Am J Epidemiol 2003; 157:843-854.
Copyright © 2003 by Johns
Hopkins Bloomberg School of Public Health
PRACTICE OF EPIDEMIOLOGY |
Identifying West Nile Virus Risk Areas: The Dynamic Continuous-Area Space-Time System
1 Center for Advanced Research of Spatial Information, Hunter College, City University of New York, New York, NY.
2 Office of Policy and Planning, New York City Department of Health and Mental Hygiene, New York, NY.
The Dynamic Continuous-Area Space-Time (DYCAST) system was developed to identify and prospectively monitor high-risk areas for West Nile virus in New York, New York (New York City). The system is based on a geographic model that uses a localized Knox test to capture the nonrandom space-time interaction of dead birds, as an indicator of an intense West Nile virus amplification cycle, within a 1.5-mile (2.41-km) buffer area and 21-day moving window. The Knox analysis is implemented as an interpolation function to create a surface of probabilities over a grid of 1,400 cells overlaying New York City. The model’s parameters were calibrated using year 2000 data and information on the vector-host transmission cycle. The DYCAST system was implemented in a geographic information system and used operationally in year 2001. It successfully identified areas of high risk for human West Nile virus infection in areas where five of seven human cases resided, at least 13 days prior to the onset of illness, and proved that it can be used as an effective tool for targeting remediation and control efforts.
arboviruses; geographic information system; space-time clustering; West Nile virus
Abbreviations: Abbreviations: DYCAST, Dynamic Continuous-Area Space-Time; MAUP, modifiable areal unit problem.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  A. M. Winters, B. G. Bolling, B. J. Beaty, C. D. Blair, R. J. Eisen, A. M. Meyer, W. J. Pape, C. G. Moore, and L. Eisen Combining Mosquito Vector and Human Disease Data for Improved Assessment of Spatial West Nile Virus Disease Risk Am J Trop Med Hyg, April 1, 2008; 78(4): 654 - 665. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. F. Nielsen, M. V. Armijos, S. Wheeler, T. E. Carpenter, W. M. Boyce, K. Kelley, D. Brown, T. W. Scott, and W. K. Reisen Risk Factors Associated with Human Infection during the 2006 West Nile Virus Outbreak in Davis, a Residential Community in Northern California Am J Trop Med Hyg, January 1, 2008; 78(1): 53 - 62. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. D. Johnson, M. Eidson, K. Schmit, A. Ellis, and M. Kulldorff Geographic Prediction of Human Onset of West Nile Virus Using Dead Crow Clusters: An Evaluation of Year 2002 Data in New York State Am. J. Epidemiol., January 15, 2006; 163(2): 171 - 180. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. R. Ward, D. E. Stallknecht, J. Willis, M. J. Conroy, and W. R. Davidson Wild bird mortality and west nile virus surveillance: biases associated with detection, reporting, and carcass persistence. J. Wildl. Dis., January 1, 2006; 42(1): 92 - 106. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. A. LANGEVIN, A. C. BRAULT, N. A. PANELLA, R. A. BOWEN, and N. KOMAR VARIATION IN VIRULENCE OF WEST NILE VIRUS STRAINS FOR HOUSE SPARROWS (PASSER DOMESTICUS) Am J Trop Med Hyg, January 1, 2005; 72(1): 99 - 102. [Abstract] [Full Text] [PDF]
|
|