Mining aggregates of over-the-counter products for syndromic surveillance

Authors:
Aurel Cami;Garrick L. Wallstrom;Ashley L. Fowlkes;Cathy A. Panozzo;William R. Hogan
Affiliations:
Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Bldg, 200 Meyran Ave., Pittsburgh, PA 15260, United States;Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Bldg, 200 Meyran Ave., Pittsburgh, PA 15260, United States;Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States;Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States;Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Bldg, 200 Meyran Ave., Pittsburgh, PA 15260, United States
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

There has recently been a surge of research efforts aimed at very early detection of disease outbreaks. An important strategy for improving the timeliness of outbreak detection is to identify signals that occur early in the epidemic process. We have developed a novel algorithm to identify aggregates of ''similar'' over-the-counter products that have strong association with a given disease. This paper discusses the proposed algorithm and reports the results of an evaluation experiment. The experimental results show that this algorithm holds promise for discovering product aggregates with outbreak detection performance that is superior to that of predefined categories. We also found that the products extracted by the proposed algorithm were more strongly correlated with the disease data than the standard predefined product categories, while also being more strongly correlated with each other than the products in any predefined category.