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.