Abstract
Timely detection of emerging disease outbreaks is critical for effective public health response. Traditional surveillance systems often rely on clinical or laboratory-confirmed data, which may delay early detection. In this study, we evaluate the potential of the National Retail Data Monitor (NRDM), which tracks over-the-counter (OTC) health-related product sales, as a tool for public health surveillance. Using data from Allegheny County, Pennsylvania (2016-2021), we developed a probabilistic model that estimates daily influenza-like illness (ILI) activity based on purchasing patterns. To train the model, we applied an expectation-maximization (EM) algorithm to estimate the conditional probabilities of OTC product purchases given ILI or not ILI, which forms the basis of our model. To test the model, we used that model and daily OTC sales to estimate the number of purchases each day due to ILI. The estimated ILI counts showed moderately strong correlation (r = 0.66) with ILI counts derived from emergency departments (EDs) in the same region. We also monitor the extent to which our model predicts the data well; if the current prediction is poor, relative to historical predictions, it raises the prospect that there is an outbreak of an unusual disease in the population, perhaps even a novel disease. Our findings suggest that surveillance of OTC products can effectively identify unusual health-related activity and provide early insights of potential known and novel outbreaks through changes in consumer purchasing behavior.