FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
International journal of health geographics2007; 6; 42; doi: 10.1186/1476-072X-6-42

Using geographic information systems and spatial and space-time scan statistics for a population-based risk analysis of the 2002 equine West Nile epidemic in six contiguous regions of Texas.

Abstract: In 2002, West Nile virus (WNV) first appeared in Texas. Surveillance data were retrospectively examined to explore the temporal and spatial characteristics of the Texas equine WNV epidemic in 2002. Using Geographic Information Systems (GIS) and the Spatial and Space-Time Scan (SaTScan) statistics, we analyzed 1421 of the reported equine WNV cases from six contiguous state Health Service Regions (HSRs), comprising 158 counties, in western, northern, central and eastern Texas. Results: Two primary epidemic peaks occurred in Epidemiological (Epi) week 35 (August 25 to 31) and Epi week 42 (October 13 to 19) of 2002 in the western and eastern part of the study area, respectively. The SaTScan statistics detected nine non-random spatio-temporal equine case aggregations (mini-outbreaks) and five unique high-risk areas imbedded within the overall epidemic. Conclusions: The 2002 Texas equine WNV epidemic occurred in a bi-modal pattern. Some "local hot spots" of the WNV epidemic developed in Texas. The use of GIS and SaTScan can be valuable tools in analyzing on-going surveillance data to identify high-risk areas and shifts in disease clustering within a large geographic area. Such techniques should become increasingly useful and important in future epidemics, as decisions must be made to effectively allocate limited resources.
Get Full Text
Publication Date: 2007-09-21 PubMed ID: 17888159PubMed Central: PMC2098755DOI: 10.1186/1476-072X-6-42Google 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 article discusses how Geographic Information Systems (GIS) and Space-Time Scan statistics were used to analyse and map a 2002 West Nile Virus (WNV) epidemic among horses in six regions of Texas. The investigation aimed to identify temporal and spatial characteristics of the epidemic, as well as high-risk areas.

Research Methodology

  • In order to analyse the 2002 WNV outbreak, the researchers used Geographic Information Systems and Spatial and Space-Time Scan (SaTScan) statistics. These systems allowed them to examine spatial (area boundaries and locations) and temporal (time/date) data.
  • From the total reported equine WNV cases, the researchers considered 1421 cases from six contiguous state Health Service Regions consisting of 158 counties across western, northern, central, and eastern Texas.
  • Surveillance data of these cases were retrospectively examined to gauge the epidemic’s progression.

Study Findings

  • The analysis indicated two primary epidemic peaks: one occurred in Epidemiological week 35 (August 25 to 31) in the western part of the study area, the other in Epidemiological week 42 (October 13 to 19) in the eastern part.
  • The SaTScan statistics identified nine instances of non-random equine case aggregates (mini-outbreaks) and five unique high-risk areas across the regions studied. These were zones demonstrating a greater concentration of WNV cases which could be defined as hot spots of the WNV epidemic.

Conclusion and Implications

  • The epidemic displayed a bi-modal pattern, as there were two significant peaks of disease occurrence in 2002.
  • The use of GIS and SaTScan was instrumental in identifying, analysing, and visualising spatio-temporal patterns and high-risk hubs in the epidemic within a vast geographic area.
  • This methodology will become increasingly vital in future epidemics for making informed decisions to effectively utilise and allocate scarce resources.

In general, the research presents a potentially effective and practical method of quickly mapping and understanding disease outbreaks, allowing for more strategic resource deployment in response to public health crises.

Cite This Article

APA
Lian M, Warner RD, Alexander JL, Dixon KR. (2007). Using geographic information systems and spatial and space-time scan statistics for a population-based risk analysis of the 2002 equine West Nile epidemic in six contiguous regions of Texas. Int J Health Geogr, 6, 42. https://doi.org/10.1186/1476-072X-6-42

Publication

International journal of health geographics
ISSN: 1476-072X
NlmUniqueID: 101152198
Country: England
Language: English
Volume: 6
Pages: 42

Researcher Affiliations

Lian, Min
  • Division of Modeling and Geographic Information Systems, Institute of Environmental and Human Health, Texas Tech University/TTU Health Sciences Center, Box 41163; Lubbock, TX 79409, USA. mlian@im.wustl.edu
Warner, Ronald D
    Alexander, James L
      Dixon, Kenneth R

