Discovering Spatio-Temporal Models of the Spread of West Nile Virus

@article{citeulike:564312, abstract = {Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment.(1-3) Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states.(4) The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts.(5) Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002.}, author = {Orme-Zavaleta, Jennifer and Jorgensen, Jane and D'Ambrosio, Bruce and Altendorf, Eric and Rossignol, Philippe A. }, citeulike-article-id = {564312}, doi = {10.1111/j.1539-6924.2006.00738.x}, issn = {0272-4332}, journal = {Risk Analysis}, keywords = {bayes, machine-learning, modeling, time-series, west-nile}, month = {April}, number = {2}, pages = {413--422}, posted-at = {2006-07-25 16:45:29}, priority = {2}, publisher = {Blackwell Publishing}, title = {Discovering Spatio-Temporal Models of the Spread of West Nile Virus}, url = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1539-6924.2006.00738.x}, volume = {26}, year = {2006} }

TY - JOUR ID - citeulike:564312 L3 - citeulike-article-id:564312 TI - Discovering Spatio-Temporal Models of the Spread of West Nile Virus JF - Risk Analysis VL - 26 IS - 2 SP - 413 EP - 422 PB - Blackwell Publishing SN - 0272-4332 N2 - Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment.(1-3) Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states.(4) The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts.(5) Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002. KW - bayes KW - machine-learning KW - modeling KW - time-series KW - west-nile AU - Orme-Zavaleta, Jennifer AU - Jorgensen, Jane AU - D'Ambrosio, Bruce AU - Altendorf, Eric AU - Rossignol, Philippe PY - 2006/04// UR - http://www.blackwell-synergy.com/doi/abs/10.1111/j.1539-6924.2006.00738.x ER -