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.

How many more? Under‐reporting of the COVID‐19 deaths in Brazil in 2020

“…However, an Indian study estimated a six times larger number of COVID-19 deaths than official reports [34], which amounts to almost 86% under-reporting. Many studies on the under-reporting of COVID-19 focused on the number of cases [35][36][37][38] as these are important for predicting future transmission rates. However, mortality remains the key parameter from the burden-of-disease perspective.…”

Estimating the parameters of SIR model of COVID-19 cases in India during lock down periods

“…Here, N=S+I+R, is independent of time t, denotes the total population size [8, 22, 54-56]. India 2020 population is estimated at 1,380,004,385 people at mid-year according to UN data.…”

“…It represents the number of individuals (on average) that a single individual infects [7, 56, 61-68]. In terms of R 0 , we have…”