Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index.
Abstract: Data from an outbreak (August to October, 2002) of West Nile virus (WNV) encephalomyelitis in a population of horses located in northern Indiana was scanned for clusters in time and space. One significant (p = 0.04) cluster of case premises was detected, occurring between September 4 and 10 in the south-west part of the study area (85.70 degrees N, 45.50 degrees W). It included 10 case premises (3.67 case premises expected) within a radius of 2264 m. Image data were acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard a National Oceanic and Atmospheric Administration polar-orbiting satellite. The Normalized Difference Vegetation Index (NDVI) was calculated from visible and near-infrared data of daily observations, which were composited to produce a weekly-1km(2) resolution raster image product. During the epidemic, a significant (p < 0.01) decrease (0.025 per week) in estimated NDVI was observed at all case and control premise sites. The median estimated NDVI (0.659) for case premises within the cluster identified was significantly (p < 0.01) greater than the median estimated NDVI for other case (0.571) and control (0.596) premises during the same period. The difference in median estimated NDVI for case premises within this cluster, compared to cases not included in this cluster, was greatest (5.3% and 5.1%, respectively) at 1 and 5 weeks preceding occurrence of the cluster. The NDVI may be useful for identifying foci of WNV transmission.
Publication Date: 2005-07-14 PubMed ID: 16011435DOI: 10.1089/vbz.2005.5.181Google 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.
- Journal Article
Summary
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The research studied an outbreak of West Nile Virus (WNV) in horses in Indiana and its correlation with the normalized difference vegetation index (NDVI). They found that the NDVI, which measures the density of green vegetation, might be useful in identifying the source of WNV transmission.
Objective of the Research
- The objective was to understand whether there was a spatial and temporal pattern to an outbreak of West Nile virus (WNV) encephalomyelitis in horses in Northern Indiana during 2002, and if this pattern correlated with the Normalized Difference Vegetation Index (NDVI).
Methodology
- Data from the outbreak was collected and analyzed for clusters in time and space. A significant cluster was identified between September 4 and 10 in the southwest part of the study area.
- Image data was gathered using the Advanced Very High Resolution Radiometer (AVHRR) sensor on a National Oceanic and Atmospheric Administration satellite. This sensor measured visible and near-infrared data from observations, which were consolidated into a weekly 1km-square raster image product.
- The NDVI, which measures the quantity, quality and development of green vegetation, was calculated from this image data.
Findings
- During the epidemic, a significant decrease in the estimated NDVI was observed at all sites of cases and controls. This indicates a change in vegetation during the outbreak.
- The median estimated NDVI for premises within the main cluster was significantly higher than for other premises. In other words, there was more dense green vegetation in the area with the most virus cases.
- The difference in NDVI for premises within the main cluster, compared to premises not in the cluster, was highest at 1 and 5 weeks before the cluster occurred. This suggests that green vegetation could be a contributing factor to the occurrence of WNV.
Conclusion
- The NDVI may be a useful tool for identifying foci of WNV transmission – places where the virus spreads. This could potentially help to predict and possibly prevent future outbreaks of the virus.
Cite This Article
APA
Ward MP, Ramsay BH, Gallo K.
(2005).
Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index.
Vector Borne Zoonotic Dis, 5(2), 181-188.
https://doi.org/10.1089/vbz.2005.5.181 Publication
Researcher Affiliations
- Department of Veterinary Pathobiology, Purdue University School of Veterinary Medicine, West Lafayette, Indiana, USA. mward@cvm.tamu.edu
MeSH Terms
- Animals
- Biomass
- Cluster Analysis
- Disease Outbreaks / veterinary
- Ecology
- Geographic Information Systems
- Horse Diseases / epidemiology
- Horses
- Indiana / epidemiology
- Plants
- Population Surveillance
- Rural Health
- Seasons
- Topography, Medical / methods
- West Nile Fever / epidemiology
- West Nile Fever / veterinary
Citations
This article has been cited 7 times.- Conte A, Candeloro L, Ippoliti C, Monaco F, De Massis F, Bruno R, Di Sabatino D, Danzetta ML, Benjelloun A, Belkadi B, El Harrak M, Declich S, Rizzo C, Hammami S, Ben Hassine T, Calistri P, Savini G. Spatio-Temporal Identification of Areas Suitable for West Nile Disease in the Mediterranean Basin and Central Europe. PLoS One 2015;10(12):e0146024.
- 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.
- Zhang Z, Ward M, Gao J, Wang Z, Yao B, Zhang T, Jiang Q. Remote sensing and disease control in China: past, present and future. Parasit Vectors 2013 Jan 11;6:11.
- Perez A, Alkhamis M, Carlsson U, Brito B, Carrasco-Medanic R, Whedbee Z, Willeberg P. Global animal disease surveillance. Spat Spatiotemporal Epidemiol 2011 Sep;2(3):135-45.
- Epp TY, Waldner C, Berke O. Predictive risk mapping of West Nile virus (WNV) infection in Saskatchewan horses. Can J Vet Res 2011 Jul;75(3):161-70.
- Winters AM, Eisen RJ, Lozano-Fuentes S, Moore CG, Pape WJ, Eisen L. Predictive spatial models for risk of West Nile virus exposure in eastern and western Colorado. Am J Trop Med Hyg 2008 Oct;79(4):581-90.
- Clements AC, Pfeiffer DU. Emerging viral zoonoses: frameworks for spatial and spatiotemporal risk assessment and resource planning. Vet J 2009 Oct;182(1):21-30.
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