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Steps in Outbreak Investigation01:18

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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:
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Pulmonary Tuberculosis I01:29

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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.
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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.
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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.
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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.
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Separation and Fractionation of Cell Wall and Cell Membrane Proteins from Mycobacterium tuberculosis for Downstream Protein Analysis
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Tuberculosis outbreak investigation using phylodynamic analysis.

Denise Kühnert1, Mireia Coscolla2, Daniela Brites2

  • 1Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Zürich, Switzerland; Institute of Medical Virology, University of Zürich, Zürich, Switzerland; Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland; Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.

Epidemics
|June 9, 2018
PubMed
Summary
This summary is machine-generated.

Phylodynamic methods reveal insights into Mycobacterium tuberculosis outbreaks. High sampling proportions improve epidemiological parameter estimation, even with limited genetic variation in slowly evolving pathogens.

Keywords:
Epidemic dynamicsPhylodynamic analysisTransmission dynamicsTuberculosis outbreak

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Area of Science:

  • Epidemiology
  • Genomics
  • Evolutionary biology

Background:

  • Phylodynamic approaches are effective for rapidly evolving viruses.
  • Advances in whole genome sequencing enable application to slowly evolving bacteria like Mycobacterium tuberculosis.
  • Understanding outbreak dynamics is crucial for public health interventions.

Purpose of the Study:

  • To investigate and compare epidemiological dynamics of two Mycobacterium tuberculosis outbreaks using phylodynamic methods.
  • To assess the feasibility of estimating short-term evolutionary rates and epidemiological parameters for M. tuberculosis.
  • To evaluate the impact of sampling proportion and temporal signal on phylodynamic analyses.

Main Methods:

  • Phylodynamic analysis of two M. tuberculosis outbreaks (Bern, Switzerland and Wat Tham Krabok refugee camp, Thailand).
  • Investigation of genetic variation for estimating evolutionary rates.
  • Reconstruction of epidemiological parameters including the effective reproduction number.
  • Comparison of outbreaks with varying temporal signals and sampling proportions.

Main Results:

  • Limited temporal signal was observed in the Bern outbreak data, while moderate signal was found in the Wat Tham Krabok (WTK) outbreak data.
  • Despite poor temporal signal, the Bern outbreak (90% sampling) allowed robust estimation of epidemiological parameters.
  • The WTK outbreak (9% sampling) showed significant uncertainty in epidemiological estimates.
  • Both outbreaks likely peaked around 1990, with longer infection periods in the WTK outbreak (approx. 9 years) compared to Bern (4-5 years).

Conclusions:

  • Estimating evolutionary rates on outbreak time scales for slowly evolving pathogens like M. tuberculosis is challenging.
  • High sampling proportions are critical for robust phylodynamic analysis and accurate estimation of outbreak epidemiological parameters.
  • Phylodynamic methods offer valuable insights into M. tuberculosis outbreaks, highlighting the importance of data completeness.