        MeSH Terms

        • Animals
        • Cluster Analysis
        • Disease Outbreaks / veterinary
        • Geographic Information Systems
        • Horse Diseases / epidemiology
        • Horse Diseases / transmission
        • Horse Diseases / virology
        • Horses / virology
        • Population Surveillance
        • Retrospective Studies
        • Risk Assessment / methods
        • Texas / epidemiology
        • West Nile Fever / epidemiology
        • West Nile Fever / transmission
        • West Nile Fever / veterinary

        References

        This article includes 28 references
        1. . Outbreak of West Nile-like viral encephalitis--New York, 1999.. MMWR Morb Mortal Wkly Rep 1999 Oct 1;48(38):845-9.
          pubmed: 10563521
        2. Nash D, Mostashari F, Fine A, Miller J, O'Leary D, Murray K, Huang A, Rosenberg A, Greenberg A, Sherman M, Wong S, Layton M. The outbreak of West Nile virus infection in the New York City area in 1999.. N Engl J Med 2001 Jun 14;344(24):1807-14.
          doi: 10.1056/NEJM200106143442401pubmed: 11407341google scholar: lookup
        3. Steele KE, Linn MJ, Schoepp RJ, Komar N, Geisbert TW, Manduca RM, Calle PP, Raphael BL, Clippinger TL, Larsen T, Smith J, Lanciotti RS, Panella NA, McNamara TS. Pathology of fatal West Nile virus infections in native and exotic birds during the 1999 outbreak in New York City, New York.. Vet Pathol 2000 May;37(3):208-24.
          doi: 10.1354/vp.37-3-208pubmed: 10810985google scholar: lookup
        4. Ostlund EN, Crom RL, Pedersen DD, Johnson DJ, Williams WO, Schmitt BJ. Equine West Nile encephalitis, United States.. Emerg Infect Dis 2001 Jul-Aug;7(4):665-9.
          pmc: PMC2631754pubmed: 11589171doi: 10.3201/eid0704.010412google scholar: lookup
        5. Komar N. West Nile virus: epidemiology and ecology in North America.. Adv Virus Res 2003;61:185-234.
          pubmed: 14714433doi: 10.1016/s0065-3527(03)61005-5google scholar: lookup
        6. Buck PA, Sockett P, Barker IK. West Nile virus: surveillance activities in Canada. Annals of Epidemiology 2003;13:582.
          doi: 10.1016/S1047-2797(03)00199-6google scholar: lookup
        7. Dupuis AP 2nd, Marra PP, Kramer LD. Serologic evidence of West Nile virus transmission, Jamaica, West Indies.. Emerg Infect Dis 2003 Jul;9(7):860-3.
          pmc: PMC3023422pubmed: 12890329doi: 10.3201/eid0907.030249google scholar: lookup
        8. Estrada-Franco JG, Navarro-Lopez R, Beasley DW, Coffey L, Carrara AS, Travassos da Rosa A, Clements T, Wang E, Ludwig GV, Cortes AC, Ramírez PP, Tesh RB, Barrett AD, Weaver SC. West Nile virus in Mexico: evidence of widespread circulation since July 2002.. Emerg Infect Dis 2003 Dec;9(12):1604-7.
          pmc: PMC3034333pubmed: 14720402doi: 10.3201/eid0912.030564google scholar: lookup
        9. Petersen LR, Marfin AA, Gubler DJ. West Nile virus.. JAMA 2003 Jul 23;290(4):524-8.
          doi: 10.1001/jama.290.4.524pubmed: 12876096google scholar: lookup
        10. Mattar S, Edwards E, Laguado J, González M, Alvarez J, Komar N. West Nile virus antibodies in Colombian horses.. Emerg Infect Dis 2005 Sep;11(9):1497-8.
          pmc: PMC3310636pubmed: 16673523doi: 10.3201/eid1109.050426google scholar: lookup
