How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

Nicola Luigi Bragazzi, Haijiang Dai, Giovanni Damiani, Masoud Behzadifar, Mariano Martini and Jianhong Wu
Additional contact information
Nicola Luigi Bragazzi: Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
Haijiang Dai: Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
Giovanni Damiani: Department of Dermatology, Case Western Reserve University, Cleveland, OH 44195, USA
Masoud Behzadifar: Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad 6813833946, Iran
Mariano Martini: Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy
Jianhong Wu: Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada

IJERPH, 2020, vol. 17, issue 9, 1-8

Abstract: SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

Keywords: viral outbreak; public health; epidemiology; artificial intelligence; Big Data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/9/3176/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/9/3176/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:9:p:3176-:d:353387

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().