Parameter calibration and uncertainty quantification in an SEIR-type COVID- 19 model using approximate Bayesian computation
Palavras-chave:
COVID-2019, nonlinear dynamics, stochastic modelling, ABC, SIR modelResumo
The present paper applies the approximate Bayesian computation (ABC) for parameter estimation and
uncertainty quantification in an SEIR-type model with data of hospitalisation and deaths from the city of Rio de
Janeiro. The analysed model considers eight compartments: susceptible, exposed, infectious, asymptomatic, hos-
pitalised, recovered and deceased (SEIAHRD). ABC is employed to update the prior probability density function
of the model parameters, where a two objective optimisation problem is formulated (data of healthcare and deaths)
and eleven parameters are identified. The transmission rate is allowed to vary over time (to change its baseline).
The applied model seems to be consistent with the available data.
