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. Issue 2 (4th March 2023)

• Record Type: Journal Article
• Authors: Volpatto, D. T.; Resende, A. C. M.; dos Anjos, L.; Silva, J.V.O.; Dias, C. M.; Almeida, R.C.; Malta, S.M.C.
• Is Part Of: Journal of simulation. Volume 17:Issue 2(2023)
• Page Start: 178
• Page End: 192
• Publication Date: 2023-03-04
• Subjects: SEAIRPD-qSEAIRPD-Q model -- Bayesian framework -- uncertainty quantification -- sensitivity analysis; Operations research -- Periodicals; Mathematical models -- Periodicals; Simulation methods -- Periodicals; 511.805
• DOI: 10.1080/17477778.2021.1977731
• Languages: English
• ISSNs: 1747-7778

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.