Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, São Paulo State, Brazil, 2007

This study aimed to analyze the spatial distribution of dengue risk and its association with socio-environmental conditions. This was an ecological study of the counts of autochthonous dengue cases in the municipality of Campinas, São Paulo State, Brazil, in the year 2007, aggregated according to 47 coverage areas of municipal health centers. Spatial models for mapping diseases were constructed with Bayesian hierarchical models, based on Integrated Nested Laplace Approximation (INLA). The analyses were stratified according to two age groups, 0 to 14 years and above 14 years. The results indicate that the spatial distribution of dengue risk is not associated with socio-environmental conditions in the 0 to 14 year age group. In the age group older than 14 years, the relative risk of dengue increases significantly as the level of socio-environmental deprivation increases. Mapping of socio-environmental deprivation and dengue cases proved to be a useful tool for data analysis in dengue surveillance systems.

A non-homogeneous poisson model with spatial anisotropy applied to ozone data from Mexico City

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Quantitative and qualitative dynamics of exotic and native blowflies ( Diptera: Calliphoridae) with migrations among municipalities

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Um Modelo Espacial para a Resposta Imunológica Adaptativa da Hepatite B

This paper proposed a two-dimensional spatial model to describe the adaptive immune response for viral hepatitis B. This model considered six populations: healthy hepatocytes T, infected hepatocytes Y , hepatitis B virus V , innate immune system I, active immune system X and memory cells, X. First, a compartmental model was constructed and its equilibrium solutions and also the threshold values related to the stability of each solution were obtained. Using this model, we was able to reproduce the different trends observed for the disease, which are: individuals that eliminate the infection without forming immune response, patients with acute and chronic carriers. By including dispersion of defense cells of the immune system and virus (spatial model), we analyze two situations: homogeneous model, in which the model parameters are the same at all points of the network, and heterogeneous model, which characterizes cells more permeable and less permeable to virus invasion. For the two spatial models (homogeneous and heterogeneous) the times relatead to the viral erradication and/or virus invasion and persistence becoming smaller in relation to the compartmental model. The results also showed that for the set of values used in the simulations and if the two diffusion rates are different from zero, the model is sensitive to variations in the rate of viral spread and not dependent on the dispersion of memory cells. Finally, the heterogeneous model when compared to the homogeneous model shows that the infection can be spatially limited depending on the type of the cell involved in the infection process

Spatial variability models of CO2 emissions from soils colonized by grass (Deschampsia antarctica) and moss (Sanionia uncinata) in Admiralty Bay, King George Island

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Semiparametric modeling of the spatial distribution of occupational accident risk in the casual labor market, Piracicaba, southeast Brazil

The objective of this study was to estimate the spatial distribution of work accident risk in the informal work market in the urban zone of an industrialized city in southeast Brazil and to examine concomitant effects of age, gender, and type of occupation after controlling for spatial risk variation. The basic methodology adopted was that of a population-based case-control study with particular interest focused on the spatial location of work. Cases were all casual workers in the city suffering work accidents during a one-year period; controls were selected from the source population of casual laborers by systematic random sampling of urban homes. The spatial distribution of work accidents was estimated via a semiparametric generalized additive model with a nonparametric bidimensional spline of the geographical coordinates of cases and controls as the nonlinear spatial component, and including age, gender, and occupation as linear predictive variables in the parametric component. We analyzed 1,918 cases and 2,245 controls between 1/11/2003 and 31/10/2004 in Piracicaba, Brazil. Areas of significantly high and low accident risk were identified in relation to mean risk in the study region (p < 0.01). Work accident risk for informal workers varied significantly in the study area. Significant age, gender, and occupational group effects on accident risk were identified after correcting for this spatial variation. A good understanding of high-risk groups and high-risk regions underpins the formulation of hypotheses concerning accident causality and the development of effective public accident prevention policies.

Soil texture spatial variability of different parent material in pereira barreto, São Paulo state

The variation of soil textural characteristics is a function of the relief and parent materials. The objective of this work was to study soil texture spatial variability from different parent material in Pereira Barreto, SP. An area of 530.67 hectares was mapped through the use of Global Positioning System receivers and obtaining of Digital Elevation Models. A set of 201 soil samples was collected from every seven hectares, at three depths: 0 - 0.25 m; 0.25 - 0.50 m; and 0.80 - 1.00 m. The amounts of sand, silt and clay were obtained by the pipette method and analyzed by both descriptive statistics and geostatistics. Soil textures varied as a function of parent materials and topography.

Geostatistical characterization of the spatial distribution of Xylella fastidiosa sharpshooter vectors on citrus

