Results
The officially reported COVID-19 cases and deaths timeline are shown in
Figure 1. Considering the total of inhabitants, the number of cases and
deaths can be considered small. However, after saturation of daily cases
in the third week of April, a new rise in the curve of infected cases at
the end of April can be observed. The same behaviour was observed in the
curve of deaths about 25 days later. Possibly, these growths could be
attributed to the Social Distance Index, shown in Figure 1 (c). During
this period infections and deaths increased, meanwhile a decline in
social isolation was observed. A sum of two Gonpertz functions was
necessary to appropriately fit both datasets over the whole timeline.
The fitted curves have been extended to show the tendency to saturation
of the number of infection cases and deaths by the end of the year. This
model predicts a total number of 1.182.657 infected people and 15.703
deaths. The daily projected cases and death curves (first derivatives of
the fit curves) clearly show the initial saturation of cases and deaths
curves, followed by a strong and broad peak. The advantage of using the
daily projected cases and death curves is to avoid strong oscillations
(with approximately a 7-day period) present in the daily reported new
deaths and infected cases data. This model projects a peak of 102 daily
deaths on September 8th, while the infected cases peak
of 6047 daily cases will be reached on September 30th.
The case fatality rate (CFR) was calculated as the ratio between the
cumulative number of deaths and the cumulative number of infected cases.
The evolution of social distancing (InLoco, 2020) is shown in Figure
1(c). Our model describes well the pandemic evolution, nowcasts and
forecasts. However, due to its simplicity, the total number of projected
deaths and infected cases should be considered cautiously and depends on
the evolution of social distancing. The values of the fitted parameters
are shown in the supplementary information.
Another scenario arises when the excess of deaths during the COVID-19
pandemic is considered. The standard approach to calculate excess
mortality uses the previous year’s historical all-deaths data as
reference (Nogueira et al., 2005). However, in the current pandemic,
with social distancing and closing of non-essential economic activities,
the situation is not comparable with any previously historical mortality
series reported so far. The total deaths in Minas Gerais between 2008
and 2014 increased 21% and then varied significantly between 2015 and
2018 (18% of variation), making the use of any kind of historical
series unapproachable. In addition, transport accidents and deaths by
assaults two of the most important mortality causes in Minas Gerais,
should heavily decrease during this period because of the reasons
mentioned above.
Figure 2 shows a comparison between the different causes of death
between 2019 (dashed lines) and 2020 (solid lines) in Minas Gerais
(Cartorio, 2020). It is worthy to note that until the
14th epidemiologic week of 2020, when the epidemic of
COVID-19 started in Minas Gerais, the weekly number of deaths for all
causes in 2020 oscillated similarly to the ones observed in 2019.
However, beyond that week most of the 2020 causes of deaths, except for
SARS, fall systematically below the values of 2019, probably as an
effect of social distancing measurements. We interpret the sharp drop
after week 25th, marked by a grey area, as a four to
five weeks delay to update the RO database. Therefore, to calculate the
excess deaths in 2020 regarding data from 2019, we should consider only
until the 25th epidemiologic week of 2020 and only
COVID-19 and SARS death causes. This excess is calculated as follows:
from the total number of deaths caused by COVID-19 plus SARS in 2020,
the number of deaths caused by SARS in 2019 is subtracted. We assumed
that all other causes of death were affected by social distancing and
decreased economic activities and were therefore not considered in the
excess deaths calculation.
The excess deaths (open black squares) calculated according to the above
developed methodology is presented in Figure 3. Since data have been
harvested from 2 different public platforms (Cartorio, 2020; SUS, 2020),
the reported deaths by COVID-19 in both platforms have also been
plotted. Both platforms use different approaches to compile the number
of deaths caused by COVID-19. Whereas the governmental data (open red
circles) is collected by reports from health surveillance, RO collects
the number of deaths (open blue triangles and open black squares) from
registered death certificates. Therefore, not only the number of deaths
attributed to COVID-19 will be different, but also the time delay in
updating both databases. The sum of two Gompertz functions was used to
well fit all datasets. The values of the fitted parameters can be found
in the supplementary information. The excess deaths curve modelling
predicts a peak of 135 daily deaths on September 22nd,
two weeks later, and 32 % higher than that predicted by the
governmental data. The difference between the RO and the governmental
data is due to COVID-19 deaths sub-notification. The upper bound of the
sub-notification can be estimated by the difference between excess
deaths and the governmental reported deaths, while the lower bound of
the sub-notification can be estimated by the difference between RO and
governmental reported deaths. The time evolution of the sub-notification
is shown in Figure 3(b).
