate change on the emergence of Crimean-Congo haemorrhagic fever in livestock in Europe: a Review. Journal of Applied Microbiology. 2010;108(6):1859–70. doi: 10.1111/j.1365-2672.2009.04638.x WOS:000277412600001. 20015209

9. Schaffner F, Medlock JM, Van Bortel W. Public health significance of invasive mosquitoes in Europe. Clin Microbiol Infect. 2013;19(8):685–92. doi: 10.1111/1469-0691.12189 23574618.

10. Cadar D, Luhken R, van der Jeugd H, Garigliany M, Ziegler U, Keller M, et al. Widespread activity of multiple lineages of Usutu virus, western Europe, 2016. Euro Surveill. 2017;22(4). doi: 10.2807/1560-7917.ES.2017.22.4.30452 28181903; PubMed Central PMCID: PMC5388094.

11. Barzon L, Papa A, Lavezzo E, Franchin E, Pacenti M, Sinigaglia A, et al. Phylogenetic characterization of Central/Southern European lineage 2 West Nile virus: analysis of human outbreaks in Italy and Greece, 2013–2014. Clin Microbiol Infect. 2015;21(12):1122 e1–10. doi: 10.1016/j.cmi.2015.07.018 26235197.

12. Mansfield KL, Banyard AC, McElhinney L, Johnson N, Horton DL, Hernandez-Triana LM, et al. Rift Valley fever virus: A review of diagnosis and vaccination, and implications for emergence in Europe. Vaccine. 2015;33(42):5520–31. doi: 10.1016/j.vaccine.2015.08.020 26296499.

13. Al-Afaleq AI, Hussein MF. The status of Rift Valley fever in animals in Saudi Arabia: a mini review. Vector Borne Zoonotic Dis. 2011;11(12):1513–20. doi: 10.1089/vbz.2010.0245 21923257.

14. OIE. OIE, World Animal Health Information System (WAHIS) Interface 2018 [cited 2019 Jan 2019]. Available from: http://www.oie.int/wahis_2/public/wahid.php/Wahidhome/Home.

15. Promed. Promed international society for infetious diseases 2018 [cited 2018 Jul 2018]. Available from: https://www.promedmail.org/.

16. Jansen van Vuren P, Kgaladi J, Patharoo V, Ohaebosim P, Msimang V, Nyokong B, et al. Human Cases of Rift Valley Fever in South Africa, 2018. Vector Borne Zoonotic Dis. 2018. doi: 10.1089/vbz.2018.2357 30183525; PubMed Central PMCID: PMC6276270.

17. Busquets N, Xavier F, Martin-Folgar R, Lorenzo G, Galindo-Cardiel I, del Val BP, et al. Experimental infection of young adult European breed sheep with Rift Valley fever virus field isolates. Vector Borne Zoonotic Dis. 2010;10(7):689–96. doi: 10.1089/vbz.2009.0205 20854022.

18. Vloet RPM, Vogels CBF, Koenraadt CJM, Pijlman GP, Eiden M, Gonzales JL, et al. Transmission of Rift Valley fever virus from European-breed lambs to Culex pipiens mosquitoes. PLoS Negl Trop Dis. 2017;11(12):e0006145. doi: 10.1371/journal.pntd.0006145 29281642; PubMed Central PMCID: PMC5760105.

19. Brustolin M, Talavera S, Nunez A, Santamaria C, Rivas R, Pujol N, et al. Rift Valley fever virus and European mosquitoes: vector competence of Culex pipiens and Stegomyia albopicta (= Aedes albopictus). Med Vet Entomol. 2017;31(4):365–72. doi: 10.1111/mve.12254 28782121.

20. Lumley S, Hernandez-Triana LM, Horton DL, Fernandez de Marco MDM, Medlock JM, Hewson R, et al. Competence of mosquitoes native to the United Kingdom to support replication and transmission of Rift Valley fever virus. Parasit Vectors. 2018;11(1):308. doi: 10.1186/s13071-018-2884-7 29776384; PubMed Central PMCID: PMC5960175.

21. Fischer EA, Boender GJ, Nodelijk G, de Koeijer AA, van Roermund HJ. The transmission potential of Rift Valley fever virus among livestock in the Netherlands: a modelling study. Vet Res. 2013;44:58. doi: 10.1186/1297-9716-44-58 23876054; PubMed Central PMCID: PMC3733972.