        11. Petersen LR, Hayes EB. Westward ho?--The spread of West Nile virus.. N Engl J Med 2004 Nov 25;351(22):2257-9.
          doi: 10.1056/NEJMp048261pubmed: 15564540google scholar: lookup
        12. Trock SC, Meade BJ, Glaser AL, Ostlund EN, Lanciotti RS, Cropp BC, Kulasekera V, Kramer LD, Komar N. West Nile virus outbreak among horses in New York State, 1999 and 2000.. Emerg Infect Dis 2001 Jul-Aug;7(4):745-7.
          pmc: PMC2631776pubmed: 11585543doi: 10.3201/eid0704.010427google scholar: lookup
        13. Equine Science
        14. Annual Statistics for West Nile Virus in Texas
        15. Census of Agriculture – County Data
        16. Detailed Statistics for West Nile Virus in Texas
        17. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997;26:1481–1496.
          doi: 10.1080/03610929708831995google scholar: lookup
        18. Kulldorff M. Statistical methods for spatial epidemiology: tests for randomness. In: Loytonen M, Gatrell A, editor. GIS and Health. London, England: Taylor & Francis; 1998. pp. 49–62.
        19. Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.. Am J Public Health 1998 Sep;88(9):1377-80.
          pmc: PMC1509064pubmed: 9736881doi: 10.2105/ajph.88.9.1377google scholar: lookup
        20. Hjalmars U, Kulldorff M, Gustafsson G, Nagarwalla N. Childhood leukaemia in Sweden: using GIS and a spatial scan statistic for cluster detection.. Stat Med 1996 Apr 15-May 15;15(7-9):707-15.
          doi: 10.1002/(SICI)1097-0258(19960415)15:7/9<707::AID-SIM242>3.0.CO;2-4pubmed: 9132898google scholar: lookup
        21. Kulldorff M, Feuer EJ, Miller BA, Freedman LS. Breast cancer clusters in the northeast United States: a geographic analysis.. Am J Epidemiol 1997 Jul 15;146(2):161-70.
          pubmed: 9230778doi: 10.1093/oxfordjournals.aje.a009247google scholar: lookup
        22. Viel JF, Arveux P, Baverel J, Cahn JY. Soft-tissue sarcoma and non-Hodgkin's lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels.. Am J Epidemiol 2000 Jul 1;152(1):13-9.
          doi: 10.1093/aje/152.1.13pubmed: 10901325google scholar: lookup
        23. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference.. Stat Med 1995 Apr 30;14(8):799-810.
          doi: 10.1002/sim.4780140809pubmed: 7644860google scholar: lookup
        24. Dwass M. Modified randomization tests for nonparametric hypotheses. Annals of Mathematical Statistics 1957;28:181–187.
        25. Mostashari F, Kulldorff M, Hartman JJ, Miller JR, Kulasekera V. Dead bird clusters as an early warning system for West Nile virus activity.. Emerg Infect Dis 2003 Jun;9(6):641-6.
          pmc: PMC3000152pubmed: 12781002doi: 10.3201/eid0906.020794google scholar: lookup
        26. Warner RD, Kimbrough RC, Alexander JL, Rush Pierce J Jr, Ward T, Martinelli LP. Human west nile virus neuroinvasive disease in Texas, 2003 epidemic: regional differences.. Ann Epidemiol 2006 Oct;16(10):749-55.
          doi: 10.1016/j.annepidem.2006.04.006pubmed: 16978879google scholar: lookup
        27. . Minnesota Department of Health Syndromic Surveillance: A New Tool to Detect Disease Outbreaks. Disease Control Newsletter 2004;32:16–17.
        28. Ward MP. Spread of equine West Nile virus encephalomyelitis during the 2002 Texas epidemic.. Am J Trop Med Hyg 2006 Jun;74(6):1090-5.
          pubmed: 16760526