A distribuição espacial das espécies de cigarrinhas (Dilobopterus costalimai Young, Acrogonia sp. e Oncometopia facialis Signoret), vetoras da Xylella fastidiosa, agente causal da Clorose Variegada dos Citros, foi estudada com o uso da geoestatística. As avaliações foram feitas em um pomar comercial de laranja 'Pêra' (Citrus sinensis [L.] Osb.), objetivando estabelecer meios para melhor controle dos vetores e da doença. O monitoramento da ocorrência das cigarrinhas no pomar foi feito através de amostragens mensais, utilizando-se armadilhas adesivas amarelas de 3 x 5, distribuídas uniformemente em 50 pontos na área, dispostas em laranjeiras à altura de 1,5 m do solo e substituídas mensalmente. Acrogonia sp. foi a espécie prevalente nas amostragens. Os resultados possibilitaram ajustar modelos aos semivariogramas da distribuição espacial das três espécies no pomar estudado. Durante os três anos consecutivos de amostragem, as populações de Acrogonia sp., D. costalimai e O. facialis apresentaram modelos de distribuição agregada somente nos meses de verão, inverno e primavera, respectivamente, mostrando a necessidade de monitoramento constante desses vetores para reduzir a sua população em épocas favoráveis ao seu desenvolvimento. Através de parâmetros geoestatísticos foi possível calcular a área de agregação das cigarrinhas no pomar. A espécie Acrogonia sp. apresentou área média de agregação de 15.760 m², enquanto para O. facialis e D. costalimai foi possível constatar áreas médias de agregação de 11.555 m² e 10.980 m², respectivamente. Esses resultados indicaram que para um levantamento seguro de cigarrinhas é necessário pelo menos dispor de uma armadilha por hectare.

Spatial variability of soil CO2 emission in different topographic positions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial variability of soil penetration resistance influenced by season of sampling

O objetivo deste trabalho foi analizar a distribuição espacial da compactação do solo e a influência da umidade do solo na resistência à penetração. Esta última variável foi descrita pelo índice de cone. O solo estudado foi Nitossolo e os dados de índice de cone foram obtidos usando um penetrômetro. A resistência do solo foi avaliada a 5 profundidades diferentes, 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm e mais de 40 cm, porém o conteúdo de umidade do solo foi medido a 0-20 cm e 20-40 cm. As condições hídricas do solo variaram nas diferentes amostragems. Os coeficientes de variação para o índice de cone foram 16,5% a 45,8% e os do conteúdo de umidade do solo variaram entre 8,96% e 21,38%. Os resultados sugeriram elevada correlação entre a resistência do solo, estimada pelo índice de cone e a profundidade do solo. Sem embargo, a relação esperada com a umidade do solo não foi apreciada. Observou-se dependência espacial em 31 de 35 séries de dados de índice de cone e umidade do solo. Esta dependência foi ajustada por modelos exponenciais com efeito pepita variável de 0 a 90% o valor do patamar. em séries de dados o comportamento foi aleatório. Portanto, a técnica das distâncias inversas foi utilizada para cartografar a distribuição das variáveis que não tiveram estrutura espacial. Na krigagem constatou-se uma suavização dos mapas comparados com esses das distâncias inversas. A krigagem indicadora foi utilizada para cartografar a variabilidade espacial do índice de cone e recomendar melhor manejo do solo.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUÇÃO: A malaria é uma doença endêmica na região da Amazônia Brasileira, e a detecção de possíveis fatores de risco pode ser de grande interesse às autoridades em saúde pública. O objetivo deste artigo é investigar a associação entre variáveis ambientais e os registros anuais de malária na região amazônica usando métodos bayesianos espaço-temporais. MÉTODOS: Utilizaram-se modelos de regressão espaço-temporais de Poisson para analisar os dados anuais de contagem de casos de malária entre os anos de 1999 a 2008, considerando a presença de alguns fatores como a taxa de desflorestamento. em uma abordagem bayesiana, as inferências foram obtidas por métodos Monte Carlo em cadeias de Markov (MCMC) que simularam amostras para a distribuição conjunta a posteriori de interesse. A discriminação de diferentes modelos também foi discutida. RESULTADOS: O modelo aqui proposto sugeriu que a taxa de desflorestamento, o número de habitants por km² e o índice de desenvolvimento humano (IDH) são importantes para a predição de casos de malária. CONCLUSÕES: É possível concluir que o desenvolvimento humano, o crescimento populacional, o desflorestamento e as alterações ecológicas associadas a estes fatores estão associados ao aumento do risco de malária. Pode-se ainda concluir que o uso de modelos de regressão de Poisson que capturam o efeito temporal e espacial em um enfoque bayesiano é uma boa estratégia para modelar dados de contagem de malária.

Spatial variability of soil water content and mechanical resistance of Brazilian ferralsol

The spatial variability of mechanical resistance to penetration (PR) and gravimetric moisture (GM) was studied at a depth of 0-0.40 m, in a ferralsol cropped with corn, and under conventional tillage in llha Solteira, Brazil (latitude 20 degrees 17'S, and longitude 52 degrees 25'W). The purpose of this study was to analyse and to try explaining the spatial variability of the mentioned soil physical properties using geostatistics. Soil data was collected at points arranged on the nodes of a mesh with 97 points. Geostatistics was used to analyse the spatial variability of PR and GM at four depths: 0-0. 1, 0.1-0.2, 0.2-0.3 and 0.3-0.4 m. PR showed a higher variability of data, with coefficients of variation of 52.39, 30.54, 16.91, and 15.18%, from the surface layers to the deepest layers. The values of the coefficients of variation for GM were lower: 9.99, 5.13, 5.59, and 5.69%. Correlation between GM and PR for the same soil layers was low. Penetration resistance showed spatial structure only in the 0.30-0.40 m layer, while gravimetric moisture showed spatial structure at all depths except for 0-0. 10 m. All the models of fitted semivariograms were spherical and exponential, with ranges of 10-80 m. Data for the variable 'GM' in the 0.20-0.30 and 0.30-0.40 m layers revealed a trend in data attributed to the occurrence of subsurface water flow. (C) 2005 Elsevier B.V. All rights reserved.