Back to Search
Start Over
Predicting seasonal influenza using supermarket retail records
- Source :
- PLoS Computational Biology, PLOS Computational Biology, PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087, info:cnr-pdr/source/autori:Miliou I.; Xiong X.; Rinzivillo S.; Zhang Q.; Rossetti G.; Giannotti F.; Pedreschi D.; Vespignani A./titolo:Predicting seasonal influenza using supermarket retail records/doi:10.1371%2Fjournal.pcbi.1009087/rivista:PLoS computational biology/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume:17, PLoS Computational Biology, Vol 17, Iss 7, p e1009087 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.<br />Comment: 17 pages, 2 figures, 4 tables (1 in appendix), 1 algorithm, submitted to PLOS Computational Biology
- Subjects :
- FOS: Computer and information sciences
RNA viruses
Computer Science - Machine Learning
Viral Diseases
020205 medical informatics
Nowcasting
Economics
Epidemiology
Computer science
Social Sciences
02 engineering and technology
medicine.disease_cause
Geographical locations
Proxy (climate)
Machine Learning (cs.LG)
Machine Learning
Seasonal influenza
Medical Conditions
Mathematical and Statistical Techniques
Medicine and Health Sciences
0202 electrical engineering, electronic engineering, information engineering
Econometrics
Influenza A virus
Biology (General)
Supermarkets
health care economics and organizations
Pathology and laboratory medicine
0303 health sciences
education.field_of_study
Settore INF/01 - Informatica
Ecology
Incidence
Statistics
Commerce
Computer Science - Social and Information Networks
Medical microbiology
Europe
Infectious Diseases
Italy
Computational Theory and Mathematics
Autoregressive model
Computational Biology
Consumer Behavior
Humans
Influenza, Human
Seasons
Modeling and Simulation
Physical Sciences
Viruses
Pathogens
Human
Research Article
Computer and Information Sciences
Time series
Infectious Disease Control
QH301-705.5
Computer Science - Artificial Intelligence
Population
Disease Surveillance
Research and Analysis Methods
Microbiology
03 medical and health sciences
Cellular and Molecular Neuroscience
Artificial Intelligence
Genetics
medicine
Influenza viruses
European Union
Statistical Methods
education
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Social and Information Networks (cs.SI)
Biology and life sciences
Retail
Organisms
Viral pathogens
Influenza
Microbial pathogens
Support vector machine
Artificial Intelligence (cs.AI)
Infectious Disease Surveillance
Season
People and places
Mathematics
Forecasting
Orthomyxoviruses
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....cb8d87c3192a47e6cba2b82e49c10d69