Demographic and socioeconomic factors in impacts of COVID-19 by regions in Brazil / Factores demográficos y socioeconómicos en impactos del COVID-19 por regiones en Brasil / Fatores demográficos e socioeconômicos nos impactos da COVID-19 por regiões do Brasil

Objective: assess which demographic and socioeconomic factors contribute to the different impacts of COVID-19 by regions in Brazil. Method: descriptive study with mathematic modeling (USA) were use to assess deaths and COVID-19 cases and also establish a standard relational relationship with demographic and socioeconomic factors across the country and by regions (2020 to 2023). The factors analyzed in the study: i) deaths and cases of COVID-19, ii) total population density per thousand kilometers, iii) isolation index, iv) population, v) Human Development Index - HDI, vi) population density, vii ) average water tariff, viii) urban water service tariff, ix) total water tariff, x) urban sewage service tariff referring to municipalities served with water, xi) service tariff of total sewage, referring to the municipalities served with water, xii) Gini index (income concentration level), xiii) 1st and 2nd dose of vaccine, and xiv) Gross Domestic Product. Results: the study reveals that COVID-19 cases/deaths are significantly correlated with GDP and inversely correlated with the vaccination rate. Conclusion: this study shows scientific evidence that supports the use of vaccination as a protective measure against COVID-19 mortality in Brazil.

Objetivo: avaliar os fatores demográficos e socioeconômicos que contribuem para os diferentes impactos da COVID-19 por regiões do Brasil. Método: estudo descritivo com modelo matemático (USA) foi utilizado para avaliar óbitos e casos de COVID-19 e também estabelecer uma relaçao padrão com fatores demográficos e socioeconômicos em todo o país e por regiões (2020a 2023). Os fatores analisados no estudo: i) óbitos e casos de COVID-19; ii) densidade populacional total por mil quilômetros; iii) índice de isolamento; iv) população; v) Índice de Desenvolvimento Humano; vi)densidade demográfica; vii) tarifa média de água; viii) tarifa de serviço de água urbana; ix) tarifa de água total; x) tarifa de serviço de esgoto urbano referente aos municípios atendidos com água; xi) tarifa de serviço de esgoto total referente aos municípios atendidos com água; xii) índice de Gini; xiii) 1ª e 2ª dose de vacina; e xiv) Produto Interno Bruto. Resultados: o estudo revela que casos/óbitos por COVID-19 são significativamente correlacionados com o PIB e inversamente correlacionados com a taxa de vacinação. Conclusão: este estudo mostra evidências científicas que apoiam o uso da vacinação como medida de proteção contra a mortalidade por COVID-19 no Brasil.

Temporal and spatial patterns of fire activity in three biomes of Brazil.

The trade-off between conservation of natural resources and agribusiness expansion is a constant challenge in Brazil. The fires used to promote agricultural expansion increased in the last decades. While studies linking annual fire occurrence and rainfall seasonality are common, the relationship between fires, land use, and land cover remains understudied. Here, we investigated the frequency of the fires and performed a trend analysis for monthly, seasonal, and annual fires in three different biomes: Cerrado, Pantanal, and Atlantic Forest. We used burned area and integrated models in distinct scales (interannual, intraseasonal, and monthly) using Probability Density Functions (PDFs). The best fitting was found for Generalized Extreme Values (GEV) distribution at all three biomes from the several PDFs tested. We found the most fire in the Pantanal (wetlands), followed by Cerrado (Brazilian Savanna) and Atlantic Forest (Semideciduous Forest). Our findings indicated that land use and land cover trends changed over the years. There was a strong correlation between fire and agricultural areas, with increasing trends pointing to land conversion to agricultural areas in all biomes. The high probability of fire indicates that expanding agricultural areas through the conversion of natural biomes impacts several natural ecosystems, transforming land cover and land use. This land conversion is promoting more fires each year.

Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil).

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007-2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov-Smirnov test, the corrected Akaike criterion AIC c , the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R 2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28-32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02206-1.

Variations in the tropospheric concentration of NO2 in the central west of Brazil, MS, and their relationship with the COVID-19.

