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DYCAST is a spatiotemporal modeling algorithm for predicting hot spots of virus transmission

left: dengue model (Carney 2010). right: real-time prediction of West Nile virus epidemic in Sacramento County, California (Carney et al 2011)

DYCAST papers

Early warning system for West Nile virus risk areas, California, USA  pdf

Carney RM, Ahearn SC, McConchie A, Glaser C, Jean C, Barker C, Park B, Padgett K, Parker E, Aquino E, Kramer V. 2011. Emerging Infectious Diseases 17(8):1445-54.

 

GIS-based early warning system for predicting high-risk areas of dengue virus transmission, Ribeirão Preto, Brazil  pdf

Carney RM. 2010. Master of Public Health Thesis. Yale University. Winner of Dean’s Prize for Outstanding Thesis.

First evidence of West Nile virus amplification and relationship to human infections  pdf

Theophilides, C. N., Ahearn, S. C., Binkowski, E. S., Paul, W. S., & Gibbs, K. 2006. International Journal of Geographical Information Science, 20(1):103-115.

Identifying West Nile virus risk areas: the dynamic continuous-area space-time system  pdf

Theophilides, C. N., Ahearn, S. C., Grady, S., & Merlino, M. 2003. American Journal of Epidemiology, 157(9):843-854.   

 

A Comparison of two Significance Testing Methodologies for the Knox Test  pdf

Theophilides, C. N., E. S. Binkowski, S. C. Ahearn, & W. S. Paul. 2008. International Journal of Geoinformatics 4(3).

GitHub

DYCAST 2.0 (by Vincent Meijer): https://github.com/veuncent/dycast

DYCAST 1.0 (by Alan McConchie): https://github.com/almccon/DYCAST

West Nile virus early warning system

(red=high risk, black=human cases). Carney et al 2011