Discussion
Notably, the epidemiologic picture is changing daily. While Brazil is
facing a challenging scenario, especially considering people under
social vulnerability, mostly blacks, other countries in Africa are
predicted to be significantly affected by the ongoing COVID-19 outbreak
(Lone and Ahmad, 2020). A pandemic status like the one we are
experiencing brings lots of concerns. The clinical and laboratory
characteristics of Covid-19 are non-specific and similar to other viral
infections, particularly respiratory diseases (Prasitsirikul et al.,
2020). As the number of COVID-19 patients is increasing dramatically
(Han et al., 2020), it is crucial to predict the number of infected
cases and deaths considering local health assistance availability. The
real number of COVID-19 infected cases is difficult, if not impossible,
to obtain since most of the patients presenting mild symptoms are not
accounted for. However, the number of patients that present moderate or
acute symptoms is particularly important and possible to be estimated.
They are the ones that will need sustained medical attention in
hospitals and will potentially overburden the health system.
Several diseases can vary from fully asymptomatic to severe symptoms
until clinical intervention, demanding for hospitalization and leading
to death, which demonstrates the importance of the measurements of the
case fatality ratio (CFR) to understand epidemic dynamics and disease
burden (Cauchemez et al., 2019).The initial saturation of the number of
infected cases observed throughout the third week of April in Figure
1(a), particularly visible in the curves of daily projected cases and
deaths, is due to the reduction of social distancing. The social
distancing index was about 30 % before the COVID-19 epidemic started in
the state, then, it rapidly peaked at about 53 % just before the first
reported death. However, the continuous social distancing decrease until
now (~38 %) has produced a new rise in infected cases
and deaths, clearly observed in the curves of daily projected cases and
deaths. The CFR calculated from governmental data and plotted in Figure
1 (b) increased from 1 % to 4.2 % at the end of April, when it slowly
decreased to 2.1 % in the second week of July. The model predicts a
further decrease to about 1.5 % until the end of the year. In
comparison, in the second week of July, Brazil exhibited a CFR=3.8 %
and that rate varied between 16.8 % and 2.3 % among the ten countries
with the largest reported COVID-19 disease (SUS, 2020). This rate is
calculated considering the tested infection cases and deaths only,
mainly of hospitalized patients. Therefore, it should be considered
biased, non-realistic, and not necessarily an upper limit.
COVID-19 epidemic significantly increased the mortality rate in 2020.
Nevertheless, the number of deaths by other causes should decrease due
to social distancing and the closing of non-essential economic
activities. Deaths caused by transport accidents and assaults were
responsible for 52 % of all deaths in Minas Gerais in 2018 (DATASUS,
2020), percentage that should heavily decrease due to the cited reasons.
Deaths related to complications in hospital interventions, which are now
postponed due to the epidemic, are also expected to decrease. In the
same way, the decrease in the economic activities should reduce the
number of deaths related to work accidents. Those reductions are clearly
visible from the start of the COVID-19 epidemic in Figure 2, except for
SARS and COVID-19 related deaths.
We interpret the rise of SARS related deaths as an excess mortality not
explained by the officially COVID-19 deaths reported in 2020. The
governmental number of deaths is even considerably lower than that
reported by the registry office. The projected COVID-19 sub-notification
of deaths shows stabilization until the end of the year with a lower
bound of about 30 % and upper bound of about 57 %, regarding
governmental data. These numbers can be affected by a reduction of the
social distancing index which as shown in Figure 1(c), has been
continuously decreasing since the epidemic started in Minas Gerais.
The same methodology applied to estimate a more realistic number of
deaths can also be applied to retrieve a more realistic estimate of
COVID-19 infected cases, as shown in Figure 4(a) and 4(b). Our
methodology indicates that in the 28th epidemiological
week of 2020, the excess of moderate and severe infection cases was
approximately 25 % higher than the officially reported number, with a
prevalence of 2.6 %, leading to a projected sub-notification of around
42 % by the end of the year. A recent survey of COVID-19 infected cases
(Hallari et al., 2020) reported a prevalence of 0.4 %, with 95 % CI
between 0.2 % and 0.7 %, in the southeast region of Brazil. This
number is 13 times lower than the value found in our study. Once
calculated the excess of deaths and cases, a more realistic CFR was also
calculated. This rate increased from about 11 % in week
13th to around 32 % in week 18thand decreased to a saturation of around 2.4 % at the end of 2020. It is
important to note that this CFR, calculated with more realistic data
(excess of deaths and excess cases data) actually represents an upper
limit. A recent estimation of the total number of COVID-19 infected
people in the state capital (city of Belo Horizonte) indicates a number
75 times larger than the official one (Aguas, 2020). Applying this
factor to the whole state and considering the excess of deaths
calculated by our methodology, we can estimate a CFR lower bound of 0.03
% by the end of the year.