COVID-19 (coronavirus disease 2019) started in late 2019 in Wuhan, China. Subsequently, the disease was disseminated in several cities around the world, where measures were taken to control the spread of the virus through the adoption of quarantine (social isolation and closure of commercial sectors). This article analyzed the environmental impact of the COVID-19 outbreak in the state of Mato Grosso do Sul, Brazil, regarding the variations of nitrogen dioxide (NO2) in the atmosphere. NO2 data from the AURA satellite, in the period before the beginning of the epidemic (2005-2019) and during the adoption of the preventive and control measures of COVID-19 in 2020, were acquired and compared. The results obtained from the analysis showed that the blockade from COVID-19, beginning in March 2020, improved air quality in the short term, but as soon as coal consumption in power plants and refineries returned to normal levels (since June 2020), due to the resumption of works, the pollution levels returned to the level of the previous years of 2020. NO2 levels showed a significant decrease, since they were mainly associated with the decrease in economic growth and transport restrictions that led to a change in energy consumption and a reduction in emissions. This study can complement the scientific community and policy makers for environmental protection and public management, not only to assess the impact of the outbreak on air quality, but also for its effectiveness as a simple alternative program of action to improve air quality.

Generalized models and the impacts of population density on COVID-19 transmission / Modelos generalizados y los impactos de la densidad de población en la transmisión del COVID-19 / Modelos generalizados e os impactos da densidade populacional na transmissão da COVID-19

Objective: to analyze epidemic curves based on mathematical models for the state of Mato Grosso do Sul and the impacts of population density on COVID-19 transmission. Method: the linear, polynomial and exponential regression model was used to make the numerical adjustment of the respective curves empirical. Result: it was found that the models used describe very well the empirical curves in which they were tested. In particular, the polynomial model is able to identify with reasonable reliability the appearance of the inflection point in the accumulated curves, which corresponds to the maximum point of the respective daily curves. The analysis indicates a weak positive correlation between infection, mortality, lethality and deaths from COVID-19 with population density, as revealed by the correlation and analysis of R2 . Conclusion: the models are very effective in describing the COVID-19 and epidemic curves in the estimation of important epidemiological parameters, such as peak case curves and daily deaths, allowing practical and efficient monitoring of the evolution of the epidemic.

Objetivo: analizar curvas epidémicas basadas en modelos matemáticos para el estado de Mato Grosso do Sul y los impactos de la densidad de población en la transmisión de COVID-19. Método: se utilizó el modelo de regresión lineal, polinomial y exponencial para hacer el ajuste numérico valor de las respectivas curvas empíricas. Resultados: se encontró que los modelos utilizados describen muy bien las curvas empíricas en las que fueron probados. En particular, el modelo polinomial es capaz de identificar con razonable fiabilidad la aparición del punto de inflexión en las curvas acumuladas, que corresponde al punto máximo de las respectivas curvas diarias. El análisis indica una correlación positiva débil entre la infección, la mortalidad, la letalidad y las muertes por COVID-19 con la densidad de población, según lo revelado por la correlación y el análisis de R2 .Conclusión: los modelos son muy efectivos para describir el COVID-19 y curvas epidémicas en la estimación de parámetros epidemiológicos importantes, como las curvas de casos máximos y las muertes diarias, lo que permite un seguimiento práctico y eficaz de la evolución de la epidemia.

Objetivo: analisar as curvas epidêmicas com base em modelos matemáticos para o estado de Mato Grosso do Sul e os impactos da densidade populacional na transmissão da COVID-19. Método: o modelo de regressão linear, polinomial e exponencial foi utilizado para fazer o ajuste numérico das respectivas curvas empíricas. Resultados: verificou-se que os modelos utilizados descrevem muito bem as curvas empíricas nas quais foram testados. Em particular, o modelo polinomial é capaz de identificar com razoável confiabilidade o aparecimento do ponto de inflexão nas curvas acumuladas, que corresponde ao ponto máximo das respectivas curvas diárias. A análise indica uma correlação positiva fraca entre infecção, mortalidade, letalidade e mortes por COVID-19 com a densidade populacional, conforme revelado pela correlação e análise de R 2. Conclusão: os modelos são muito eficazes na descrição das curvas epidêmicas de COVID19 e na estimativa de parâmetros epidemiológicos importantes, como curvas de casos de pico e óbitos diários, permitindo um monitoramento prático e eficiente da evolução da epidemia

Statistical modeling of hospital admissions for pneumonia in Campo Grande / Modelagem estatística das internações hospitalares por pneumonia em Campo Grande / Modelado estadístico de ingresos hospitalarios por neumonía en Campo Grande

