Dynamic network model for predicting occurrences of Salmonella at food facilities

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5354 LNBI, Issue , 2008, Pages 56-63

  Sarkar, Purnamrita ^a   Chen, Lujie ^a   Dubrawski, Artur ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Food safety surveillance; Latent space models; Link analysis; Risk based inspection; Social networks

Indexed keywords

DYNAMIC ANALYSIS; FOOD PROCESSING; HEALTH RISKS; RISK ASSESSMENT; STRAIN;

FOOD SAFETY SURVEILLANCE; LATENT SPACE MODELS; LINK ANALYSIS; RISK BASED INSPECTION; SOCIAL NETWORKS;

RISK ANALYSIS;

EID: 58549116699     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89746-0_6     Document Type: Conference Paper

References (12)

1. Borg, I., Groenen, P.: Modern multidimensional scaling. Springer, Heidelberg (1997)
2. Breiger, R.L., Boorman, S.A., Arabie, P.: An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. J. of Math. Psych. 12, 328-383 (1975)
3. CDC. Centers for Disease Control and Prevention: General information on Salmonellosis (2008), http://www.cdc.gov/ncidod/dbmd/diseaseinfo/salmonellosisg. htm
4. Dubrawski, A., Chen, L., Ostlund, J.: Using AFDL algorithm to estimate risk of positive outcomes of microbial tests at food establishments. Advances in Disease Surveillance 5 (2008)
5. FSIS. Food Safety Inspection Service, U.S. Department of Agriculture: Data analysis for public health risk-based inspection system for processing and slaughter. Appendix E - Data analyses (2008), http://www.fsis.usda.gov/oppde/ nacmpi/ feb2008/processingappendix-e-041808.pdf
6. Madadhain, J., Smyth, P.: Learning predictive models for link formation. In: Sunbelt 25: International Sunbelt Social Network Conference (2005)
7. Preparata, F., Shamos, M.: Computational geometry: An introduction. Springer, Heidelberg (1985)
8. Raftery, A.E., Handcock, M.S., Hoff, P.D.: Latent space approaches to social network analysis. J. Amer. Stat. Assoc. 15, 460 (2002)
9. Reis, B.Y., Kohane, I.S., Mandl, K.D.: An Epidemiological Network Model for Disease Outbreak Detection. PLoS Med. (2007)
10. Roure, J., Dubrawski, A., Schneider, J.: Learning specific detectors of adverse events in multivariate time series. Advances in Disease Surveillance 4 (2007a)
11. Roure, J., Dubrawski, A., Schneider, J.: A study into detection of bioevents in multiple streams of surveillance data. In: Zeng, D., Gotham, I., Komatsu, K., Lynch, C., Thurmond, M., Madigan, D., Lober, B., Kvach, J., Chen, H. (eds.) BioSurveillance 2007. LNCS, vol. 4506, pp. 124-133. Springer, Heidelberg (2007b)
12. Sarkar, P., Moore, A.: Dynamic social network analysis using latent space models. In: Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NIPS) (2005)