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Stochastic dynamics for reinfection by transmitted diseases

The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous re...

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Autores principales: Barros, Alessandro S., Pinho, Suani T. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Physical Society 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217523/
https://www.ncbi.nlm.nih.gov/pubmed/28709290
http://dx.doi.org/10.1103/PhysRevE.95.062135
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author Barros, Alessandro S.
Pinho, Suani T. R.
author_facet Barros, Alessandro S.
Pinho, Suani T. R.
author_sort Barros, Alessandro S.
collection PubMed
description The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous reinfection due to direct contact with an infected individual (with smaller reinfection rate [Formula: see text] than infection rate [Formula: see text]). In this paper, we examine the stochastic susceptible, infected, recovered, infected (SIRI) model simulating the endogenous reactivation by a spontaneous reaction, while exogenous reinfection by a catalytic reaction. Analyzing the mean-field approximations of a site and pairs of sites, and Monte Carlo (MC) simulations for the particular case of exogenous reinfection, we obtained continuous phase transitions involving endemic, epidemic, and no transmission phases for the simple approach; the approach of pairs is better to describe the phase transition from endemic phase (susceptible, infected, susceptible (SIS)-like model) to epidemic phase (susceptible, infected, and removed or recovered (SIR)-like model) considering the comparison with MC results; the reinfection increases the peaks of outbreaks until the system reaches endemic phase. For the particular case of endogenous reactivation, the approach of pairs leads to a continuous phase transition from endemic phase (SIS-like model) to no transmission phase. Finally, there is no phase transition when both effects are taken into account. We hope the results of this study can be generalized for the susceptible, exposed, infected, and removed or recovered ([Formula: see text]) model, for which the state exposed (infected but not infectious), describing more realistically transmitted diseases such as tuberculosis. In future work, we also intend to investigate the effect of network topology on phase transitions when the SIRI model describes both transmitted diseases ([Formula: see text]) and social contagions ([Formula: see text]).
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spelling pubmed-72175232020-05-13 Stochastic dynamics for reinfection by transmitted diseases Barros, Alessandro S. Pinho, Suani T. R. Phys Rev E Articles The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous reinfection due to direct contact with an infected individual (with smaller reinfection rate [Formula: see text] than infection rate [Formula: see text]). In this paper, we examine the stochastic susceptible, infected, recovered, infected (SIRI) model simulating the endogenous reactivation by a spontaneous reaction, while exogenous reinfection by a catalytic reaction. Analyzing the mean-field approximations of a site and pairs of sites, and Monte Carlo (MC) simulations for the particular case of exogenous reinfection, we obtained continuous phase transitions involving endemic, epidemic, and no transmission phases for the simple approach; the approach of pairs is better to describe the phase transition from endemic phase (susceptible, infected, susceptible (SIS)-like model) to epidemic phase (susceptible, infected, and removed or recovered (SIR)-like model) considering the comparison with MC results; the reinfection increases the peaks of outbreaks until the system reaches endemic phase. For the particular case of endogenous reactivation, the approach of pairs leads to a continuous phase transition from endemic phase (SIS-like model) to no transmission phase. Finally, there is no phase transition when both effects are taken into account. We hope the results of this study can be generalized for the susceptible, exposed, infected, and removed or recovered ([Formula: see text]) model, for which the state exposed (infected but not infectious), describing more realistically transmitted diseases such as tuberculosis. In future work, we also intend to investigate the effect of network topology on phase transitions when the SIRI model describes both transmitted diseases ([Formula: see text]) and social contagions ([Formula: see text]). American Physical Society 2017-06 2017-06-29 /pmc/articles/PMC7217523/ /pubmed/28709290 http://dx.doi.org/10.1103/PhysRevE.95.062135 Text en ©2017 American Physical Society This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source.
spellingShingle Articles
Barros, Alessandro S.
Pinho, Suani T. R.
Stochastic dynamics for reinfection by transmitted diseases
title Stochastic dynamics for reinfection by transmitted diseases
title_full Stochastic dynamics for reinfection by transmitted diseases
title_fullStr Stochastic dynamics for reinfection by transmitted diseases
title_full_unstemmed Stochastic dynamics for reinfection by transmitted diseases
title_short Stochastic dynamics for reinfection by transmitted diseases
title_sort stochastic dynamics for reinfection by transmitted diseases
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217523/
https://www.ncbi.nlm.nih.gov/pubmed/28709290
http://dx.doi.org/10.1103/PhysRevE.95.062135
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