Using a neural network for mining interpretable relationships of West Nile risk factors

Social Science and Medicine

Volumn 72, Issue 3, 2011, Pages 418-429

  Ghosh, Debarchana ^a   Guha, Rajarshi ^b  

^a KENT STATE UNIVERSITY   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Neural network; Nonlinear; Risk factors; Urban morphology; USA; West Nile virus

Indexed keywords

INSECTICIDE;

ARTIFICIAL NEURAL NETWORK; DISEASE PREVALENCE; DISEASE SPREAD; LAND COVER; METROPOLITAN AREA; MOSQUITO; RISK FACTOR;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIRD; DATA MINING; ENVIRONMENTAL SANITATION; ENVIRONMENTAL TEMPERATURE; GEOGRAPHY; HOUSING; INCIDENCE; INFECTION PREVENTION; INFECTION RISK; LAKE; LAND USE; METHODOLOGY; MOSQUITO; POPULATION DENSITY; RISK FACTOR; UNITED STATES; VECTOR CONTROL; WEST NILE FLAVIVIRUS; ANIMAL; ANIMAL DISEASE; COMPUTER SIMULATION; ENVIRONMENT; ENVIRONMENTAL PLANNING; EPIDEMIC; HEALTH CARE POLICY; HUMAN; SOCIOECONOMICS; VIROLOGY; WEST NILE FEVER;

MINNESOTA; UNITED STATES;

ANIMALIA; AVES; MOSQUITO-BORNE VIRUSES; WEST NILE VIRUS;

ANIMALS; BIRDS; COMPUTER SIMULATION; DATA MINING; DISEASE OUTBREAKS; ENVIRONMENT; ENVIRONMENT DESIGN; HEALTH POLICY; HUMANS; INCIDENCE; MINNESOTA; MOSQUITO CONTROL; NEURAL NETWORKS (COMPUTER); RISK FACTORS; SOCIOECONOMIC FACTORS; WEST NILE FEVER;

EID: 79953003306     PISSN: 02779536     EISSN: 18735347     Source Type: Journal    
DOI: 10.1016/j.socscimed.2010.09.014     Document Type: Article

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