        Citations

        This article has been cited 16 times.
        1. Beeman SP, Morrison AM, Unnasch TR, Unnasch RS. Ensemble ecological niche modeling of West Nile virus probability in Florida. PLoS One 2021;16(10):e0256868.
          doi: 10.1371/journal.pone.0256868pubmed: 34624026google scholar: lookup
        2. Whiteman A, Desjardins MR, Eskildsen GA, Loaiza JR. Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data. PLoS Negl Trop Dis 2019 Sep;13(9):e0007266.
          doi: 10.1371/journal.pntd.0007266pubmed: 31545819google scholar: lookup
        3. Lu Y, Deng X, Chen J, Wang J, Chen Q, Niu B. Risk analysis of African swine fever in Poland based on spatio-temporal pattern and Latin hypercube sampling, 2014-2017. BMC Vet Res 2019 May 22;15(1):160.
          doi: 10.1186/s12917-019-1903-zpubmed: 31118049google scholar: lookup
        4. Stevens KB, Pfeiffer DU. Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems. Spat Spatiotemporal Epidemiol 2015 Apr;13:15-29.
          doi: 10.1016/j.sste.2015.04.003pubmed: 26046634google scholar: lookup
        5. Li J, Kolivras KN, Hong Y, Duan Y, Seukep SE, Prisley SP, Campbell JB, Gaines DN. Spatial and temporal emergence pattern of Lyme disease in Virginia. Am J Trop Med Hyg 2014 Dec;91(6):1166-72.
          doi: 10.4269/ajtmh.13-0733pubmed: 25331806google scholar: lookup
        6. Lal A, Hales S. Heterogeneity in hotspots: spatio-temporal patterns in neglected parasitic diseases. Epidemiol Infect 2015 Feb;143(3):631-9.
          doi: 10.1017/S0950268814001101pubmed: 24819745google scholar: lookup
        7. Ozdenerol E, Taff GN, Akkus C. Exploring the spatio-temporal dynamics of reservoir hosts, vectors, and human hosts of West Nile virus: a review of the recent literature. Int J Environ Res Public Health 2013 Oct 25;10(11):5399-432.
          doi: 10.3390/ijerph10115399pubmed: 24284356google scholar: lookup
        8. Ghosh D, Manson SM, McMaster RB. Delineating West Nile Virus Transmission Cycles at Various Scales: The Nearest Neighbor Distance-Time Model. Cartogr Geogr Inf Sci 2010 Apr 1;37(2):149-163.
          doi: 10.1559/152304010791232208pubmed: 23144590google scholar: lookup
        9. Vander Kelen PT, Downs JA, Stark LM, Loraamm RW, Anderson JH, Unnasch TR. Spatial epidemiology of eastern equine encephalitis in Florida. Int J Health Geogr 2012 Nov 5;11:47.
          doi: 10.1186/1476-072X-11-47pubmed: 23126615google scholar: lookup
        10. Kelen PT, Downs JA, Burkett-Cadena ND, Ottendorfer CL, Hill K, Sickerman S, Hernandez J, Jinright J, Hunt B, Lusk J, Hoover V, Armstrong K, Unnasch RS, Stark LM, Unnasch TR. Habitat associations of eastern equine encephalitis transmission in Walton County Florida. J Med Entomol 2012 May;49(3):746-56.
          doi: 10.1603/me11224pubmed: 22679885google scholar: lookup
        11. Hyder K, Vidal-Diez A, Lawes J, Sayers AR, Milnes A, Hoinville L, Cook AJ. Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain. BMC Vet Res 2011 Mar 19;7:14.
          doi: 10.1186/1746-6148-7-14pubmed: 21418593google scholar: lookup
        12. Liu H, Weng Q, Gaines D. Spatio-temporal analysis of the relationship between WNV dissemination and environmental variables in Indianapolis, USA. Int J Health Geogr 2008 Dec 18;7:66.
          doi: 10.1186/1476-072X-7-66pubmed: 19094221google scholar: lookup
        13. Guzmán-Urieta EO, Jordán-Dahlgren E, Alvarez-Filip L. Epidemiological analysis reveals coral species affected by stony coral tissue loss disease present a similar epizootic progression despite differences in susceptibility and population impact. PLoS One 2026;21(1):e0339054.
          doi: 10.1371/journal.pone.0339054pubmed: 41481580google scholar: lookup
        14. Grout L, Hales S, Baker MG, French N, Wilson N. Severe weather events and cryptosporidiosis in Aotearoa New Zealand: A case series of space-time clusters. Epidemiol Infect 2024 Apr 15;152:e64.
          doi: 10.1017/S095026882400058Xpubmed: 38616329google scholar: lookup
        15. Tesfaye S, Regassa F, Beyene G, Leta S, Paeshuyse J. Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak reports. Front Vet Sci 2024;11:1277007.
          doi: 10.3389/fvets.2024.1277007pubmed: 38532795google scholar: lookup
        16. Amenu K, McIntyre KM, Moje N, Knight-Jones T, Rushton J, Grace D. Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review. Front Vet Sci 2023;10:1231711.
          doi: 10.3389/fvets.2023.1231711pubmed: 37876628google scholar: lookup
        Subscribe to our newsletter
        Join 100,113 horse owners like you who receive our email updates on horse health and nutrition.