Professor of Epidemiology College of Veterinary Medicine Predictive Modeling of West Nile Virus Outbreaks Using Remotely-Sensed Data Dr. Michael Ward Professor of Epidemiology College of Veterinary Medicine Texas A&M University James Steele Conference on Diseases in Nature Transmissible to Man, Austin, 11 June 2007

James Schuermann Zoonosis Control Group Texas Department of State Health Services, Austin TX Linda Highfield Department of Veterinary Integrative Biosciences Texas A&M University, College Station TX partial funding provided by the Texas Equine Research Advisory Committee

Outline Background Methods Results Discussion Conclusions

1. Background

West Nile Virus family Flaviviridae genus Flavivirus Japanese Encephalitis serocomplex, includes: Japanese encephalitis Murray Valley encephalitis St. Louis encephalitis Kunjin antigenically, all closely related

WNV History first occurrence in U.S.: 1999 ( Bronx Zoo, New York ) by 2001: extension of range to include Florida 2002: large equine epidemic by 2003: 46 states, 7 Canadian provinces, 5 Mexican states only states WNV not detected: Alaska, Hawaii

WNV Life Cycle Vector Mosquito Reservoir Wild birds Dead end host Horses and humans

WNV Mosquito Vectors biological and mechanical vectors 14 species identified Culex spp. most likely in the U.S. breed in standing water Cx. pipiens, quiquefasciatus, tarsalis Aedes spp. may spread disease to horses breed in locations where water will be present

WNV Avian Reservoirs responsible for distribution >110 species of birds most susceptible species include American crows, fish jays, blue jays game species (wild ducks, geese, pheasants, turkeys, pigeons, doves) raptors (owls, hawks, eagles)

First indicators of WNV activity % counties dead bird 62 equine case 29 human case 4 infected mosquito pool 3 sentinel bird seroconversion 0.8 seropositive wild-caught bird 0.2

WNV Surveillance Programs avian mortality surveillance tracking system mosquito trapping and testing testing wild birds, sentinel chickens, horses and humans with neurologic disease forecasting systems: environmental variables temperature precipitation remotely-sensed data

2. Methods

reported cases of equine WNV encephalomyelitis: 2002, 2003 and 2004 time series of case reports, 2-week window image data: 2-week 1km2 resolution rasters of the Normalized Difference Vegetation Index (NDVI) mean NDVI for each 2-week period periods with versus without reported cases autoregressive model: NDVI as a predictor of equine WNV cases (scaled,  transform)

What is the NDVI? Advanced Very High Resolution Radiometer (AVHRR) sensor, NOAA polar-orbiting satellite Normalized Difference Vegetation Index: visible and near-infrared data daily observations  biweekly 1km2 resolution raster based on daily maximum observed NDVI value resulting 1x1 km pixel represents maximum scaled NDVI value during each 2 weeks of the study period

3. Results

correlation, number of cases reported versus NDVI: 45% 2-week periods N mean (95% CI) WNV cases reported 45 0.4390 (0.4219, 0.4561) WNV cases not reported 33 0.3962 (0.3730, 0.4193) (P<0.001) correlation, number of cases reported versus NDVI: 45%

cases = – 0.9102 + 8.5762 (casesweeks 1–2) – 5.6137 (casesweeks 3–4) + 0.9262 (NDVIweeks 1–2) – 0.2661 (NDVIweeks 3–4) no. observed versus predicted cases highly correlated (rSP 83%, P<0.001)

mean difference, observed versus predicted cases, P= 0.973

4. Discussion

Prevention and Control reduce exposure indoor housing, repellants? mosquito control larvicides, adulticides, environment vaccination killed or recombinant canarypox-vectored 2 doses, 3-6 weeks apart; annual booster

Forecasting Systems anticipate increases in risk identify “hotspots” optimize control strategies increased awareness identify “hotspots” sentinel warning for zoonotic disease

5. Conclusion

remotely-sensed data: availability low-cost coverage could be used to: enhanced WNV surveillance provide early warning of increased risk identify hotspots warn of potential zoonotic transmission of WNV