Luiz Duczmal

Federal University of Minas Gerais | UFMG

Os alarmes epidemiológicos para a tomada de decisão por parte dos agentes de saúde pública podem ser soados com antecedência e monitorados com base em previsões a partir de modelos SEIR (Susceptíveis-Expostos-Infectados-Recuperados) [1-5]. A tomada de decisão antecipada com base nos alertas do modelo SEIR tem evitado colapsos do sistema de saúde e...

Alerta de terceira onda de COVID-19 para Manaus. Manuscrito já revisado aguardando publicação.

Aqui mostramos que Curitiba sofre ameaça de uma quarta onda de COVID-19 devido a variante delta do SARS-CoV-2.

Avaliação da pandemia de COVID-19 em Araucária, Paraná, mostrando continuidade da pandemia até 2022.

Preprint available at: https://philip.inpa.gov.br Is Brazil’s COVID-19 epicenter really approaching herd immunity? A recent study estimated that in October 2020 three-quarters of the population of Manaus (the capital of the largest state in the Brazilian Amazon) had contact with SARS-CoV-2. We show that 46% of the Manaus population having had conta...

This article presents a statistic to identify the proportional intensity of an area to belong to a space-time cluster, which is very important in spatial statistics. A significant extension of the so-called F-function has been described in detail, which, associated with new technology, allows the evaluation of the intensity of change moments in the...

The spatial scan statistic is a widely used technique for detecting spatial clusters. Several extensions of this technique have been developed over the years. The objectives of these techniques are the detection accuracy improvement and a flexibilization on the search clusters space. Based on Voronoi-Based Scan (VBScan), we propose a biobjective ap...

Dependence measures, from linear correlation coefficients to recent copula-based methods, have been widely used to find out associations between variables. Although the latter type of measure has overcome many drawbacks of traditional measures, copula has intrinsically some undesirable characteristics for particular applications. In this paper, we...

Dependence measures, from linear correlation coefficients to recent copula-based methods, have been widely used to find out associations between variables. Although the latter type of measure has overcome many drawbacks of traditional measures, copula has intrinsically some undesirable characteristics for particular applications. In this paper, we...

Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods. Spatial Scan Statistics have been adapted to analyze multivariate data sources, but only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and compu...

In recent years Kulldorff’s circular scan statistic has become the most popular tool for detecting spatial clusters. However, window-imposed limitation may not be appropriate to detect the true cluster. To work around this problem we usually use complex tools that allow the detection of clusters with arbitrary format, but at the expense of an incre...

A modification is proposed to the usual inference test of the Kulldorff’s spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new modified inference question is answered: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely clu...

This chapter reviews a dynamic programming scan approach to the detection and inference of arbitrarily shaped spatial clusters in aggregated geographical area maps, which is formulated here as a classic knapsack problem. A polynomial algorithm based on constrained dynamic programming is proposed, the spatial clusters detection dynamic scan. It mini...

The spatial scan statistic is one of the most important methods to detect and monitor spatial disease clusters. Usually it is assumed that disease cases follow a Poisson, Binomial, Bernoulli, or negative binomial distribution. In practice, however, case count datasets frequently present zero inflation and/or dispersion (underdispersion or overdispe...

The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisson processes. The most popular variant of the spatial scan is the circular scan. However, such approach has several limitations, in particular, the circular window is not suitable to make the correct description of irregularly shaped and/or unconnected...

Spatial Scan Statistics have been developed for geographical cluster detection in different types of models, for example, Bernoulli, multinomial, Poisson, Exponential, Weibull and Normal. However, some data are continuous in the interval such as rates, proportions and indices, or are limited in the interval , . In this paper, we propose a spatial s...

In practical applications of multivariate sliding window (SW) control charts, a considerable amount of difficulty lies in selecting parameters related to the window size and to the disposal of past observations. Although widely used for pattern recognition problems, to the best of the authors’ knowledge, there have been no comparative analyses of t...

ovariate studies associating the presence of regularly shaped geographic clusters with environmental factors are routinely done using the Circular Scan. However, if the study employs irregular clusters instead, accurate results depend on the generation of a rich family of variants of the primary cluster. We employ climate information to assess the...

