Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pulmonary Tuberculosis IV

Pulmonary Tuberculosis IV

Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...

Pulmonary Tuberculosis I

Pulmonary Tuberculosis I

Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Pulmonary Tuberculosis III

Pulmonary Tuberculosis III

Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:

Pulmonary Tuberculosis II

Pulmonary Tuberculosis II

Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...

Pulmonary Tuberculosis V

Pulmonary Tuberculosis V

Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comparative evaluation of SARS-CoV-2 laboratory testing data sources for U.S. COVID-19 public health surveillance, 2020-2024.

Frontiers in public health·2026

Same author

Time to Progress Through the Latent Tuberculosis Infection Care Cascade in Primary Care.

Public health reports (Washington, D.C. : 1974)·2026

Same author

Characteristics of Plausible Source Cases Responsible for Recent Mycobacterium tuberculosis Transmission, United States, 2018-2022.

Emerging infectious diseases·2026

Same author

Large Tuberculosis Outbreaks - United States, 2017-2023.

MMWR. Morbidity and mortality weekly report·2026

Same author

Associations of exposure to ambient PM<sub>2.5</sub> and its major components with pulmonary tuberculosis in central and southwestern China: a spatiotemporal modeling study.

BMC public health·2026

Same author

Latent tuberculosis infection care cascade outcomes in primary care clinics in the tuberculosis epidemiologic studies consortium-III.

Annals of epidemiology·2026

Search research articles

Related Experiment Video

Updated: Oct 29, 2025

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Published on: September 5, 2017

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.

Kathryn Winglee¹, Clinton J McDaniel¹, Lauren Linde²

¹Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.

Frontiers in Public Health

|July 8, 2021

Summary

This summary is machine-generated.

The Logically Inferred Tuberculosis Transmission (LITT) algorithm automates tuberculosis (TB) source case identification. This tool aids public health staff in preventing TB spread by systematically analyzing complex data during investigations.

Keywords:

cluster investigation genomic epidemiology transmission tuberculosis whole-genome sequencing

More Related Videos

Matrix-based DNA Extraction for Targeted Next-Generation Sequencing on Decontaminated Sputum Samples

Matrix-based DNA Extraction for Targeted Next-Generation Sequencing on Decontaminated Sputum Samples

Published on: June 6, 2025

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

Related Experiment Videos

Last Updated: Oct 29, 2025

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Published on: September 5, 2017

Matrix-based DNA Extraction for Targeted Next-Generation Sequencing on Decontaminated Sputum Samples

Matrix-based DNA Extraction for Targeted Next-Generation Sequencing on Decontaminated Sputum Samples

Published on: June 6, 2025

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

Area of Science:

Public Health
Infectious Disease Epidemiology
Bioinformatics

Background:

Tuberculosis (TB) transmission chains are crucial for targeted public health interventions.
Current methods for identifying TB transmission lack automation, hindering efficient resource allocation.
Manual cluster investigations are time-consuming and complex.

Purpose of the Study:

To develop and evaluate an automated algorithm for identifying tuberculosis transmission chains.
To systematize the integration of whole-genome sequencing, clinical, and epidemiological data.
To assist public health staff in pinpointing TB source cases and preventing further transmission.

Main Methods:

Development of the Logically Inferred Tuberculosis Transmission (LITT) algorithm.
Integration of whole-genome sequencing, clinical, and epidemiological data.
Evaluation of LITT performance on 534 cases across 56 TB clusters in three U.S. jurisdictions.

Main Results:

LITT successfully identified and ranked potential source cases for TB clusters.
High agreement (80%) was observed between LITT and human investigators in identifying the most likely source case.
Discrepancies often stemmed from data errors, highlighting the algorithm's sensitivity.

Conclusions:

The LITT algorithm offers a systematic approach to analyzing complex TB data.
LITT enhances the efficiency of TB cluster investigations and aids in preventing transmission.
Accessible tools, including a GUI and training, were developed to support frontline staff.