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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Understanding contagion dynamics through microscopic processes in active Brownian particles.

Ariel Norambuena1, Felipe J Valencia1,2, Francisca Guzmán-Lastra3,4

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Active Brownian particles (ABP) model disease spread, offering a new way to analyze contagion dynamics. This approach links microscopic behaviors to macroscopic patterns, improving upon traditional Susceptible-Infected-Recovery (SIR) models.

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Area of Science:

  • Physics
  • Epidemiology
  • Statistical Mechanics

Background:

  • Traditional Susceptible-Infected-Recovery (SIR) models are widely used for contagion dynamics.
  • Understanding disease spread in living systems requires models that capture individual agent behavior.

Purpose of the Study:

  • Introduce Active Brownian Particles (ABP) to model contagion dynamics of living agents.
  • Statistically describe disease spread using particle densities, activity, and recovery times.
  • Develop a first-principles analytical expression for contagion rate.

Main Methods:

  • Simulations of Active Brownian Particles (ABP).
  • Ensemble averaging to derive statistical descriptions.
  • Comparison with traditional Susceptible-Infected-Recovery (SIR) models.

Main Results:

  • ABP successfully reproduces time-dependent contagion dynamics observed in SIR models.
  • Identified critical densities and contagious radius influencing virus spread.
  • Derived a parameter-free analytical expression for contagion rate from microscopic parameters.

Conclusions:

  • ABP provides a novel framework for analyzing contagion dynamics by incorporating microscopic processes.
  • This approach offers a valuable alternative to classical SIR models for biological systems.
  • The derived analytical expression enhances understanding of disease spread mechanisms.