Predicting seasonal influenza using supermarket retail records

TL;DR

This paper introduces a novel approach to influenza forecasting by leveraging supermarket retail data as a proxy signal, demonstrating improved prediction accuracy for Italy's seasonal flu up to four weeks ahead.

Contribution

The study presents a new method using retail market data and SVR models to enhance real-time influenza forecasting accuracy.

Findings

Retail data improves flu prediction accuracy

SVR outperforms baseline models

Retail data acts as an effective epidemic proxy

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.