Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()

Soares de Oliveira, Anderson Castro; Martins Morita, Lia Hanna; Barroso da Silva, Eveliny; Ribeiro Zardo, Luiz André; Fernandes Fontes, Cor Jesus; Tita Granzotto, Daniele Cristina

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.