Justification and Objectives: Brazil lacks consistent epidemiological data on the respiratory morbidity of children and older adults, which makes it difficult to plan and execute effective preventive and health promotion actions. The objective of this study was to analyze the adjustments of distributions (Weibull, Normal, Gamma, Logistic) of historical series of hospitalizations for respiratory diseases (total hospitalizations), from 2011 to 2015, in Campo Grande, Mato Grosso do Sul. Methods: to determine the statistical models, four statistical indicators (coefficient of determination, mean root square error, mean absolute error and mean absolute percentage error) were performed from 2011 to 2015. Parameter estimates are obtained for the models adopted in the study, with and without a regression structure. Results: the results showed that Weibull, Gamma, Normal and Logistic distributions, applied to the series of hospitalizations for respiratory diseases in Campo Grande, were satisfactory in determining the shape and scale parameters, and the statistical indicators R2 , MAE, RSME and MAPE confirmed the data goodness-of-fit, and the graphical analysis indicated a satisfactory distribution fit. Conclusion: the analysis of monthly values indicates that Gamma is the best of the four distributions based on those selected. The regression model can be adjusted to the data and used as an alternative distribution that describes the hospitalization data considered in Campo Grande, Brazil.(AU)

Justificativa e Objetivos: o Brasil carece de dados epidemiológicos consistentes sobre a morbidade respiratória de crianças e idosos, o que dificulta o planejamento e a execução de ações efetivas de prevenção e promoção da saúde. O objetivo deste estudo foi analisar os ajustes das distribuições (Weibull, Normal, Gamma, Logística) da série histórica de internações por doenças respiratórias (total de internações), no período de 2011 a 2015, em Campo Grande, Mato Grosso do Sul. Métodos: para determinar os modelos estatísticos, foram executados quatro indicadores estatísticos (coeficiente de determinação, erro quadrático médio, erro absoluto médio e erro percentual absoluto médio) de 2011 a 2015. As estimativas dos parâmetros são obtidas para os modelos adotados no estudo com e sem uma estrutura de regressão. Resultados: os resultados mostraram que as distribuições Weibull, Gamma, Normal e Logística, aplicadas à série de internações por doenças respiratórias em Campo Grande, foram satisfatórias na determinação dos parâmetros de forma e escala, e os indicadores estatísticos R2, MAE, RSME e MAPE confirmaram a qualidade do ajuste dos dados, e a análise gráfica apontou um ajuste satisfatório das distribuições. Conclusão: a análise dos valores mensais indica que a Gamma é a melhor das quatro distribuições baseadas nos selecionados. O modelo de regressão pode ser ajustado aos dados e ser usado como uma distribuição alternativa que descreve os dados de internação considerados em Campo Grande, Brasil.(AU)

Justificación y Objetivos: el Brasil carece de datos epidemiológicos consistentes sobre la morbilidad respiratoria de niños y ancianos, lo que dificulta la planificación y ejecución de acciones efectivas de prevención y promoción de la salud. El objetivo de este estudio fue analizar los ajustes de las distribuciones (Weibull, Normal, Gamma, Logística) de la serie histórica de hospitalizaciones por enfermedades respiratorias (hospitalizaciones totales), de 2011 a 2015, en Campo Grande, Mato Grosso do Sul. Métodos: para la determinación de los modelos estadísticos, se realizaron cuatro indicadores estadísticos (coeficiente de determinación, raíz del error cuadrático medio, error medio absoluto y error porcentual absoluto medio) de 2011 a 2015. Se obtienen estimaciones de los parámetros para los modelos adoptados en el estudio, con y sin estructura de regresión. Resultados: los resultados mostraron que las distribuciones Weibull, Gamma, Normal y Logística, aplicadas a la serie de internaciones por enfermedades respiratorias en Campo Grande, fueron satisfactorias en la determinación de los parámetros de forma y escala, y los indicadores estadísticos R2, MAE, RSME y MAPE confirmaron la calidad de ajuste de los datos, y el análisis gráfico indicaron un ajuste satisfactorio de las distribuciones. Conclusión: el análisis de los valores mensuales indica que la Gamma es la mejor de las cuatro distribuciones en base a las seleccionadas. El modelo de regresión se puede ajustar a los datos y utilizar como una distribución alternativa que describe los datos de hospitalización considerados en Campo Grande, Brasil.(AU)

Impact of Climate Change on Human Infectious Diseases: Dengue

Abstract Climate is considered an important factor in the temporal and spatial distribution of vector-borne diseases. Dengue transmission involves many factors: although it is not yet fully understood, climate is a critical factor as it facilitates risk analysis of epidemics. This study analyzed the effect of seasonal factors and the relationship between climate variables and dengue risk in the municipality of Campo Grande, from 2008 to 2018. Generalized linear models with negative binomial and Poisson distribution were used. The most appropriate model was the one with "minimum temperature" and "precipitation", both lagged by one month, controlled by "year". In this model, a 1°C rise in the minimum temperature of one month led to an increase in dengue cases the following month, while a 10 mm increase in precipitation led to an increase in dengue cases the following month.