Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Althomsons, Sandy P. ; Hill, Andrew N. ; Harrist, Alexia V. ; France, Anne Marie ; Powell, Krista M. ; Posey, James E. ; Cowan, Lauren S. ; Navin, Thomas R.
• Description:
  We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.
• Subjects:
  Cluster Analysis Disease Outbreaks Dispatch Genome, Bacterial Genomics/methods Genotype Humans Incidence Models, Statistical Molecular Epidemiology Mycobacterium Tuberculosis/genetics Polymorphism, Single Nucleotide Prevalence Tuberculosis/diagnosis Tuberculosis/epidemiology Tuberculosis/microbiology

  More +
• Source:
  Emerg Infect Dis. 24(3):573-575.
• Pubmed ID:
  29460749
• Pubmed Central ID:
  PMC5823347
• Document Type:
  Journal Article
• Place as Subject:
  United States
• Location:
  North America
• Volume:
  24
• Issue:
  3
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:cfc8ce8a4b2b73e4fd4b896461e5964c3e8e49955a670b8078c45a5fa22fa1cf
• Download URL:
  https://stacks.cdc.gov/view/cdc/52196/cdc_52196_DS1.pdf
• File Type:
  [PDF - 508.04 KB ]