Journal article  Open Access

Predicting seasonal influenza using supermarket retail records

Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.

Forecasting  Time series  Influenza 

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 realtime 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.

Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087

Publisher: Public Library of Science,, San Francisco, CA , Stati Uniti d'America

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BibTeX entry
	title = {Predicting seasonal influenza using supermarket retail records},
	author = {Miliou I. and Xiong X. and Rinzivillo S. and Zhang Q. and Rossetti G. and Giannotti F. and Pedreschi D. and Vespignani A.},
	publisher = {Public Library of Science,, San Francisco, CA , Stati Uniti d'America},
	doi = {10.1371/journal.pcbi.1009087},
	journal = {PLoS computational biology},
	volume = {17},
	year = {2021}