Advertisement

Environmental and Ecological Statistics

, Volume 21, Issue 4, pp 651–666 | Cite as

Clustering of the abundance of West Nile virus vector mosquitoes in Peel Region, Ontario, Canada

  • Xiaogang Wang
  • Jiafeng Wang
  • Curtis Russell
  • Paul Proctor
  • Richard Bello
  • Kaz Higuchi
  • Huaiping ZhuEmail author
Article

Abstract

Understanding the spatial–temporal distribution of vector mosquitoes is essential in designing an efficient mosquito control strategy to reduce the risk of the mosquito-borne disease. In this paper, we apply a non-parametric clustering method, CLUES, to the surveillance data of West Nile virus vector mosquitoes collected by light traps in Peel Region, Ontario, during the mosquito seasons in 2004–2010. In order to obtain robust and reliable results, a statistical smoothing procedure LOWESS is applied to the original time series data. It was found that the mosquito trap sites can be clustered into three groups. The weather impact on the mosquito abundance of each clustered group are similar, while the interannual variability and the highest abundance and peak time in each mosquito season are different. The impact of weather factors on this clustering is investigated.

Keywords

Abundance Automatic clustering Culex pipiens/restuans mosquitoes  \(k\)-Nearest neighbors Local shrinking Precipitation Temperature 

Notes

Acknowledgments

The authors would like to thank the editor, the associated editor and two reviewers for their valuable comments and suggestions that greatly improve the quality and presentation of the paper. The opinions, results and conclusions reported in this paper are those of the authors. No endorsement by the Ontario Agency for Health Protection and Promotion is intended or should be inferred.

