Related Experiment Video

Updated: Dec 6, 2025

DNA Fingerprinting of Mycobacterium leprae Strains Using Variable Number Tandem Repeat VNTR - Fragment Length Analysis FLA
09:39

DNA Fingerprinting of Mycobacterium leprae Strains Using Variable Number Tandem Repeat VNTR - Fragment Length Analysis FLA

Published on: July 15, 2011

27.6K

Leprosy in Brazil in the 21st century: analysis of epidemiological and operational indicators using inflection point

Carlos Dornels Freire de Souza1, João Paulo Silva de Paiva1, Thiago Cavalcanti Leal1

  • 1Department of Medicine, Universidade Federal de Alagoas, Campus Arapiraca, Arapiraca, AL, Brazil.

Anais Brasileiros De Dermatologia
|October 4, 2020
PubMed

Keywords:
LeprosyMycobacterium lepraeTime series studies

More Related Videos

Optimized Protocols for Mycobacterium leprae Strain Management: Frozen Stock Preservation and Maintenance in Athymic Nude Mice10:32

Optimized Protocols for Mycobacterium leprae Strain Management: Frozen Stock Preservation and Maintenance in Athymic Nude Mice

Published on: March 23, 2014

15.5K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Related Concept Videos

Steps in Outbreak Investigation01:18

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:
394
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
770
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.3K
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.4K
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
3.0K