Modelagem espaço-temporal do padrão de infestação da broca do café levando em consideração excesso de zeros e dados faltantes

The study of pest distributions in space and time in agricultural systems provides important information for the optimization of integrated pest management programs and for the planning of experiments. Two statistical problems commonly associated to the space-time modelling of data that hinder its implementation are the excess of zero counts and the presence of missing values due to the adopted sampling scheme. These problems are considered in the present article. Data of coffee berry borer infestation collected under Colombian field conditions are used to study the spatio-temporal evolution of the pest infestation. The dispersion of the pest starting from initial focuses of infestation was modelled considering linear and quadratic infestation growth trends as well as different combinations of random effects representing both spatially and not spatially structured variability. The analysis was accomplished under a hierarchical Bayesian approach. The missing values were dealt with by means of multiple imputation. Additionally, a mixture model was proposed to take into account the excess of zeroes in the beginning of the infestation. In general, quadratic models had a better fit than linear models. The use of spatially structured parameters also allowed a clearer identification of the temporal increase or decrease of infestation patterns. However, neither of the space-time models based on standard distributions was able to properly describe the excess of zero counts in the beginning of the infestation. This overdispersed pattern was correctly modelled by the mixture space-time models, which had a better performance than their counterpart without a mixture component.O estudo da distribuição de pragas em espaço e tempo em sistemas agrícolas fornece informação importante para a otimização de programas de manejo integrado de pragas e para o planejamento de experimentos. Dois problemas estatísticos comumente associados à modelagem espaço-temporal desse tipo de dados que dificultam sua implementação são o excesso de zeros nas contagens e a presença de dados faltantes devido ao esquema de amostragem adotado. Esses problemas são considerados no presente artigo. Para estudar a evolução da infestação da broca do café a partir de focos iniciais de infestação foram usados dados de infestação da praga coletados em condições de campo na Colômbia. Foram considerados modelos com tendência de crescimento da infestação linear e quadrática, assim como diferentes combinações de efeitos aleatórios representando variabilidade espacialmente estruturada e não estruturada. As análises foram feitas sob uma abordagem Bayesiana hierárquica. O método de imputação múltipla foi usado para abordar o problema de dados faltantes. Adicionalmente, foi proposto um modelo de mistura para levar em consideração o excesso de zeros nas contagens no início da infestação. Em geral, os modelos quadráticos tiveram um melhor ajuste que os modelos lineares. O uso de parâmetros espacialmente estruturados permitiu uma identificação mais clara dos padrões temporais de acréscimo ou decréscimo na infestação. No entanto, nenhum dos modelos espaço-tempo baseados em distribuições padrões descreveu, apropriadamente, o excesso de zeros no início da infestação. Esse padrão de sobredispersão foi corretamente modelado pelos modelos de mistura espaço-tempo, os quais tiveram um melhor desempenho que seus homólogos sem mistura.CNP

Impacto do Programa Fica Vivo na redução dos homicídios em comunidade de Belo Horizonte

