Evolution Of COVID-19 In Brazil Research Articles

Altitude conditions seem to determine the evolution of COVID-19 in Brazil

COVID-19 is spreading rapidly in Brazil, a country of continental dimensions, but the incidence of the disease is showing to be very heterogeneous, affecting cities and regions differently. Thus, there is a gap regarding what factors would contribute to accentuate the differences in the incidence of COVID-19 among Brazilian cities. This work aimed to evaluate the effect of altitude on the incidence of COVID-19 in Brazilian cities. We analyzed the relative incidence (RI), the relative death rate (RDR) of COVID-19, and air relative humidity (RH) in all 154 cities in Brazil with a population above 200 thousand inhabitants, located between 5 and 1135 m in altitude. Pearson's correlation analysis was performed to compare a relationship between altitude with RI and RDR, and between RH with RI and RDR. Altitudes were classified into three classes [low (altitude ≤ 97 m a. s. l), middle (97 < altitude ≤ 795 m a. s. l), high (795 < altitude ≤ 1135 m a. s. l)] for the RI, RDR, and RH variables. To compare the three classes of altitude, analysis of variance (ANOVA) and Tukey test were used to compare averages (p < 0.05). Our epidemiological analysis found that the RI, RDR, and RH were lower in cities located in high altitudes (795 < altitude ≤ 1135 m a. s. l) when compared to the middle (97 < altitude ≤ 795 m a. s. l) and low (altitude ≤ 97 m a. s. l) cities altitudes. Furthermore, our study shows that there is a negative correlation between the incidence of COVID-19 with altitude and a positive correlation with RH in the cities analyzed. Brazilian cities with high altitude and low RH have lower RI and RDR from COVID-19. Thus, high altitude cities may be favorable to shelter people at risk. This study may be useful for understanding the behavior of SARS-CoV2, and start point for future studies to establish causality of environmental conditions with SARS-CoV2 contributing to the implementation of measures to prevent and control the spread of COVID-19.

Diffusive process under Lifshitz scaling and pandemic scenarios

We here propose to model active and cumulative cases data from COVID-19 by a continuous effective model based on a modified diffusion equation under Lifshitz scaling with a dynamic diffusion coefficient. The proposed model is rich enough to capture different aspects of a complex virus diffusion as humanity has been recently facing. The model being continuous it is bound to be solved analytically and/or numerically. So, we investigate two possible models where the diffusion coefficient associated with possible types of contamination are captured by some specific profiles. The active cases curves here derived were able to successfully describe the pandemic behavior of Germany and Spain. Moreover, we also predict some scenarios for the evolution of COVID-19 in Brazil. Furthermore, we depicted the cumulative cases curves of COVID-19, reproducing the spreading of the pandemic between the cities of São Paulo and São José dos Campos, Brazil. The scenarios also unveil how the lockdown measures can flatten the contamination curves. We can find the best profile of the diffusion coefficient that better fit the real data of pandemic.

COVID-19 in Brazil: evolution of the epidemic up until epidemiological week 20 of 2020.

Objective to describe the evolution of COVID-19 in Brazil up until epidemiological week 20 of 2020. Methods this is an ecological study based on data and official documents from the Brazilian Ministry of Health and international organizations; comparisons were made between Brazil and other countries and incidence and mortality rates were calculated. Results by the end of epidemiological week 20, 233,142 cases, and 15,633 deaths had been confirmed for Brazil as a whole and 3,240 (58.2%) of the country's municipalities had reported at least one case; Brazil was at an earlier phase of the pandemic when compared to other countries, except Russia and Turkey, regarding cumulative cases, and except Canada regarding cumulative deaths; the highest rates were found in Brazil's Northern Region states, where Amazonas state had the highest incidence rates(4,474.6/1,000,000) and mortality rates (331.8/1,000,000). Conclusion Brazil is one of the countries with the highest number of confirmed cases and deaths, with marked regional differences.

