Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

TL;DR

This paper introduces a Bayesian computation framework for epidemic models that improves parameter estimation and uncertainty quantification by identifying initial conditions and learning informative priors, demonstrated on COVID-19 data.

Contribution

It presents a novel methodology combining initial condition identification and prior learning via cross-entropy, enhancing epidemic model calibration and forecasting accuracy.

Findings

Effective parameter estimation for COVID-19 in Rio de Janeiro

Model accurately describes and forecasts epidemic dynamics

Methodology suitable for real-time epidemic analysis

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.