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- W4288056102 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." @default.
- W4288056102 created "2022-07-28" @default.
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- W4288056102 date "2022-07-13" @default.
- W4288056102 modified "2023-09-30" @default.
- W4288056102 title "Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation" @default.
- W4288056102 doi "https://doi.org/10.48550/arxiv.2207.12111" @default.
- W4288056102 hasPublicationYear "2022" @default.
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