Detecting linkages between human illness and Salmonella isolates in food using a new tool for spatio-temporal analysis of multi-stream data.
We present a prototype tool designed to enable computationally efficient visualization of data and its spatio-temporal analysis by food safety and public health investigators. Its utility is evaluated in the following contexts: (1) Investigation of relationships between cases of Salmonella related human illness and Salmonella positives in meat and poultry products at USDA regulated establishments; (2) Identification and detection of patterns in food safety data which may impact public health.
Chen
Lujie
L
Auton Lab, Carnegie Mellon Univeristy, Pittsburgh, PA, USA.
Dubrawski
Artur W
AW
Ray
Saswati
S
Baysek
Michael
M
Kelley
Lynda
L
Dunham
Adrienne
A
Huckabee
Mark
M
Fedorka-Cray
Paula J
PJ
Jackson
Charlene
C
McGlinchey
Beth
B
D000465:Algorithms; D001185:Artificial Intelligence; D005506:Food Contamination / Q000706:statistics & numerical data*; D005516:Food Microbiology; D016247:Information Storage and Retrieval / Q000379:methods; D008498:Medical Record Linkage / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D011159:Population Surveillance / Q000379:methods*; D012478:Salmonella Food Poisoning / Q000175:diagnosis* / Q000453:epidemiology*; D012984:Software; D014481:United States; D014584:User-Computer Interface