References

  1. Artz M (2011) Surveillance and epidemiology of infectious diseases using spatial and temporal clustering methods. Integrated series in information systems, 1, vol 27. Infectious disease informatics and biosurveillance. Part 2, pp 207–234Google Scholar
  2. Balakrishnan S, Srinivasan M, Elumalai K (2011) A survey on mosquito diversity in Parangipettai coast, southeast coast of Tamilnadu, India. J Entomol 8:259–266CrossRefGoogle Scholar
  3. Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17:790–799CrossRefGoogle Scholar
  4. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836CrossRefGoogle Scholar
  5. Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596–610CrossRefGoogle Scholar
  6. Comaniciu D, Meer P (1999) Mean shift analysis and applications. In: Proceedings of the seventh international conference on computer vision, pp 1197–1203Google Scholar
  7. Comaniciu D, Meer P (2000) Real-time tracking of non-rigid objects using mean ahift. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR’00). vol 2, pp 142–149Google Scholar
  8. Comaniciu D, Meer P (2001) The variable bandwidth mean shift and data-driven scale selection. In: Proceedings of the eighth international conference on computer vision, vol 1, pp 438–445Google Scholar
  9. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603–619CrossRefGoogle Scholar
  10. DeGaetano AT (2005) Meteorological effects on adult mosquito (Culex) population in metropolitan New Jersey. Int J Biometeorol 49:345–353PubMedCrossRefGoogle Scholar
  11. Devi NP, Jauhari RK (2007) Mosquito species associated within some western Himalayas phytogeographic zones in the Garhwal region of India. J Insect Sci 7:32Google Scholar
  12. Drackley A, Newbold KB, Taylor C (2011) Defining social-based spatial boundaries in the Region of Peel, Ontario, Canada. Int J Health Geogr 10:38PubMedCentralPubMedCrossRefGoogle Scholar
  13. Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Amer Stat Assoc 97:611–631CrossRefGoogle Scholar
  14. Fukunaga K, Hostetler LD (1975) The estimation of the Gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21:32–40CrossRefGoogle Scholar
  15. Hartigan JA, Wong MA (1979) A K-means clustering algorithm. Appl Stat 28:100–108CrossRefGoogle Scholar
  16. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkCrossRefGoogle Scholar
  17. Kilpatrick AM, Meola MA, Moudy RM, Kramer LD (2008) Temperature, viral genetics, and the transmission of West Nile virus by Culex pipiens mosquitoes. PLoS Pathog 4:e1000092. doi: 10.1371/journal.ppat.1000092 PubMedCentralPubMedCrossRefGoogle Scholar
  18. Kundu S (1999) Gravitational clustering: a new approach based on the spatial distribution of the points. Pattern Recognit 32:1149–1160CrossRefGoogle Scholar
  19. Mack YP, Rosenblatt M (1979) Multivariate \(k\)-nearest neighbor density estimates. J Multivar Anal 9:1–15CrossRefGoogle Scholar
  20. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1, Berkeley. University of California Press, CA, pp 281–297Google Scholar
  21. Madder DJ, Surgeoner GA, Helson BV (1983) Number of generations, egg production, and developmental time of Culex pipiens and Culex restuans (Diptera: Culicidae) in Southern Ontario. J Med Entomol 20:275–287PubMedGoogle Scholar
  22. Magbity EB, Lines JD (2002) Spatial and temporal distribution of Anopheles Gambiae s.l. (Diptera: Culicidae) in two Tanzanian villages: implication for designing mosquito sampling routines. Bull Entomol Res 92:483–488PubMedCrossRefGoogle Scholar
  23. Mostashari F, Kulldorff M, Hartman JJ, Miller JR, Kulasekera V (2003) Dead bird clustering: a potential early warning system for West Nile virus activity. Emerg Infect Dis 9:641–646PubMedCentralPubMedCrossRefGoogle Scholar
  24. Patz JA, Olson SH, Uejio CK, Gibbs HK (2008) Disease emergence from global climate and land use change. Med Clin N Am 92:1473–1491PubMedCrossRefGoogle Scholar
  25. Peel Public Health (2002) West Nile virus. http://www.peelregion.ca/health/vbd/whatis-WNV.htm
  26. Richards SL, Mores CN, Lord CC, Tabachnick WJ (2007) Impact of extrinsic incubation temperature and virus exposure on vector competence of Culex pipiens quinquefasciatus Say (Diptera: Culicidae) for West Nile virus. Vector Borne Zoonotic Dis 7:629–636PubMedCentralPubMedCrossRefGoogle Scholar
  27. Sato Y (2000) An autonomous clustering technique. In: Kiers, ALH, Rasson, J-P, Groenen, PJE, Schader, M (eds) Data analysis, classification, and related methods. Springer, BerlinGoogle Scholar
  28. Service MW (1993) Mosquito ecology: field sampling methods, 2nd edn. Chapman and Hall, LondonCrossRefGoogle Scholar
  29. Simonoff JS (1993) Smoothing methods in statistics. Springer, BerlinGoogle Scholar
  30. Tibshirani R, Walther G, Hastie T (2000) Estimating the number of clusters in a dataset via the gap statistic. Technical report 208, Dept of Statistics, Stanford UnivGoogle Scholar
  31. Trawinski, MacKay DS (2008) Meteorologically conditioned time-series predictions of West Nile virus vector mosquitoes. Vector-Borne Zoonotic Dis 8:505–521PubMedCrossRefGoogle Scholar
  32. Wang J, Ogden NH, Zhu H (2011) The impact of weather conditions on Culex pipiens and Culex restuans mosquito abundance: a case study in Peel Region. J Med Entomol 48:468–475PubMedCrossRefGoogle Scholar
  33. Wang JH, Rau JD (2001) VQ-agglomeration: a novel approach to clustering. IEE Proc-Vis Image Signal Process 148:36–44CrossRefGoogle Scholar
  34. Wang X, Qiu W, Zamar RH (2006) CLUES: a non-parametric clustering method based on local shrinking. Comput Stat Data Anal 52:286–298CrossRefGoogle Scholar
  35. Wright WE (1977) Gravitational clustering. Pattern Recognit 9:151–166CrossRefGoogle Scholar
  36. Zhong H, Yan Z, Jones F, Brock C (2003) Ecological analysis of mosquito light trap collections from west central Florida. Environ Entomol 32:807–815CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Xiaogang Wang
    • 1
  • Jiafeng Wang
    • 1
  • Curtis Russell
    • 2
  • Paul Proctor
    • 3
  • Richard Bello
    • 4
  • Kaz Higuchi
    • 5
    • 6
  • Huaiping Zhu
    • 1
    Email author
  1. 1.LAMPS, Department of Mathematics and StatisticsYork UniversityTorontoCanada
  2. 2.Enteric, Zoonotic and Vector-Borne DiseasesPublic Health OntarioTorontoCanada
  3. 3.Environmental Health, Vector-Borne Diseases TeamRegion of PeelCanada
  4. 4.Department of GeographyYork UniversityTorontoCanada
  5. 5.Faculty of Environmental StudiesYork UniversityTorontoCanada
  6. 6.Adaptation and Impacts Research SectionEnvironment CanadaTorontoCanada

Personalised recommendations