Material and Methods
Our study collected and analysed Brazilian public datasets of COVID-19,
only. The use of anonymized public data dismissed the need of ethical
committee approval. Data were evaluated from January
1st to July 17th of 2020, based on
governmental cases and mortality reports(SUS, 2020), as well as daily
mortality available in the RO Portal (Cartorio, 2020). We herein
compared different mortality causes to obtain those not affected by
social distancing and economic activity reduction. Excess mortality is
calculated by the difference between COVID-19 and other mortality causes
in 2020 regarding the same timeline in 2019. To model the timeline of
each dataset and to predict future behaviour, a simple and very
intuitive model based on the Gompertz function (Tjørve and Tjørve, 2017)
was used. This model has been successfully used to model COVID-19
mortality in China and other countries (Catala et al., 2020; Yang et
al., 2020). It considers that the time evolution of the population
(deaths and infected cases) can be simulated by re-parametrized Gompertz
functions (Tjørve and Tjørve, 2017) given by:
\(N\left(t\right)=N_{\max}\cdot exp\left\{-exp\left[-r\left(t-T_{i}\right)\right]\right\}\),(1)
Where N(t) is the cumulative number of deaths or infected cases
at a given time t , Nmax is the maximum
number of deaths or infected cases, r characterizes the growth
rate, and Ti is the time at the inflection point
of the curve. The daily deaths or infected cases toll corresponds to the
first-time derivative of N(t) . In this case, the parameterTi corresponds to the moment the curve peaks, andNmax×r characterizes the curve maximum.
Depending on the characteristics of the dataset, the sum of two Gompertz
functions might be needed. This will be indicated by the analysis of
each dataset. The six tables that summarize the values and errors of the
fitting parameters obtained from the fitting of the model to the data
presented in each figure are described in supplemental information
section.