Stochastic dynamics for reinfection by transmitted diseases.
Alessandro S Barros1, Suani T R Pinho2
1Departamento de Física, Instituto Federal da Bahia-40110-150 Salvador, Brazil.
Physical Review. E
|July 16, 2017
Summary
This study uses stochastic models to explore infectious disease dynamics, finding that reinfection can alter transmission phases. The research highlights how different reinfection types and modeling approaches affect epidemic outcomes.
Area of Science:
- Epidemiology
- Mathematical Biology
- Computational Science
Background:
- Stochastic models are crucial for understanding infectious disease dynamics.
- Reinfection, through endogenous reactivation or exogenous contact, is a key factor in disease transmission.
Purpose of the Study:
- To investigate the impact of reinfection on disease transmission dynamics using a stochastic susceptible, infected, recovered, infected (SIRI) model.
- To analyze phase transitions between endemic, epidemic, and no transmission states under different reinfection scenarios.
Main Methods:
- Mean-field approximations (site and pairs of sites) were used to analyze the SIRI model.
- Monte Carlo (MC) simulations were employed for the exogenous reinfection case.
- The study compared analytical results with simulation outcomes.
Main Results:
- The pairs approach better describes phase transitions from endemic to epidemic phases compared to MC results.
- Reinfection was found to increase outbreak peaks, potentially leading to an endemic phase.
- Continuous phase transitions were observed for exogenous reinfection and endogenous reactivation, but not when both were present.
Conclusions:
- The modeling approach significantly influences the description of phase transitions in infectious disease dynamics.
- Understanding reinfection mechanisms is vital for accurate epidemiological predictions.
- The findings can inform extensions to more complex models like SEIR for diseases such as tuberculosis.
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