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Related Concept Videos

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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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.
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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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Models of Health Promotion and Illness Prevention II01:18

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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
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Related Experiment Video

Updated: Oct 6, 2025

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
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A time-since-infection model for populations with two pathogens.

Ferdinand Pfab1, Roger M Nisbet1, Cheryl J Briggs1

  • 1Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, USA.

Theoretical Population Biology
|January 20, 2022
PubMed
Summary

This study extends the time-since-infection framework to model multiple pathogens, enhancing epidemic modeling by linking within-host dynamics to population-level spread. The new model provides formulas for basic reproduction numbers, crucial for understanding pathogen invasion dynamics.

Keywords:
CoinfectionEpidemicsKermack–McKendrick modelPartial differential equationsSuperinfectionTime-since-infection model

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • The Kermack-McKendrick SIR model is a foundational compartmental model for epidemics.
  • Existing multi-pathogen models often use compartmental approaches lacking mechanistic links to within-host dynamics.
  • Time-since-infection models offer a mechanistic link between within-host processes and population-level disease spread.

Purpose of the Study:

  • To extend the time-since-infection framework to model interactions between two pathogens.
  • To develop a more mechanistic approach for understanding multi-pathogen epidemics.
  • To derive formulas for basic reproduction numbers in a two-pathogen system.

Main Methods:

  • Extension of the Kermack-McKendrick time-since-infection framework for two pathogens.
  • Derivation of formulas for basic reproduction numbers (R0) for pathogen invasion analysis.
  • Integration of a simple within-host pathogen model with the population-level model.
  • Numerical simulation and verification of derived formulas for invasibility conditions.

Main Results:

  • Formulas for basic reproduction numbers were derived for the two-pathogen system.
  • The derived R0 formulas predict pathogen invasion potential, considering the presence of the other pathogen.
  • Numerical simulations confirmed the accuracy of the R0 formulas in specifying invasibility conditions.

Conclusions:

  • The extended time-since-infection framework provides a powerful tool for mechanistic modeling of multi-pathogen epidemics.
  • This approach bridges the gap between within-host pathogen dynamics and population-level transmission.
  • The derived R0 calculations are vital for predicting disease spread and invasion in complex epidemiological scenarios.