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.
Pattern Recognit Lett
Mining aggregates of over-the-counter products for syndromic surveillance.