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

作　　者：Anderson Castro Soares de Oliveira Lia Hanna Martins Morita Eveliny Barroso da Silva Luiz Andre Ribeiro Zardo Cor Jesus Fernandes Fontes Daniele Cristina Tita Granzotto 

机构地区：[1]Departamento de Estatística,Universidade Federal de Mato Grosso-UFMT,CEP:78060-900,Cuiaba,MT,Brazil [2]Departamento de Estatística,Universidade Estadual de Maringa-UEM,CEP:87020-900,Maringa,PR,Brazil [3]Faculdade de Medicina,Universidade Federal de Mato Grosso-UFMT,CEP:78060-900,Cuiaba,MT,Brazil

出　　处：《Infectious Disease Modelling》2020年第1期699-713,共15页传染病建模（英文）

摘　　要：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 underreporting 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.

关 键 词：COVID-19 UNDER-REPORTING SIR model Bayesian aproach 

分 类 号：O17[理学—数学] R563.1[理学—基础数学]