22. Croft S, Chauvenet ALM, Smith GC. A systematic approach to estimate the distribution and total abundance of British mammals. PLoS One. 2017;12(6):e0176339. doi: 10.1371/journal.pone.0176339 28658248; PubMed Central PMCID: PMC5489149.

23. Robinson TP, Wint GR, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, et al. Mapping the global distribution of livestock. PLoS One. 2014;9(5):e96084. doi: 10.1371/journal.pone.0096084 24875496; PubMed Central PMCID: PMC4038494.

24. Sallam MF, Al Ahmed AM, Abdel-Dayem MS, Abdullah MA. Ecological niche modeling and land cover risk areas for rift valley fever vector, culex tritaeniorhynchus giles in Jazan, Saudi Arabia. PLoS One. 2013;8(6):e65786. doi: 10.1371/journal.pone.0065786 23762424; PubMed Central PMCID: PMC3675080.

25. Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss DJ, et al. Mapping the zoonotic niche of Ebola virus disease in Africa. eLife. 2014;3:e04395. doi: 10.7554/eLife.04395 25201877

26. Estrada-Pena A, Venzal JM. Climate niches of tick species in the Mediterranean region: modeling of occurrence data, distributional constraints, and impact of climate change. J Med Entomol. 2007;44(6):1130–8. doi: 10.1603/0022-2585(2007)44[1130:cnotsi]2.0.co;2 18047215.

27. Tran A, Ippoliti C, Balenghien T, Conte A, Gely M, Calistri P, et al. A geographical information system-based multicriteria evaluation to map areas at risk for Rift Valley fever vector-borne transmission in Italy. Transbound Emerg Dis. 2013;60 Suppl 2:14–23. doi: 10.1111/tbed.12156 24589097.

28. Sanchez-Vizcaino F, Martinez-Lopez B, Sanchez-Vizcaino JM. Identification of suitable areas for the occurrence of Rift Valley fever outbreaks in Spain using a multiple criteria decision framework. Vet Microbiol. 2013;165(1–2):71–8. doi: 10.1016/j.vetmic.2013.03.016 23602438.

29. Kosmider R, Smith J, Gillings S, Snow L, Breed AC, Irvine RM, et al. Updated risk of H5N1 HPAI incursion to poultry in Great Britain via wild birds. Vet Rec. 2016;179(18):464. doi: 10.1136/vr.103700 27634350.

30. Snow LC, Newson SE, Musgrove AJ, Cranswick PA, Crick HQ, Wilesmith JW. Risk-based surveillance for H5N1 avian influenza virus in wild birds in Great Britain. Vet Rec. 2007;161(23):775–81. 18065812.

31. Turell MJ, Dohm DJ, Fonseca DM. Comparison of the Potential for Different Genetic Forms in the Culex pipiens Complex in North America to Transmit Rift Valley Fever Virus. J Am Mosq Control Assoc. 2014;30(4):253–9. doi: 10.2987/14-6441R.1 25843130.

32. Ndiaye el H, Fall G, Gaye A, Bob NS, Talla C, Diagne CT, et al. Vector competence of Aedes vexans (Meigen), Culex poicilipes (Theobald) and Cx. quinquefasciatus Say from Senegal for West and East African lineages of Rift Valley fever virus. Parasit Vectors. 2016;9:94. doi: 10.1186/s13071-016-1383-y 26897521; PubMed Central PMCID: PMC4761212.

33. Medlock JM, Vaux AG. Seasonal dynamics and habitat specificity of mosquitoes in an English wetland: implications for UK wetland management and restoration. J Vector Ecol. 2015;40(1):90–106. doi: 10.1111/jvec.12137 26047189.

34. Medlock JM, Snow KR, Leach S. Possible ecology and epidemiology of medically important mosquito-borne arboviruses in Great Britain. Epidemiol Infect. 2007;135(3):466–82. doi: 10.1017/S0950268806007047 16893487; PubMed Central PMCID: PMC2870593.

35. Brugman VA, Horton DL, Phipps LP, Johnson N, Cook AJ, Fooks AR, et al. Epidemiological perspectives on West Nile virus surveillance in wild birds in Great Britain. Epidemiol Infect. 2013;141(6):1134–42. doi: 10.1017/S095026881200177X 22948134.

36. Snow KR. Mosquitoes: Richmond Publishing Co. Ltd.; 1990.

37. Fernandez de Marco M, Brugman VA, Hernandez-Triana LM, Thorne L, Phipps LP, Nikolova NI, et al. Detection of Theileria orientalis in mosquito blood meals in the United Kingdom. Vet Parasitol. 2016;229:31–6. doi: 10.1016/j.vetpar.2016.09.012 27809975.

