Predicting seasonal influenza using supermarket retail records.

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...

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Autores principales:	Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/8e9b306957374f1390ceb8a02876e4d8
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id	oai:doaj.org-article:8e9b306957374f1390ceb8a02876e4d8
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spelling	oai:doaj.org-article:8e9b306957374f1390ceb8a02876e4d82021-12-02T19:57:33ZPredicting seasonal influenza using supermarket retail records.1553-734X1553-735810.1371/journal.pcbi.1009087https://doaj.org/article/8e9b306957374f1390ceb8a02876e4d82021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009087https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Increased 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.Ioanna MiliouXinyue XiongSalvatore RinzivilloQian ZhangGiulio RossettiFosca GiannottiDino PedreschiAlessandro VespignaniPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009087 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ioanna Miliou Xinyue Xiong Salvatore Rinzivillo Qian Zhang Giulio Rossetti Fosca Giannotti Dino Pedreschi Alessandro Vespignani Predicting seasonal influenza using supermarket retail records.
description	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.
format	article
author	Ioanna Miliou Xinyue Xiong Salvatore Rinzivillo Qian Zhang Giulio Rossetti Fosca Giannotti Dino Pedreschi Alessandro Vespignani
author_facet	Ioanna Miliou Xinyue Xiong Salvatore Rinzivillo Qian Zhang Giulio Rossetti Fosca Giannotti Dino Pedreschi Alessandro Vespignani
author_sort	Ioanna Miliou
title	Predicting seasonal influenza using supermarket retail records.
title_short	Predicting seasonal influenza using supermarket retail records.
title_full	Predicting seasonal influenza using supermarket retail records.
title_fullStr	Predicting seasonal influenza using supermarket retail records.
title_full_unstemmed	Predicting seasonal influenza using supermarket retail records.
title_sort	predicting seasonal influenza using supermarket retail records.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8e9b306957374f1390ceb8a02876e4d8
work_keys_str_mv	AT ioannamiliou predictingseasonalinfluenzausingsupermarketretailrecords AT xinyuexiong predictingseasonalinfluenzausingsupermarketretailrecords AT salvatorerinzivillo predictingseasonalinfluenzausingsupermarketretailrecords AT qianzhang predictingseasonalinfluenzausingsupermarketretailrecords AT giuliorossetti predictingseasonalinfluenzausingsupermarketretailrecords AT foscagiannotti predictingseasonalinfluenzausingsupermarketretailrecords AT dinopedreschi predictingseasonalinfluenzausingsupermarketretailrecords AT alessandrovespignani predictingseasonalinfluenzausingsupermarketretailrecords
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Predicting seasonal influenza using supermarket retail records.

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