Modeling the early evolution of the COVID-19 in Brazil: Results from a Susceptible–Infectious–Quarantined–Recovered (SIQR) model

The world evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov2 or simply COVID-19) led the World Health Organization to declare it a pandemic. The disease appeared in China in December 2019, and it has spread fast around the world, especially in European countries like Italy and Spain. The first reported case in Brazil was recorded in February 26, and after that the number of cases grew fast. In order to slow down the initial growth of the disease through the country, confirmed positive cases were isolated to not transmit the disease. To better understand the early evolution of COVID-19 in Brazil, we apply a Susceptible–Infectious–Quarantined–Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health, obtained from February 26, 2020 through March 25, 2020. Based on analytical and numerical results, as well on the data, the basic reproduction number is estimated to [Formula: see text]. In addition, we estimate that the ratio between unidentified infectious individuals and confirmed cases at the beginning of the epidemic is about 10, in agreement with previous studies. We also estimated the epidemic doubling time to be [Formula: see text] days.

Evolução epidemiológica da COVID-19 no Brasil e no mundo

Objetivo: analisar a evolução epidemiológica da COVID-19 no Brasil correlacionando ao cenário mundial atual. Metodologia: trata-se de um estudo epidemiológico analítico e documental com abordagem quantitativa e descritiva de dados secundários da COVID-19 do Boletim Epidemiológico Especial número 15 do Ministério da Saúde do Brasil sobre a situação epidemiológica até o dia 08 de maio de 2020. Resultados: os Estados Unidos lideram em número de casos confirmados 1.318.787 (33%) e óbitos 78.503 (28%) no mundo. O Brasil, ocupa o 8º lugar com 145.328 (4%) dos casos confirmados de COVID-19, a sua letalidade está em 6,8% e a mortalidade é de 46,6 por 1.000.000 de habitantes. Em 2020, registrou-se 107.895 hospitalizações por Síndrome Respiratória Aguda Grave no Brasil, desse total, 27.086 (25%) eram casos confirmados de COVID-19 e 7.514 (28%) evoluíram para óbito, sendo as pessoas com 60 anos ou mais 5.697 (69%) dos casos e as cardiopatias e o diabetes os grupos de risco que destacaram-se consideravelmente nos óbitos dos idosos. Conclusão: a doença ainda não está sob controle, o distanciamento social e o uso de máscaras são a melhor forma de evitar a progressão de casos e mortes por COVID-19.

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil.

This data-driven work aims to analyze and classify the spatiotemporal distribution of all Brazilian states considering data so diverse as the number of Covid-19 cases, deaths, confirmed cases per 100 k inhabitants, mortality per 100 k inhabitants and case fatality rates as health indicators. We also considered population, area and population density as geographic indicators. Finally, GDP and HDI were taken into account as economic and social criteria. For this task data were collected from April 3rd until August 8th, 2020, corresponding to epidemiological weeks 14–32, reaching three million cases and a hundred thousand deaths. With this data it was possible to classify Brazilian states using multivariate methods into possible groups by means of non-hierarchical (k-means) cluster as well as factor analysis. It was possible to group all states plus the Federal District into five clusters, taking into account these 10 variables over the first five months of the epidemic. Group changes between states were observed over time and clusters, and between three and four factors were found. However, even with great difference on health indicators during days, the number of clusters remains fixed. Also, São Paulo and Rio de Janeiro states were ranked at top list taking into account all epidemiological weeks. Correlations were observed between variables, such as the number of Covid cases and deaths with GDP for most of epidemiological weeks. Some clusters were more critical due to specific variables, including cities that are main hotspots. These multivariate findings would provide a comprehensive description of the ongoing Covid-19 epidemic and may help to guide subsequent studies to understand and control virus transmission.

COVID-19 e tratamento cirúrgico do câncer no Brasil

Concerned with the evolution of COVID-19 in Brazil and its impact on our health system and oncology surgical assistance, the authors performed a review and suggested strategies for approach based on current evidence.