38. Brugman VA, Hernandez-Triana LM, Prosser SW, Weland C, Westcott DG, Fooks AR, et al. Molecular species identification, host preference and detection of myxoma virus in the Anopheles maculipennis complex (Diptera: Culicidae) in southern England, UK. Parasit Vectors. 2015;8:421. doi: 10.1186/s13071-015-1034-8 26271277; PubMed Central PMCID: PMC4536751.

39. NBN. The UK National Biodiversity Network 2018. Available from: https://nbn.org.uk/.

40. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 2009;19(1):181–97. doi: 10.1890/07-2153.1 19323182.

41. Morton D, Rowland C, Wood C, Meek L, Marston C, Smith G, et al. Final report for LCM 2007—the new UK land cover map. CS Technical Report No 11/07 NERC/Centre for Ecology and Hydrology (CEH project number: C03259). 2011.

42. Office M. UK Met Office data 2018 [cited 2018 2018]. Available from: https://www.metoffice.gov.uk/services/data-provision/big-data-drive/wholesale.

43. Afrane YA, Githeko AK, Yan G. The ecology of Anopheles mosquitoes under climate change: case studies from the effects of deforestation in East African highlands. Ann N Y Acad Sci. 2012;1249:204–10. doi: 10.1111/j.1749-6632.2011.06432.x 22320421; PubMed Central PMCID: PMC3767301.

44. Elith J, Kearney M, Phillips S. The art of modelling range-shifting species. Methods in Ecology and Evolution. 2010;1(4):330–42.

45. Shabani F, Kumar L, Ahmadi M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol Evol. 2016;6(16):5973–86. doi: 10.1002/ece3.2332 27547370; PubMed Central PMCID: PMC4983607.

46. Duan RY, Kong XQ, Huang MY, Fan WY, Wang ZG. The predictive performance and stability of six species distribution models. PLoS One. 2014;9(11):e112764. doi: 10.1371/journal.pone.0112764 25383906; PubMed Central PMCID: PMC4226630.

47. Stohlgren TJ, Ma P, Kumar S, Rocca M, Morisette JT, Jarnevich CS, et al. Ensemble habitat mapping of invasive plant species. Risk Anal. 2010;30(2):224–35. doi: 10.1111/j.1539-6924.2009.01343.x 20136746.

48. Araujo MB, New M. Ensemble forecasting of species distributions. Trends Ecol Evol. 2007;22(1):42–7. doi: 10.1016/j.tree.2006.09.010 17011070.

49. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. 2008 [cited 2019 Jan 2019]. Available from: http://www.R-project.org.

50. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution. 2012;3:327–38. doi: 10.1111/j.2041-210X.2011.00172.x

51. Hijmans R. kfold partitioning, R dismo package 2019 [cited 2019 Jan 2019]. Available from: https://www.rdocumentation.org/packages/dismo/versions/1.1-4/topics/kfold.

52. Naimi B. R package, usdm: Uncertainty Analysis for Species distribution models 2019 [cited 2019 Jan 2019]. Available from: https://CRAN.R-project.org/package=usdm.

53. FAO. Food and Agriculture Organization of the United Nations. FAO GEONETWORK. GLW: The Gridded Livestock of the World Project (GeoLayer). 2015 [cited Jun 2019 Jun 2019]. Available from: http://data.fao.org/ref/c6f03530-f317-11db-9a22-000d939bc5d8.html?version=1.0.

54. rspatial. 5. Model fitting, prediction, and evaluation [cited 2019 Jan 2019]. Available from: http://rspatial.org/sdm/rst/5_sdm_models.html.

55. Hand DJ. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning. 2009;77:103–23. doi: 10.1007/s10994-009-5119-5

56. Frey HC, Patil SR. Identification and review of sensitivity analysis methods. Risk Anal. 2002;22(3):553–78. Epub 2002/06/29. 12088234.

57. Ewing DA, Purse BV, Cobbold CA, Schafer SM, White SM. Uncovering mechanisms behind mosquito seasonality by integrating mathematical models and daily empirical population data: Culex pipiens in the UK. Parasit Vectors. 2019;12(1):74. doi: 10.1186/s13071-019-3321-2 30732629; PubMed Central PMCID: PMC6367758.

Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom

Souhrn

Klíčová slova:

Zdroje

PLOS One

INSIGHTS from European Respiratory Congress

Současné pohledy na riziko v parodontologii

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií

Střevní příprava před kolonoskopií