OBJETIVO: Evaluar el impacto del programa de prevención de homicidios. MÉTODOS: Con base en los datos del Programa "Fica Vivo" (Permanezca Vivo), de prevención de homicidios, fue realizado un estudio quasi experimental con análisis de series temporales de la ocurrencia de homicidios en el región urbanizada Morro das Pedras en Belo Horizonte, Sureste de Brasil, de 2002 a 2006. Se comparó el número de homicidios ocurridos en esa localidad con los de otros barrios violentos y no violentos y otras urbanizaciones de la ciudad, en cada una de las fases del Programa. Para evaluar la hipótesis de que la reducción de los homicidios resultó de las acciones implementadas por el Programa, fue elaborado un modelo estadístico basado en modelos lineales generalizados. RESULTADOS: En los primeros seis meses se obtuvo 69% de reducción en el número promedio de homicidios. En los períodos de retroceso y retomada parcial del Programa, el efecto de reducción de los homicidios disminuyó, pero la diferencia entre coeficientes con el obtenido en el período inicial no fue estadísticamente significativo. Aún con la retomada integral del Programa, el efecto continuó similar a los dos períodos anteriores, probablemente porque el programa fue implantado en otros barrios violentos de la ciudad. CONCLUSIONES: Los resultados señalan que el modelo del Programa Fica Vivo puede constituir una importante alternativa para prevención de homicidios contra jóvenes en comunidades que presenten características semejantes a las de la experiencia piloto en el Morro das Pedras.OBJETIVO: Avaliar o impacto de programa de prevenção de homicídios. MÉTODOS: Com base nos dados do Programa Fica Vivo, de prevenção de homicídios, foi realizado um estudo quase experimental com análise de séries temporais da ocorrência de homicídios no aglomerado Morro das Pedras, em Belo Horizonte, MG, de 2002 a 2006. Comparou-se o número de homicídios ocorridos nessa localidade com os de outras favelas violentas e não violentas e outros bairros da cidade, em cada uma das fases do Programa. Para testar a hipótese de que a redução dos homicídios resultou das ações implementadas pelo Programa, foi elaborado um modelo estatístico baseado em modelos lineares generalizados. RESULTADOS: Nos primeiros seis meses obteve-se 69% de redução no número médio de homicídios. Nos períodos de refluxo e retomada parcial do Programa, o efeito de redução dos homicídios diminuiu, mas a diferença entre coeficientes com aquele do período inicial não foi estatisticamente significante. Mesmo com a retomada integral do Programa, o efeito continuou similar aos dos períodos anteriores, provavelmente porque o Programa foi implantado em outras favelas violentas da cidade. CONCLUSÕES: Os resultados apontam que o modelo do Programa Fica Vivo pode constituir uma importante alternativa para prevenção de homicídios contra jovens em comunidades que apresentem características semelhantes às da experiência piloto no Morro das Pedras.OBJECTIVE: To evaluate the impact of a homicide prevention program. METHODS: A quasi-experimental study was performed using time series analysis of homicide incidence in the Morro das Pedras area in the city of Belo Horizonte, Southeastern Brazil, from 2002 to 2006. The number of homicides occurring in this location was compared to other violent and non-violent favelas and to other neighborhoods of the city, during each of the Program phases. To test the hypothesis that homicide reduction was caused by the actions implemented by the program, a statistical model was developed based on generalized linear models. RESULTS: In the first six months a 69% reduction in the number of homicides was obtained. During the other Program periods, the effect on the reduction of homicides lessened, but the difference among coefficients compared to the initial period was not statistically significant. Even with full Program implementation, the effect continued to be similar to the previous periods, probably because the program was implemented in other violent favelas in the city. CONCLUSIONS: The results suggest that the Staying Alive Program model can be an important alternative for the prevention of youth homicides in communities that have characteristics similar to the pilot program in Morro das Pedras

Surveillance to detect emerging space time clusters.

The interest is on monitoring incoming space time events to detect an emergent space time cluster as early as possible. Assume that point process events are continuously recorded in space and time. In a certain unknown moment, a small localized cluster of increased intensity starts to emerge. Its location is also unknown. The aim is to let an alarm to go off as soon as possible after its emergence, but avoiding that it goes off unnecessarily. The alarm system should also provide an estimate of the cluster location. In addition to that, the alarm system should take into account the purely spatial and the purely temporal heterogeneity, which are not specified by the user. A space time surveillance system with these characteristics using a martingale approach to derive the surveillance system properties is proposed. The average run length for the situation when there are clusters present in the data is appropriately defined and the method is illustrated in practice. The algorithm is implemented in a freely available stand-alone software and it is also a feature in a freely available GIS system

Bayesian spatial models with a mixture neighborhood structure.

In Bayesian disease mapping, one needs to specify a neighborhood structure to make inference about the underlying geographical relative risks. We propose a model in which the neighborhood structure is part of the parameter space. We retain the Markov property of the typical Bayesian spatial models: given the neighborhood graph, disease rates follow a conditional autoregressive model. However, the neighborhood graph itself is a parameter that also needs to be estimated. We investigate the theoretical properties of our model. In particular, we investigate carefully the prior and posterior covariance matrix induced by this random neighborhood structure, providing interpretation for each element of these matrices

Optimal generalized truncated sequential Monte Carlo test.

When it is not possible to obtain the analytical null distribution of a test statistic U, Monte Carlo hypothesis tests can be used to perform the test. Monte Carlo tests are commonly used in a wide variety of applications, including spatial statistics, and biostatistics. Conventional Monte Carlo tests require the simulation of m independent copies from U under the null hypothesis, what is computationally intensive for large data sets. Truncated sequential Monte Carlo designs can be performed to reduce computational effort in such situations. Different truncated sequential procedures have been proposed. They work under restrictive assumptions on the distribution of U aiming to bound the power loss and to reduce execution time. Since the use of Monte Carlo tests are based on the situations where the null distribution of U is unknown, their results are not valid for the general case of any test statistic. In this paper, we derive an optimal scheme for truncated sequential Monte Carlo hypothesis tests. This scheme minimizes the expected number of simulations under any alternative hypothesis, and bounds the power loss in arbitrarily small values. The first advantage from this scheme is that the results concerning the power and the expected time are valid for any test statistic. Also, we present practical examples of optimal procedures for which the expected number of simulations are reduced by 60% in comparison with some of the best procedures in the literature