Towards bioinformatics assisted infectious disease control.
BACKGROUND
This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining.


RESULTS
This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration.


CONCLUSIONS
The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.
Sintchenko
Vitali
V
Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia. vsintchenko@usyd.edu.au
Gallego
Blanca
B
Chung
Grace
G
Coiera
Enrico
E
D003140:Communicable Disease Control; D019295:Computational Biology / Q000379:methods*; D004196:Disease Outbreaks / Q000517:prevention & control*; D020869:Gene Expression Profiling / Q000379:methods; D016680:Genome, Bacterial; D005838:Genotype; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics / Q000379:methods; D012486:Salmonella typhimurium / Q000235:genetics / Q000472:pathogenicity