As shown above, untested COVID-19 deaths can be erroneously diagnosed
and reported as SARS deaths. We suppose the same problem is happening
for the diagnosis of moderate infection cases. We consider as moderate
infection cases those that were erroneously diagnosed as SARS cases but
did not result in death. Therefore, the same methodology applied to
estimate the sub-notification of COVID-19 deaths will be used to
estimate the sub-notification of COVID-19 moderate cases. Figure 4 (a)
shows the evolution of cumulative weekly SARS cases (Infogripe, 2020) in
2020 (black solid squares) and 2019 (red solid circles). Interestingly,
during the first 9 weeks of the year, the number of cases reported in
2020 and 2019 were similar. However, as soon as the COVID-19 epidemic
started in Minas Gerais, end of week 9th, the number
of SARS in 2020 cases heavily increased compared to 2019. Since there
were no reported additional epidemics related to respiratory symptoms
apart from COVID-19 as well as those of every year, we interpret that
difference as non-diagnosed cases of COVID-19.
Figure 4(b) shows the evolution of COVID-19 excess cases (green open
stars), calculated as the difference between SARS and COVID-19 cases in
2020 related to 2019 SARS cases. For comparison, the cumulative weekly
number of reported COVID-19 cases (blue open triangle) is also plotted.
The excess COVID-19 cases dataset has been well fitted by the sum of two
Gompertz functions (red solid line). The officially reported COVID-19
cases data have also been well fitted by a sum of two Gompertz functions
(blue solid line). The fitted parameter values are shown in the
supplementary information.
The sub-notification of COVID-19 cases upper bound can be calculated now
as the difference between the excess cases and governmental reported
cases and is shown in Figure 4(c). An estimation of CFR upper bound can
now be obtained considering the excess of deaths and the excess of
infected cases, as shown in Figure 4(c).
Ethnic discrepancies impact health care access in Minas Gerais where
self-declared black and brown-skinned people have lower incomes and less
access to health assistance than self-declared white people (IBGE,
2020). Herein, we include the impact of COVID-19 in both ethnics
separating the data presented in Figure 1 by self-declared skin colour
or ethnicity of the patients. We have considered as black patients the
sum of all self-declared black and brown-skinned patients, while white
patients correspond to self-declared white only. To calculate the
percentage of cases and deaths from black persons we have also
considered patients that were not classified in both groups. In this
case the availability of data separated by self-declared skin colour was
restricted up to June 24th.
Figure 5(a) shows the cumulative number of infected cases evolution,
separated by black (open black squares) and white patients (open blue
circles). A small increase of cases of black patients can be observed.
The modelling of the datasets (solid lines) predicts an increase of such
difference towards the end of the year. The percentage progression of
infected black patients (regarding the overall patients), as calculated
from the modelling results, is shown by the dashed red line. This number
oscillates between 60% and 55% from the beginning of the epidemic
towards the end of 2020.
The cumulative deaths progression is shown in Figure 5(b). A small
increase of deaths amongst black patients (open black squares) can be
observed in recent days. The modelling of the datasets (solid lines)
also predicts an increase of such difference towards the end of the
year. The percentage change in deaths in black patients (regarding the
overall number of patients), as calculated from the modelling results,
is shown by the dashed red line. This number rises from around 27% at
the beginning of the epidemic to a saturation of about 54% at the end
of 2020.
The CFR of both populations can be calculated from these modelled
results. Figure 5(c) shows the timeline of CFR for each population. As
can be seen, a saturation of about 4.5%, for black patients was
predicted while for white patients an increase of 8.2% was calculated
towards the end of 2020.
Demography has been also impacting COVID-19 pandemic in Minas Gerais.
Then, in figure 6 we the number of cases and deaths (same data as in
Figure 5) per 100.000 inhabitants separated by municipality and
self-declared skin colour.
Interestingly, the distribution of cases and deaths is non-uniform
across the state. There are municipalities with a higher number of cases
and a lower number of deaths. Most cases and deaths per 100.000
inhabitants are not only concentrated in the capital, but in cities
closer to the state borders with interstate connexions. There are also
strong differences in locations with more cases and deaths in black and
white patients. The number of cases and deaths in black patients is
somehow better distributed along the territory, with a large incidence
in the northern half. However, the cases and deaths in white patients
are concentrated in the southern half, which is economically more
developed and populated.