Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations
Abstract Empirical records of epidemics reveal that fluctuations are important factors for the spread and prevalence of infectious diseases. The exact manner in which fluctuations affect spreading dynamics remains poorly known. Recent analytical and numerical studies have demonstrated that improved...
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2019
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oai:doaj.org-article:35dca6943c384defa2992a7a4864cfde2021-12-02T15:09:15ZHamiltonian dynamics of the SIS epidemic model with stochastic fluctuations10.1038/s41598-019-52351-x2045-2322https://doaj.org/article/35dca6943c384defa2992a7a4864cfde2019-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-52351-xhttps://doaj.org/toc/2045-2322Abstract Empirical records of epidemics reveal that fluctuations are important factors for the spread and prevalence of infectious diseases. The exact manner in which fluctuations affect spreading dynamics remains poorly known. Recent analytical and numerical studies have demonstrated that improved differential equations for mean and variance of infected individuals reproduce certain regimes of the SIS epidemic model. Here, we show they form a dynamical system that follows Hamilton’s equations, which allow us to understand the role of fluctuations and their effects on epidemics. Our findings show the Hamiltonian is a constant of motion for large population sizes. For small populations, finite size effects break the temporal symmetry and induce a power-law decay of the Hamiltonian near the outbreak onset, with a parameter-free exponent. Away from the onset, the Hamiltonian decays exponentially according to a constant relaxation time, which we propose as a metric when fluctuations cannot be neglected.Gilberto M. NakamuraAlexandre S. MartinezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-9 (2019) |
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Medicine R Science Q Gilberto M. Nakamura Alexandre S. Martinez Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
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Abstract Empirical records of epidemics reveal that fluctuations are important factors for the spread and prevalence of infectious diseases. The exact manner in which fluctuations affect spreading dynamics remains poorly known. Recent analytical and numerical studies have demonstrated that improved differential equations for mean and variance of infected individuals reproduce certain regimes of the SIS epidemic model. Here, we show they form a dynamical system that follows Hamilton’s equations, which allow us to understand the role of fluctuations and their effects on epidemics. Our findings show the Hamiltonian is a constant of motion for large population sizes. For small populations, finite size effects break the temporal symmetry and induce a power-law decay of the Hamiltonian near the outbreak onset, with a parameter-free exponent. Away from the onset, the Hamiltonian decays exponentially according to a constant relaxation time, which we propose as a metric when fluctuations cannot be neglected. |
format |
article |
author |
Gilberto M. Nakamura Alexandre S. Martinez |
author_facet |
Gilberto M. Nakamura Alexandre S. Martinez |
author_sort |
Gilberto M. Nakamura |
title |
Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
title_short |
Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
title_full |
Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
title_fullStr |
Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
title_full_unstemmed |
Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations |
title_sort |
hamiltonian dynamics of the sis epidemic model with stochastic fluctuations |
publisher |
Nature Portfolio |
publishDate |
2019 |
url |
https://doaj.org/article/35dca6943c384defa2992a7a4864cfde |
work_keys_str_mv |
AT gilbertomnakamura hamiltoniandynamicsofthesisepidemicmodelwithstochasticfluctuations AT alexandresmartinez hamiltoniandynamicsofthesisepidemicmodelwithstochasticfluctuations |
_version_ |
1718387866597326848 |