Predicting seasonal influenza using supermarket retail records
This research provides an incremental method for public health officials to forecast seasonal influenza by leveraging readily available retail data.
This paper explores the use of supermarket retail data, specifically "sentinel baskets" of co-purchased products, as a novel proxy for seasonal influenza incidence. The developed nowcasting and forecasting framework, using Support Vector Regression, provides estimates for influenza incidence in Italy up to 4 weeks ahead, outperforming autoregressive and product-purchase baselines.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.