dorsal/arxiv
View SchemaDynamical Monte Carlo method for stochastic epidemic models
| Authors | O. E. Aiello, M. A. A. da Silva |
|---|---|
| Categories | |
| ArXiv ID | physics/0208089 |
| URL | https://arxiv.org/abs/physics/0208089 |
Abstract
In this work we introduce a new approach to Dynamical Monte Carlo methods to simulate markovian processes. We apply this approach to formulate and study an epidemic generalized SIRS model. The results are in excellent agreement with the fourth order Runge-Kutta method in a region of deterministic solution. Introducing local stochastic interactions, the Runge-Kutta method is no longer applicable. Thus, we solve the system described by a set of stochastic differential equations by a Dynamical Monte Carlo technique and check the solutions self-consistently with a stochastic version of the Euler method. We also analyzed the results under the herd-immunity concept.
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"date_created": "2026-03-02T18:00:38.902000Z",
"date_modified": "2026-03-02T18:00:38.902000Z",
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"abstract": "In this work we introduce a new approach to Dynamical Monte Carlo methods to\nsimulate markovian processes. We apply this approach to formulate and study an\nepidemic generalized SIRS model. The results are in excellent agreement with\nthe fourth order Runge-Kutta method in a region of deterministic solution.\nIntroducing local stochastic interactions, the Runge-Kutta method is no longer\napplicable. Thus, we solve the system described by a set of stochastic\ndifferential equations by a Dynamical Monte Carlo technique and check the\nsolutions self-consistently with a stochastic version of the Euler method. We\nalso analyzed the results under the herd-immunity concept.",
"arxiv_id": "physics/0208089",
"authors": [
"O. E. Aiello",
"M. A. A. da Silva"
],
"categories": [
"physics.bio-ph",
"q-bio"
],
"title": "Dynamical Monte Carlo method for stochastic epidemic models",
"url": "https://arxiv.org/abs/physics/0208089"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "3f11ed72-758b-4c7a-9699-3f9bac15b032",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
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