tetano
Editor, Senior Moderator
PLoS Comput Biol
. 2021 Jul 12;17(7):e1009087.
doi: 10.1371/journal.pcbi.1009087. Online ahead of print.
Predicting seasonal influenza using supermarket retail records
Ioanna Miliou[SUP] 1 2 [/SUP], Xinyue Xiong[SUP] 3 [/SUP], Salvatore Rinzivillo[SUP] 2 [/SUP], Qian Zhang[SUP] 3 [/SUP], Giulio Rossetti[SUP] 2 [/SUP], Fosca Giannotti[SUP] 2 [/SUP], Dino Pedreschi[SUP] 1 [/SUP], Alessandro Vespignani[SUP] 3 [/SUP]
Affiliations
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.
. 2021 Jul 12;17(7):e1009087.
doi: 10.1371/journal.pcbi.1009087. Online ahead of print.
Predicting seasonal influenza using supermarket retail records
Ioanna Miliou[SUP] 1 2 [/SUP], Xinyue Xiong[SUP] 3 [/SUP], Salvatore Rinzivillo[SUP] 2 [/SUP], Qian Zhang[SUP] 3 [/SUP], Giulio Rossetti[SUP] 2 [/SUP], Fosca Giannotti[SUP] 2 [/SUP], Dino Pedreschi[SUP] 1 [/SUP], Alessandro Vespignani[SUP] 3 [/SUP]
Affiliations
- PMID: 34252075
- DOI: 10.1371/journal.pcbi.1009087
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.