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Related Experiment Video

Updated: May 13, 2026

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray

Published on: April 25, 2014

Using statistical methods and genotyping to detect tuberculosis outbreaks.

J Steve Kammerer¹, Nong Shang, Sandy P Althomsons

¹Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA 30333, USA. fzk3@cdc.gov

International Journal of Health Geographics

|March 19, 2013

Summary

This summary is machine-generated.

Statistical methods can detect tuberculosis outbreaks months earlier than public health officials. This early detection of infectious disease outbreaks is crucial for reducing disease burden in the U.S.

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The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

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Last Updated: May 13, 2026

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray

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Published on: April 25, 2014

The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

Published on: August 11, 2008

Area of Science:

Epidemiology
Public Health Surveillance
Statistical Modeling

Background:

Early identification of infectious disease outbreaks is critical for reducing disease burden.
Previous research utilized statistical methods for spatial or temporal disease detection.

Purpose of the Study:

To assess the effectiveness and timeliness of three spatio-temporal statistical methods in detecting known tuberculosis outbreaks.
Evaluate the potential for early outbreak detection using routinely collected data.

Main Methods:

Retrospective analysis of routinely available molecular and surveillance data.
Application of three statistical methods: county-based log-likelihood ratio, cumulative sums, and spatial scan statistic.
Comparison of detection times with local public health authority identification.

Main Results:

Eight out of nine known tuberculosis outbreaks were identified by the statistical methods.
Six outbreaks were detected 1-52 months (median 10 months) earlier than by public health authorities.
Nearly 60% of patients in the studied outbreaks were identified after statistical detection but before public health awareness.

Conclusions:

Routinely collected tuberculosis data, when analyzed with statistical methods, can enable early detection of outbreaks, potentially months ahead of public health awareness.
The three evaluated spatio-temporal methods demonstrated comparable performance in outbreak detection.
Further prospective analysis is warranted to fully elucidate the performance of these methods for tuberculosis outbreak detection.