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Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
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Competing epidemics on complex networks.

Brian Karrer1, M E J Newman

  • 1Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 9, 2011
PubMed
Summary
This summary is machine-generated.

This study models two competing diseases spreading simultaneously on a contact network. It reveals a dynamic transition where disease dominance shifts based on growth rates, with significant outcomes influenced by early-stage random events.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Human diseases spread through contact networks, a focus of extensive research.
  • Understanding disease dynamics is crucial for public health interventions.

Purpose of the Study:

  • To model and analyze the dynamics of two competing diseases spreading concurrently on a single contact network.
  • To investigate how infection immunity to both diseases affects disease spread and outcomes.
  • To determine the phase diagram and predict final infection numbers for each disease.

Main Methods:

  • Utilized a combination of analytical and numerical methods to study the disease spread model.
  • Derived the phase diagram to illustrate different disease dominance scenarios.
  • Estimated the expected final number of infected individuals for each competing disease.

Main Results:

  • Identified an unusual dynamical transition where disease dominance shifts with relative growth rates.
  • Observed strong dependence on stochastic fluctuations near the transition, decreasing slowly with network size.
  • Found regions of single disease dominance and a significant coexistence regime where both diseases become epidemic.

Conclusions:

  • The model predicts complex outcomes for competing diseases, including coexistence and stochastic influences on final prevalence.
  • Disease spread dynamics are sensitive to growth rates and early-stage random events, even in large populations.
  • Understanding these dynamics is key to predicting and managing co-circulating epidemics.