Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation
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This paper proposes a data-driven machine learning framework for parameter estimation and uncertainty quantification in epidemic models based on two key ingredients: (i) prior parameters learning via the cross-entropy method and (ii) update of the model calibration and uncertainty propagation through approximate Bayesian computation. The effectiveness of the new methodology is illustrated with the aid of actual data from COVID-19 epidemic at Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR-type 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, which makes the proposed methodology very appealing for use in the context of real-time epidemic modeling.
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