The stochastic θ-SEIHRD model: Adding randomness to the COVID-19 spread
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The stochastic θ-SEIHRD model: Adding randomness to the COVID-19 spreadDate
2022Citation
Á. Leitao y C. Vázquez, «The stochastic θ-SEIHRD model: Adding randomness to the COVID-19 spread», Communications in Nonlinear Science and Numerical Simulation, vol. 115, dic. 2022, doi: 10.1016/j.cnsns.2022.106731.
Abstract
[Abstract]: In this article we mainly extend a newly introduced deterministic model for the COVID-19 disease to a stochastic setting. More precisely, we incorporated randomness in some coefficients by assuming that they follow a prescribed stochastic dynamics. In this way, the model variables are now represented by stochastic process, that can be simulated by appropriately solving the system of stochastic differential equations. Thus, the model becomes more complete and flexible than the deterministic analogous, as it incorporates additional uncertainties which are present in more realistic situations. In particular, confidence intervals for the main variables and worst case scenarios can be computed.
Keywords
COVID-19
Compartmental models
Stochastic modelling
CIR process
Monte Carlo simulation
RODEs
Compartmental models
Stochastic modelling
CIR process
Monte Carlo simulation
RODEs
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Rights
Atribución 3.0 España
ISSN
1007-5704