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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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Infection

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Related Experiment Video

Updated: May 10, 2026

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification
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Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification

Published on: March 29, 2024

Introducing the outbreak threshold in epidemiology.

Matthew Hartfield1, Samuel Alizon

  • 1Laboratoire MIVEGEC, UMR CNRS 5290, IRD 224, UM1, UM2, Montpellier, France. matthew.hartfield@ird.fr

Plos Pathogens
|June 21, 2013
PubMed
Summary
This summary is machine-generated.

A new outbreak threshold (T₀) helps predict when rare pathogens may cause major epidemics. This threshold indicates the number of infected individuals needed to overcome stochastic fade-out and initiate widespread outbreaks.

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High-throughput Detection Method for Influenza Virus
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High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

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Last Updated: May 10, 2026

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification
06:11

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification

Published on: March 29, 2024

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Dynamics

Background:

  • Rare pathogens may disappear due to random chance (stochastic effects) rather than causing epidemics.
  • Existing criteria are insufficient to determine when a pathogen transitions from low risk to a high probability of a major outbreak.

Purpose of the Study:

  • To introduce and define an outbreak threshold (T₀) for predicting epidemic potential.
  • To establish criteria for identifying when a pathogen is likely to cause a major outbreak versus fading out.

Main Methods:

  • Mathematical modeling of pathogen dynamics in host populations.
  • Analysis of stochastic effects on early epidemic stages.
  • Derivation of the outbreak threshold (T₀) based on the reproductive ratio (R₀).

Main Results:

  • For large, homogeneous populations, approximately 1/Log(R₀) infected individuals are required to prevent early stochastic fade-out.
  • The derived threshold (T₀) scales with population heterogeneity and the reproductive ratio (R₀).

Conclusions:

  • The study provides a quantitative measure (T₀) to assess epidemic risk from rare pathogens.
  • Understanding this threshold is crucial for effective control strategies targeting emerging and re-emerging infectious diseases.