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Related Concept Videos

Generation Time01:22

Generation Time

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Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
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Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

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Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
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Infection01:20

Infection

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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...
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Stages of Infection01:26

Stages of Infection

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Stages of infection describe what happens to a susceptible host once a pathogen invades the human body. The stages of infection are incubation, prodromal, illness, stage of decline, and convalescence. The incubation stage is the period from exposure to a pathogen until symptoms start. The infected person is unaware of impending illness as the pathogens grow and multiply within the body. The duration may vary depending on the type of infection. The incubation period of measles averages ten to...
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Exponential Equations with Logarithms: Problem Solving01:29

Exponential Equations with Logarithms: Problem Solving

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In ecological studies, exponential models are often used to predict how populations grow over time under favorable conditions. These models assume that the growth rate is proportional to the current population, leading to continuous and compounding increases.The model expresses the population as a function of time, combining the initial population with a growth factor raised to an exponent involving the growth rate and time. To estimate how long it takes for a population to reach a specific...
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Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells
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On the relationship between serial interval, infectiousness profile and generation time.

Sonja Lehtinen1, Peter Ashcroft1, Sebastian Bonhoeffer1

  • 1Institute for Integrative Biology, Department of Environmental System Science, ETH Zürich, Zürich, Switzerland.

Journal of the Royal Society, Interface
|January 6, 2021
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Summary

Estimating epidemic generation times is crucial for control. Current methods using serial intervals have flawed assumptions, but their variances offer useful bounds for contact tracing strategies.

Keywords:
SARS-CoV-2contact tracingepidemiologygeneration timeinfectiousnessmodelling

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Dynamics

Background:

  • Generation time is key to epidemic dynamics and control.
  • Infection times are unknown, making direct generation time observation difficult.
  • Serial intervals (symptom onset to onset) are often used as a proxy for generation times.

Purpose of the Study:

  • To clarify assumptions of two common methods for estimating generation time distributions from serial intervals.
  • To evaluate the plausibility of these assumptions for pathogen transmission.
  • To propose a pragmatic approach for using these estimates in epidemic analysis.

Main Methods:

  • Analysis of assumptions linking infectiousness, incubation periods, and serial intervals.
  • Comparison of generation time distributions derived from two distinct estimation approaches.
  • Evaluation of the impact of generation time variance on epidemic controllability.

Main Results:

  • Neither common estimation approach for generation time is plausible for most pathogens.
  • The variances derived from the two approaches can serve as upper and lower bounds.
  • Underestimating generation time variance may lead to overestimating epidemic controllability.

Conclusions:

  • A pragmatic approach is to use both estimation methods and consider their derived variances as bounds.
  • Accurate estimation of generation time variance is critical for effective contact tracing and epidemic control.
  • Further research should explore more robust methods for estimating generation time distributions.