Supersize me: How whole-genome sequencing and big data are transforming epidemiology

Trends in Microbiology

Volumn 22, Issue 5, 2014, Pages 282-291

  Kao, Rowland R ^a   Haydon, Daniel T ^a   Lycett, Samantha J ^a   Murcia, Pablo R ^b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Bayesian inference; Forensic epidemiology; Mathematical modeling; Pathogen evolution; Who infected whom?

Indexed keywords

BOVINE TUBERCULOSIS; DISEASE TRANSMISSION; EPIDEMIC; EPIDEMIOLOGY; EVOLUTIONARY HOMOLOGY; FOOT AND MOUTH DISEASE; FORENSIC SCIENCE; GENE AMPLIFICATION; GENE MUTATION; GENE SEQUENCE; GENE TRANSFER; GENETIC DISTANCE; HIGH RISK POPULATION; HIGH THROUGHPUT SEQUENCING; INCUBATION TIME; INFECTION; INFECTION CONTROL; MULTILOCUS SEQUENCE TYPING; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PRIORITY JOURNAL; REVIEW; RNA VIRUS; BIOSTATISTICS; COMMUNICABLE DISEASE; CONTACT EXAMINATION; FORENSIC MEDICINE; GENOMICS; HEALTH; MOLECULAR EPIDEMIOLOGY; PROCEDURES; TRANSMISSION;

BIOSTATISTICS; COMMUNICABLE DISEASES; CONTACT TRACING; FORENSIC MEDICINE; GENOMICS; GLOBAL HEALTH; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; MOLECULAR EPIDEMIOLOGY;

EID: 84899969725     PISSN: 0966842X     EISSN: 18784380     Source Type: Journal    
DOI: 10.1016/j.tim.2014.02.011     Document Type: Review

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