PLoS Computational Biology (Jul 2021)

Predicting seasonal influenza using supermarket retail records.

  • Ioanna Miliou,
  • Xinyue Xiong,
  • Salvatore Rinzivillo,
  • Qian Zhang,
  • Giulio Rossetti,
  • Fosca Giannotti,
  • Dino Pedreschi,
  • Alessandro Vespignani

Journal volume & issue
Vol. 17, no. 7
p. e1009087


Read online

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.