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

Abstract

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.

Double Trouble: Dengue Followed by COVID-19 Infection Acquired in Two Different Regions: A Doctor’s Case Report and Spatial Distribution of Cases in Presidente Prudente, São Paulo, Brazil

“…Similar to dengue, with COVID-19, a considerable number of patients are asymptomatic, and the number of cases reported is underestimated. In Presidente Prudente and countrywide, they may not reflect the real spread of the pandemic [31].…”

Understanding the Patterns of Serological Testing for COVID-19 Pre- and Post-Vaccination Rollout in Michigan

“…However, both estimated serology test positivity rates were quite different from the estimated prevalence (9.2% and 36.2% for the pre- and post-vaccine launch periods) in our serologic data. There were three main explanations: (1) the under-report factor estimated from CDC might not be accurate for Michigan [ 16 , 17 , 18 ]; (2) most people received vaccination in the last two months of our study, but the number of serology tests in these two months was limited in our study; and (3) according to our association analysis, people who had serologic tests were more likely to have prior medical issues compared to the general population. Therefore, the estimated prevalence in our study might not be representative for Washtenaw County or the State of Michigan.…”

A diffusive SEIR model for community transmission of Covid-19 epidemics: application to Brazil

“…We obtain the reported COVID-19 case data for each 27 states of Brazil from [23]. We assume that only a fraction, f = 0.2, of newly infected cases are reported [16,39]. Therefore, the expression f t 0 Ωi λI(x, t)dx gives the accumulated reported cases of state i, i = 1, 2, .., 27, at day t, where Ω i ⊂ Ω is the domain of state i (we do not take account for the reported cases before t = 0 for simplicity).…”