Impact of COVID‐19 in Minas Gerais, Brazil: Excess deaths, sub‐notified cases, geographic and ethnic distribution

Abstract

By analysing the evolution of the COVID‐19 epidemic in the state of Minas Gerais, Brazil, we showed the importance of considering the sub‐notification not only of deaths but also of infected cases. It was shown that the largely used criteria of a historical all‐deaths baseline are not approachable in this case, where most of the deaths are associated with causes that should decrease due to social distancing and reduction of economic activities. A quite simple and intuitive model based on the Gompertz function was applied to estimate excess deaths and excess of infected cases. It fits well the data and predicts the evolution of the epidemic adequately. Based on these analyses, an excess of 21.638 deaths and 557.216 infected cases is predicted until the end of 2020, with an upper bound of the case fatality rate of around 2.4% and a prevalence of 2.6%. The geographical distribution of cases and deaths and its ethnic correlation are also presented. This study points out the necessity of governmental and private organizations working together to improve public awareness and stimulate social distancing to curb the viral infection, especially in critical places with high poverty.

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

“…The 21.62% underestimation of COVID-19 mortality is very close to the 22% [23] and not far from 28% [25] excess mortality from natural causes and SARI, respectively. Two other studies found an average underreporting of 40.68% (range 25.9-62.7%) for six large metropolitan areas in Brazil [20], and 30-57% in the state of Minas Gerais by mid-June 2020 compared with 2017-2019 mean [22]. The lower bound of these studies was close to the present study mean under-reporting estimate.…”

“…To the best of the author's knowledge, this is the first paper on the under-reporting of COVID-19 deaths in Brazil and its federal states for the whole year of 2020 based on comparison of various methods and data sources. Other papers on this topic used the data as of June [20,21], July [22], October [23] and September [24]. Except for the latter, all other used historical time series forecast [19], or 3-year average prior to the epidemic [21], or the last year before the epidemic [22] as a reference to estimate the EM due to COVID-19.…”

“…Other papers on this topic used the data as of June [20,21], July [22], October [23] and September [24]. Except for the latter, all other used historical time series forecast [19], or 3-year average prior to the epidemic [21], or the last year before the epidemic [22] as a reference to estimate the EM due to COVID-19. Only two publications provided results for all federal states, as well as for the whole country [23,25] but did not compare the results by multiple methods and data sources.…”

The COVID-19 pandemic and vaccination abandonment in children: spatial heterogeneity maps

“…However, the epidemiological curves in the country conceal different patterns for notifying the disease in the different administrative units 32 . In Minas Gerais, the curve of COVID-19 infection cases was increased at the end of April 2020 33 . In fact, it was during this month that all macro-regions of the state presented the highest isolation rate between March and November 2020 (above 40%) 34 .…”

The influence of social isolation on the incidence of positivity in covid-19 tests in a metropolitan region of São Paulo, Brazil

“…Studies of geographic distribution are commonly used in epidemiological studies and represents an important visual tool to understand the density of a disease, or, in the case of this study, the demographic location of the highest concentration of tests requested in each city 14 . The use of these supplementary indicators to monitoring the spread of the disease is crucial for decision-making regarding the protocols to be followed, besides collaborating to appropriate management of patients in the hospitals and health units 13,15,16 .…”