Predicting seasonal influenza using supermarket retail records

Abstract

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.

arXiv comment17 pages, 2 figures, 4 tables (1 in appendix), 1 algorithm, submitted to PLOS Computational Biology

References in corpus (6)

• Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
• Accurate estimation of influenza epidemics using Google search data via ARGO
• Prediction of infectious disease epidemics via weighted density ensembles
• Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs
• Predicting the Flu from Instagram
• Nowcasting Influenza Incidence with CDC Web Traffic Data: A Demonstration Using a Novel Data Set