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Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.基于众包数据的 2019 年冠状病毒病早期流行病学分析：人群水平观察研究。
Lancet Digit Health. 2020 Apr;2(4):e201-e208. doi: 10.1016/S2589-7500(20)30026-1. Epub 2020 Feb 20.
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COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.2019冠状病毒病与人工智能：保护医护人员并遏制传播
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Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.公共卫生干预下中国新冠疫情趋势的改进型SEIR模型及人工智能预测
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Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease-19 Self-Testing.用于新型冠状病毒肺炎自我检测的区块链与人工智能技术
Diagnostics (Basel). 2020 Apr 1;10(4):198. doi: 10.3390/diagnostics10040198.
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Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.利用社交媒体搜索索引预测 2019 年新型冠状病毒（COVID-19）病例数。
Int J Environ Res Public Health. 2020 Mar 31;17(7):2365. doi: 10.3390/ijerph17072365.
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Corona Virus (COVID-19) "Infodemic" and Emerging Issues through a Data Lens: The Case of China.冠状病毒（COVID-19）“信息疫情”与数据视角下的新问题：以中国为例。
Int J Environ Res Public Health. 2020 Mar 30;17(7):2309. doi: 10.3390/ijerph17072309.
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The Novel Coronavirus - A Snapshot of Current Knowledge.新型冠状病毒——当前知识要点概览。
Microb Biotechnol. 2020 May;13(3):607-612. doi: 10.1111/1751-7915.13557. Epub 2020 Mar 6.
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The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China.致命的冠状病毒：2003 年 SARS 大流行和 2020 年中国新型冠状病毒疫情。
J Autoimmun. 2020 May;109:102434. doi: 10.1016/j.jaut.2020.102434. Epub 2020 Mar 3.
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Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks.鉴于过去人类冠状病毒爆发情况洞察2019新型冠状病毒（SARS-CoV-2）
Pathogens. 2020 Mar 4;9(3):186. doi: 10.3390/pathogens9030186.
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Novel Coronavirus 2019 (Sars-CoV2): a global emergency that needs new approaches?2019新型冠状病毒（严重急性呼吸综合征冠状病毒2）：一场需要新应对方法的全球紧急情况？
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大数据和人工智能如何帮助更好地管理 COVID-19 大流行。

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

机构信息

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.

Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy.

出版信息

Int J Environ Res Public Health. 2020 May 2;17(9):3176. doi: 10.3390/ijerph17093176.

DOI:10.3390/ijerph17093176
PMID:32370204
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7246824/
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.

摘要

SARS-CoV2 是一种新型冠状病毒，是世界卫生组织宣布的 COVID-19 大流行的罪魁祸首。得益于分子和计算技术以及信息和通信技术（ICT）领域的最新进展，人工智能（AI）和大数据可以帮助处理来自公共卫生监测、实时疫情监测、趋势预测/预测、政府机构和机构的定期情况简报和更新以及卫生机构利用信息的巨大、前所未有的大量数据。本综述旨在概述人工智能和大数据在全球管理大流行方面的潜在应用。