@article{PAR00013498,
  title = {{R}eal-time microbiology laboratory surveillance system to detect abnormal events and emerging infections, {M}arseille, {F}rance},
  author = {{A}bat, {C}. and {C}haudet, {H}. and {C}olson, {P}. and {R}olain, {J}. {M}. and {R}aoult, {D}idier},
  editor = {},
  language = {{ENG}},
  abstract = {{I}nfectious diseases are a major threat to humanity, and accurate surveillance is essential. {W}e describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. {T}wo historical {M}icrosoft {E}xcel databases were implemented. {T}he data were then sorted and used to execute the following 2 surveillance systems in {E}xcel: the {B}acterial real-time {L}aboratory-based {S}urveillance {S}ystem ({BALYSES}) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the {M}arseille {A}ntibiotic {R}esistance {S}urveillance {S}ystem ({MARSS}), which surveys the primary beta-lactam resistance phenotypes for 15 selected bacterial species. {T}he first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. {F}rom {M}ay 21, 2013, through {J}une 4, 2014, {BALYSES} and {MARSS} enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. {T}his system is currently being refined and improved.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{E}merging {I}nfectious {D}iseases},
  volume = {21},
  numero = {8},
  pages = {1302--1310},
  ISSN = {1080-6040},
  year = {2015},
  DOI = {10.3201/eid2108.141419},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00013498},
}