Infectious Disease Research in the Era of Big Data

被引：4

作者：

Kasson, Peter M. ^{[1
,2
,3
]}

机构：

[1] Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA

[2] Univ Virginia, Dept Mol Physiol, Charlottesville, VA 22908 USA

[3] Uppsala Univ, Dept Cell & Mol Biol, Sci Life Lab, S-75237 Uppsala, Sweden

来源：

ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 3, 2020 | 2020年 / 3卷

关键词：

big data; infectious disease; personalized medicine; transmission modeling; REAL-TIME; SYNDROMIC SURVEILLANCE; VIRUS TRANSMISSION; HOST FACTORS; INFLUENZA; RESISTANCE; SPREAD; GENES; SUSCEPTIBILITY; DISCOVERY;

D O I：

10.1146/annurev-biodatasci-121219-025722

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Infectious disease research spans scales from the molecular to the global-from specific mechanisms of pathogen drug resistance, virulence, and replication to themovement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

引用

页码：43 / 59

页数：17

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