Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.
Nonlinear Dyn
; 111(10): 9649-9679, 2023.
Article
em En
| MEDLINE
| ID: mdl-37025428
ABSTRACT
This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.
Texto completo:
1
Bases de dados:
MEDLINE
Idioma:
En
Revista:
Nonlinear Dyn
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Brasil