key: cord-1045149-dx1l191y authors: Rodrigues, C. A.; Pinto, A. S.; Sobrinho, C. L.; Santos, E. G.; Cruz, L. A.; Nunes, P. C.; Costa, M. G.; Rocha, M. O. title: Covid-19 epidemic curve in Brazil: A sum of multiple epidemics, whose income inequality and population density in the states are correlated with growth rate and daily acceleration date: 2020-09-12 journal: nan DOI: 10.1101/2020.09.09.20191353 sha: a1bafd60f1ddcdbb91433cb165f0853919e7ea64 doc_id: 1045149 cord_uid: dx1l191y Introduction: Recently, we demonstrated that the polynomial interpolation method can be used to accurately calculate the daily acceleration of cases and deaths by Covid-19. The acceleration of new cases is important for the characterization and comparison of epidemic curves. The objective of this work is to measure the diversity of epidemic curves and understand the importance of socioeconomic variables in the acceleration, peak cases and deaths by Covid-19 in Brazilian states. Methods: This is an ecological study with time series analysis of new cases and deaths by Covid-19 in Brazil and its 27 federation units. Using the polynomial interpolation method, we calculated the daily cases and deaths with the measurement of the respective acceleration. We calculated the correlation coefficient between the epidemic curve data and socioeconomic data. Results: The combination of daily data and acceleration determined that the states of Brazil are in different stages of the epidemic. Maximum acceleration of peak cases, peak of cases, maximum acceleration of deaths and peak of deaths are associated with the Gini index and population density, but did not correlate with HDI and per capita income. Conclusion: Brazilian states showed heterogeneous data curves. Densitypopulation and socioeconomic inequality are associated with worse control of the epidemic. socioeconomic variables in the acceleration, peak cases and deaths by in Brazilian states. This is an ecological study with time series analysis of new cases and deaths by in Brazil and its federation units. Conrado Gini in 1912, is used to measure the distribution of income across a social group. It points out the difference between the income of the poorest and the richest in a population, numerically varies from zero, 0, perfect equality to one, 1, perfect inequality) (9), per capita income (ratio between the total income obtained and the total number of inhabitants in a given state/territory -numerical indicator), population density (ratio between the total population and a given territory (geographical area) -numerical indicator)(10), HDI (Human Development Index-developed in 1990, by economists Nahbub ul Haq (Pakistani) and Amartya Sem (Indian), it is a combination of life expectancy, average schooling and per capita income of a given population, numerically ranges from zero (0) very low HDI one (1) very high HDI) (9). The HDI of the population of Brazil and federation units was obtained from the IBGE website. We calculated the incidence rate by the ratio between the number of cases accumulated on the last day of the series and the population multiplied by 1000. Likewise, mortality was obtained by dividing the number of deaths accumulated at the end of the historical series and the population multiplied by 1000. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . https://doi.org/10.1101/2020.09.09.20191353 doi: medRxiv preprint The epidemic curve was obtained by using the polynomial method in a similar way in previous work (4) . The polynomial was automatically generated by the Matlab software, whose degree and coefficients were adjusted to the curve of daily cases and deaths, figure 1, using the degree at most 8, according to the equation an. day n + an-1. day n-1 + .... + a1. day + a0, n ∈ N In order to illustrate the applicability of the polynomial in describing the epidemic curve, we present data on new cases from Austria, a country that presented the complete curve at the end of this historical series, figure 1. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . https://doi.org/10.1101/2020.09.09.20191353 doi: medRxiv preprint The correlations of socioeconomic data, incidence rate, mortality, maximum acceleration of cases, peak of cases and deaths were measured by the Pearson Correlation Coefficient if they presented normal or normalized distribution after logarithmic transformation. The Spearman method was used to assess the correlation between variables with non-normal distribution. The normality analysis was performed by the Kolmogorov-Smirnov tests. The results were considered significant if p <0.05. Cases: On July 11, 2020, the last day of the historical series, Brazil reached 1,839,850 cases, incidence rate = 8.94 cases/1000 inhabitants, showing a reduction, but still remaining high. As it presented a decrease in acceleration, the country was in the second stage of the acceleration phase, is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . On July 11, 2020, the last day of the historical series, Brazil registered 71,469 deaths, mortality equal to 0.34 deaths/1000 inhabitants. The country presented the maximum acceleration = 23.9 deaths/day 2 on May 7th, day 83 of the series. The most recent acceleration is equal to 13.4 deaths/day 2 , after a short period in which it was negative, constituting a peak in plateau, table 1 and figure 3 . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . https://doi.org/10.1101/2020.09.09.20191353 doi: medRxiv preprint Our results demonstrate that the peak of daily cases, acceleration of cases and deaths and peak of deaths showed a positive correlation with population density and the Gini Index. This result is in agreement with articles that demonstrate the importance of population distancing for the flattening of the epidemic curve, and reinforce the usefulness of lockdown or physical distancing to reduce peak cases and deaths (11). Brazil presented a great acceleration of new cases, and, at the end of this series, it was approaching the peak. This curve represents the sum of various epidemics already occurring in the states, in different phases and at different severity levels. Such heterogeneity may be related to the central government's lack of coordination regarding measures to control the spread of the virus, territorial extent and the country's geographical, social, economic and cultural diversity. In addition, unlike other epidemic diseases, the Covid-19 epidemic in Brazil had its first reports of imported cases (aloctenes), brought by subjects who returned from international trips or foreigners from countries that were themselves in epidemic situation, and later extended to the periphery and smaller cities in the interior of the country (8). The results of this study suggest that the worst Covid-19 indicators were related to demographic density and the Gini Index (indicator of income inequality), same behavior observed with social problems that are more related to income inequality than per capita income (12). The incidence and mortality rate data should be viewed with caution, as we are still experiencing the epidemic and, at this moment, we need data that reflect the daily variation of newly reported cases and their acceleration, which are important because they reflect the strength or trend of the outbreaks taking daily data into account. It does not need to be compared with previous data, it assesses the absolute slope of the curve. Its measurement can be useful to evaluate non-pharmacological measures in response to the Covid-19 pandemic. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 12, 2020. . COVID-19 in Latin America Covid-19: Concerns rise as cases expand rapidly in South America Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study Covid-19 growth rate analysis: Application of a low-complexity tool for understanding and comparing epidemic curves Estudo das adaptações hemodinâmicas da miocardiopatia chagásica pela curva de volume de ventrículo esquerdo obtida pelo ecocardiograma tridimensional. (Tese de Doutorado) Nonlinear least squares with local polynomial interpolation for quantitative analysis of IR spectra Quantum algorithm for multivariate polynomial interpolation Brazilian states presented heterogeneous data curves, therefore classified at different stages of the epidemic, with socioeconomic inequality and population density being associated with worse control of the epidemic.