Social inequalities in Brazil are intrinsically associated with
ethnicities where most black people are among the poorest. A
considerable number amount of black people lives in crowded communities,
with reduced access to health care. Noticeably, Minas Gerais has 60 %
of the population self-declared black, against 39.7% of self-declared
white persons and 72 % of black persons are also in the group with 10
% lower incomes (IBGE, 2020). The data presented in Figure 5 shows that
60 % of COVID-19 cases are in black patients, fitting the demographic
profile. Nonetheless, in the second half of April, only 55% of deaths
corresponded to black patients, which is 5% below the demographic
profile. Therefore, we understand that SARS-Cov-2 does not affect
differently black and white populations in Minas Gerais. For unknown
reasons, deaths among black patients are 5% lower than expected. A
recent study suggests that a certain degree of cross protection can
exist from previous infection by another coronavirus (Grifoni et al.,
2020). We speculate that the self-declared black population can be
partially protected, due to being previously infected by another
coronavirus. However, further studies are necessary to understand this
discrepancy.
The geographic distribution of cases and deaths in Minas Gerais is also
notable. The distributions throughout the state are non-uniform,
demonstrating high incidence in large urban cores, with several
municipalities showing a considerable number of cases and a reduced
number of deaths. The large number of deaths seem to be concentrated not
only in the capital, but also in cities closer to the state borders. The
geographical distribution of cases and deaths in white and black
patients follows the geographical distribution of both populations in
the state, where the white population is concentrate in the southwest
and richer region of the state, while the black population is more
concentrated in the northeast and poorer portion of the state. It is
striking that even knowing that most of the black population lives in
the lowest Human Development Index (HDI) regions of the state, our
calculated CRF for black patients is 3.7 % points lower than for white
patients.
Mathematical modelling is necessary to predict the behaviour of epidemic
variables and to assist authorities in appropriate policies. Several
mathematical models have been used to assess deaths and infected cases
such as outbreak analytics (Polonsky et al., 2019), data-driven (Ranjan,
2020), stochastic simulations of early outbreak trajectories (Riou and
Althaus, 2020), SAIR (Monteiro, 2020) and the spatiotemporal dynamics of
a spread applying machine learning (Siettos and Russo, 2013; Science et
al., 2020). We applied the Gompertz function and showed how this simple
technique can considerably contribute to the analyses of epidemiological
data collected during COVID-19 pandemic. Herein, we used a proved,
simpler, and intuitive mathematical model that successfully demonstrated
our results. This model well reproduced the time evolution data however,
has been reported to have systematically overestimated the COVID-19
related prediction in China (Yang et al., 2020). Uncertainties are also
present in the used data from public resources, mainly due to collection
efficiency and update delays. Therefore, caution is required in the
consideration of the absolute predicted number of deaths and cases.
COVID-19 cases increase several folds every 24 h. The increasing number
of patients and victims makes it even difficult to maintain a real-time
track of the numbers, also due to notification delays and updates of the
public databases. In countries with severe inequalities, such as Brazil,
a considerable part of patients receives suboptimal attention in the
public health system (Villela, 2020). Between the Influenza virus
subtype (H1N1) pandemic (2009) and COVID-19, Brazil lost about 34.500
hospital beds (Marson and Ortega, 2020) impairing access of the more
vulnerable populations to health assistance.
Notwithstanding, national and regional priorities need to be identified
considering the rational use of limited healthcare resources. The resume
of non-essential economic activities as well as changes in social
distancing drastically alter the epidemic evolution. The social
distancing index has dropped below the 40 % level since June in Minas
Gerais, approaching the pre-epidemic baseline, accompanied by an
increase of 163 % in the number of infected cases and 172 % in the
number of deaths, reported by governmental agencies, just in the first
half of July. Of course, it is expected that, with improved testing
coverage, more cases of COVID-19 will be detected and the clearest
picture of the disease burden in Minas Gerais will become evident.