ArticlePLoS computational biology2021
Predicting seasonal influenza using supermarket retail records.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Shopping Data for Population Health Surveillance: Opportunities, Challenges, and Future Directions.Journal of medical Internet research · 2025Article
- Integrating information from historical data into mechanistic models for influenza forecasting.PLoS computational biology · 2024Article
- Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.Journal of the American College of Emergency Physicians open · 2024Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- Crime, inequality and public health: a survey of emerging trends in urban data science.Frontiers in big data · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.