Using a distributed search engine to identify optimal product sets for use in an outbreak detection system


This study tests an approach for identifying sets of over-the-counter (OTC) thermometer products whose aggregate sales correlate optimally with aggregate counts of emergency department (ED) visits where patients have symptoms consistent with Constitutional syndrome such as fever and chills. We show that by using a distributed search engine alongside search algorithms (Brute-force), we can quickly identify a minimum set of OTC thermometer products whose sales are optimally correlated to the ED data. We used the Pearson correlation coefficient function to measure the degree of correlation between OTC and ED time series. The optimal OTC product set-comprising 9 thermometer products found by the Brute-force algorithm-has a correlation coefficient value of 0.96. We believe the approach used in this study can be used to efficiently identify different optimal OTC sets for detection of different types of disease outbreaks.

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