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Sequential epidemic-like spread between agglomerates of self-propelled agents in one dimension
Pablo de Castro1, Felipe Urbina2, Ariel Norambuena2
1ICTP-South American Institute for Fundamental Research - Instituto de Física Teórica da UNESP, Rua Dr. Bento Teobaldo Ferraz 271, 01140-070 São Paulo, Brazil.
Physical Review. E
|November 18, 2023
Summary
Motile organisms form clusters, influencing disease spread. Controlling epidemics requires understanding cluster size, travel, and recovery rates, as these factors impact epidemic duration and final infection numbers.
Area of Science:
- Epidemiology
- Statistical Physics
- Mathematical Biology
Background:
- Motile organisms aggregate into clusters (e.g., cities, colonies), influencing disease transmission dynamics.
- Controlling large-scale epidemics depends on factors like cluster characteristics and individual recovery rates.
Purpose of the Study:
- To investigate the spread of susceptible-infected-recovered (SIR) epidemics in one-dimensional spatially structured systems.
- To model how self-propelled particles form clusters and how this impacts epidemic evolution.
Main Methods:
- Utilized a self-propelled particle model exhibiting spontaneous cluster formation.
- Analyzed epidemic time evolution averaged over multiple outbreaks in a 1D system.
- Incorporated stochastic reorientation rates to study cluster dynamics and disease spread.
Main Results:
- Lower stochastic reorientation rates lead to larger, fewer clusters, increasing epidemic duration.
- The number of infected individuals initially rises with reorientation rate as individuals leave clusters faster.
- Higher reorientation rates cause excessive diffusion and smaller clusters, reducing the total number of infected individuals.
Conclusions:
- Cluster formation significantly impacts epidemic dynamics, affecting both duration and final infection counts.
- Individual parameters, like reorientation rate, have a non-trivial effect on the total number of individuals ever infected.
- The study provides a foundation for understanding epidemics in higher dimensions and complex spatial structures.
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