Display options
Cite
Kala AK, Tiwari C, Mikler AR, et al. A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ. 2017;5:e3070doi: 10.7717/peerj.3070.
Kala, A. K., Tiwari, C., Mikler, A. R., & Atkinson, S. F. (2017). A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ, 5e3070. https://doi.org/10.7717/peerj.3070
Kala, Abhishek K, et al. "A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters." PeerJ vol. 5 (2017): e3070. doi: https://doi.org/10.7717/peerj.3070
Kala AK, Tiwari C, Mikler AR, Atkinson SF. A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ. 2017 Mar 28;5:e3070. doi: 10.7717/peerj.3070. eCollection 2017. PMID: 28367364; PMCID: PMC5372833.
Copy Download .nbib Format: NLM
Share it on

PeerJ. 2017 Mar 28;5:e3070. doi: 10.7717/peerj.3070. eCollection 2017.

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters.

PeerJ

Abhishek K Kala, Chetan Tiwari, Armin R Mikler, Samuel F Atkinson

Affiliations

  1. Advanced Environmental Research Institute and Department of Biological Sciences, University of North Texas , Denton , TX , United States.
  2. Advanced Environmental Research Institute and Department of Geography and the Environment, University of North Texas , Denton , TX , United States.
  3. Advanced Environmental Research Institute and Department of Computer Science and Engineering, University of North Texas , Denton , TX , United States.

PMID: 28367364 PMCID: PMC5372833 DOI: 10.7717/peerj.3070

Abstract

BACKGROUND: The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.

METHODS: We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.

RESULTS: LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted

CONCLUSIONS: The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Keywords: Avian impacts; Emerging infectious diseases; Geographic information systems (GIS); Model comparison; Spatial modeling; West Nile virus

Conflict of interest statement

The authors declare there are no competing interests.

References

  1. PLoS One. 2011;6(8):e23280 - PubMed
  2. PLoS Negl Trop Dis. 2013;7(1):e1998 - PubMed
  3. Emerg Infect Dis. 2001 Jul-Aug;7(4):662-4 - PubMed
  4. Am J Trop Med Hyg. 2014 Mar;90(3):389-90 - PubMed
  5. Am J Trop Med Hyg. 1994 Sep;51(3):271-80 - PubMed
  6. Int J Health Geogr. 2008 May 01;7:19 - PubMed
  7. Vector Borne Zoonotic Dis. 2002 Fall;2(3):157-64 - PubMed
  8. Emerg Infect Dis. 2007 Dec;13(12):1918-20 - PubMed
  9. Emerg Infect Dis. 2001 Jul-Aug;7(4):615-20 - PubMed
  10. PLoS One. 2014 May 07;9(5):e96935 - PubMed
  11. J Am Mosq Control Assoc. 2014 Mar;30(1):7-20 - PubMed
  12. Emerg Infect Dis. 2007 Nov;13(11):1788-90 - PubMed
  13. Int J Health Geogr. 2006 Aug 31;5:36 - PubMed
  14. Emerg Infect Dis. 2003 Jun;9(6):641-6 - PubMed
  15. Int J Health Geogr. 2008 Mar 29;7:12 - PubMed
  16. J Med Entomol. 2007 Nov;44(6):1067-73 - PubMed
  17. Emerg Infect Dis. 2004 Aug;10(8):1369-78 - PubMed
  18. Emerg Infect Dis. 2003 Apr;9(4):483-4 - PubMed
  19. Emerg Infect Dis. 2001 Jul-Aug;7(4):631-5 - PubMed
  20. Int J Health Geogr. 2011 Jul 25;10:44 - PubMed
  21. Vector Borne Zoonotic Dis. 2008 Aug;8(4):505-21 - PubMed
  22. Int J Health Geogr. 2008 Apr 15;7:15 - PubMed
  23. MMWR Morb Mortal Wkly Rep. 2001 Jul 27;50(29):617-9 - PubMed
  24. Science. 2011 Oct 21;334(6054):323-7 - PubMed
  25. Geospat Health. 2014 Nov;9(1):203-12 - PubMed
  26. Geospat Health. 2014 Nov;9(1):153-68 - PubMed
  27. JAMA. 2012 Nov 14;308(18):1846-8 - PubMed
  28. Lancet Infect Dis. 2002 Sep;2(9):519-29 - PubMed
  29. Geospat Health. 2012 Nov;7(1):91-100 - PubMed
  30. J Med Entomol. 2006 Mar;43(2):337-43 - PubMed
  31. Emerg Infect Dis. 2002 Dec;8(12):1385-91 - PubMed
  32. PLoS One. 2012;7(6):e38978 - PubMed
  33. Int J Health Geogr. 2004 Apr 20;3(1):8 - PubMed
  34. PLoS One. 2008;3(12):e3744 - PubMed
  35. Comp Immunol Microbiol Infect Dis. 2014 Mar;37(2):131-41 - PubMed
  36. Am J Epidemiol. 2006 Jan 15;163(2):171-80 - PubMed
  37. Emerg Infect Dis. 2005 Aug;11(8):1167-73 - PubMed
  38. N Engl J Med. 2001 Jun 14;344(24):1807-14 - PubMed
  39. Int J Health Geogr. 2011 Aug 03;10:50 - PubMed

Publication Types