The COVID-19 (SARS-CoV-2) uncertainty tripod in Brazil: Assessments on model-based predictions with large under-reporting

Abstract

The COVID-19 pandemic (SARS-CoV-2 virus) is the global crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We develop an adapted Susceptible-Infected-Recovered (SIR) model, which explicitly incorporates the under-reporting and the response of the population to public health policies (confinement measures, widespread use of masks, etc). Large amounts of uncertainty could provide misleading predictions of the COVID-19 spread. In this paper, we discuss the role of uncertainty in these model-based predictions, which is illustrated regarding three key aspects: (i) Assuming that the number of infected individuals is under-reported, we demonstrate anticipation regarding the infection peak. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic cases. (ii) Considering that the actual amount of deaths is larger than what is being registered, we demonstrate an increase of the mortality rates. (iii) When we consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the “the uncertainty tripod”: under-reporting level in terms of cases, deaths, and the true mortality rate of the disease. We demonstrate that if two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates.

SARS-CoV-2 infections in 165 countries over time

“…First, erroneous reporting of total COVID-19-related deaths will have a direct impact on the estimated infection counts. This caveat is particularly important for countries with less developed medical or reporting infrastructure (Bastos et al, 2021;Feyissa et al, 2021;Galvêas et al, 2021;Lloyd-Sherlock et al, 2021;Natashekara, 2021; Veiga e Silva et al, 2020), as well as for countries were reports may be censored or modified for political reasons (Kilani, 2021;Kobak, 2021). A general underreporting of total COVID-19-related deaths, as has been suspected for example for Brazil (Bastos et al, 2021;…”

A control framework to optimize public health policies in the course of the COVID-19 pandemic

“…In particular, when the internal model also has , the results refer to a nominal case analysis. The main goal in the identification stage is to adapt the model fit by assuming that the input data series may suffer interference from several factors, such as case under-ascertainment and underreporting, as well as notification delays 31 . In addition, the model parameters may undergo slight variations in the unfolding pandemic due to changes in medical treatments and protocols and the enactment of governmental measures, for instance.…”

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

“…In addition, we have extended our model with a transmission rate mitigation factor to simulate the introduction of a variety of social measures (lockdown, etc. ), mainly referring to the methodology introduced by Bastos et al (47) and Morato et al (48).…”

“…This factor expresses the observed social isolation ratio within the susceptible population. This means that ψ = 0 is the case where there is no control over viral load (no social measures introduced), while ψ = 1 represents a complete lockdown, without any social interactions (47).…”