A generalised SEIRD model with implicit social distancing mechanism: A Bayesian approach for the identification of the spread of COVID-19 with applications in Brazil and Rio de Janeiro state

Abstract

ABSTRACT We develop a generalized Susceptible--Exposed--Infected--Removed--Dead (SEIRD) model considering social distancing measures to describe the COVID-19 spread in Brazil. We assume uncertain scenarios with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy. A Bayesian framework is proposed for the identification of model parameters and uncertainty quantification of the model outcomes. We identify through sensitivity analysis (SA) that the model parameter related to social distancing measures is one of the most influential. Different relaxation strategies of social distancing measures are then investigated to determine which are viable and less hazardous to the population. The scenario of abrupt social distancing relaxation implemented after the peak of positively diagnosed cases can prolong the epidemic. A more severe scenario occurs if a social distancing relaxation policy is implemented prior to the evidence of epidemiological control, indicating the importance of the appropriate choice of when to start the relaxation.