The detection and inference of arbitrarily shaped spatial clusters in aggregated geographical areas is described here as a multi-objective combinatorial optimization problem. A multi-objective dynamic programming algorithm, the Geo Dynamic Scan, is proposed for this formulation, finding a collection of Pareto-optimal solutions. It takes into accoun...

Objective To propose a more realistic model for disease cluster detection, through a modification of the spatial scan statistic to account simultaneously for inflated zeros and overdispersion. Introduction Spatial Scan Statistics [1] usually assume Poisson or Binomial distributed data, which is not adequate in many disease surveillance scenarios....

Objective To incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection using statistical tools from multi-criteria analysis. Introduction Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single sou...

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done i...

Controls coordinates.

Dengue fever cases coordinates and onset-date.

The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through th...

There is considerable uncertainty in the disease rate estimation for aggregated area maps, especially for small population areas. As a consequence the delineation of local clustering is subject to substantial variation. Consider the most likely disease cluster produced by any given method, like SaTScan, for the detection and inference of spatial cl...

Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on...

This work presents a Linear Matrix Inequality (LMI) formulation for training Radial Basis Function (RBF) neural networks, considering the context of multiobjective learning. The multiobjective learning approach treats the bias-variance dilemma in neural network modeling as a bi-objective optimization problem: the minimization of the empirical risk...

We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined factors for local clustering of diseases, through the comparative evaluation of the significance of the most likely clusters detected under maps whose neighborhood structures were modified according to those factors. A multi-objective genetic algorithm...

The geographic delineation of irregularly shaped spatial clusters is an ill defined problem. Whenever the spatial scan statistic is used, some kind of penalty correction needs to be used to avoid clusters' excessive irregularity and consequent reduction of power of detection. Geometric compactness and non-connectivity regu-larity functions have bee...

We described and evaluated a novel elitist genetic algorithm for the detection of spatial clusters, which uses the spatial scan statistic in maps divided into finite numbers of regions. The offspring generation is very inexpensive. Children zones are automatically connected, accounting for the higher speed of the genetic algorithm. Although random...

Irregularly shaped spatial disease clusters occur commonly in epidemiological stud-ies, but their geographic delineation is poorly defined. Most current spatial scan soft-ware usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, cor-responding...

Multiple or irregularly shaped spatial clusters are often found in disease or syndromic surveillance maps. We develop a novel method to delineate the contours of spatial clusters, especially when there is not a clearly dominating primary cluster, through artificial neural networks. The method may be applied either for maps divided into regions or p...

In disease surveillance, there are often many different data sets or data groupings for which we wish to do surveillance. If each data set is analysed separately rather than combined, the statistical power to detect an outbreak that is present in all data sets may suffer due to low numbers in each. On the other hand, if the data sets are added by t...

A new approach is presented for the detection and inference of irregularly shaped spatial clusters, using a genetic algorithm. Given a map divided into regions with corresponding populations at risk and cases, the graph-related operations are minimized by means of a fast offspring generation and efficient evaluation of Kuldorff's spatial scan stati...

The spatial scan statistic is commonly used for geographical disease cluster detection, cluster evaluation and disease surveillance. The most commonly used shape of the scanning window is circular. In this paper we explore an elliptic version of the spatial scan statistic, using a scanning window of variable location, shape (eccentricity), angle an...

Spatial scan statistics are commonly used for geographic disease cluster detection and evaluation. We propose and implement a modified version of the simulated annealing spatial scan statistic that incorporates the concept of "non-compactness" in order to penalize clusters that are very irregular in shape. We evaluate its power for the simulated an...

We propose a modification of the spatial scan statistic that takes account of workflow, which is the movement of individuals between home and work. The objective is to detect clusters of disease in situations where exposure occurs in the workplace, but only home address is available for analysis. In these situations, application of the usual spatia...

Irregularly shaped spatial disease clusters occur commonly in epidemiological studies, but their geographic delineation is poorly defined. Most current spatial scan software usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, corresponding to...

We propose a novel scan statistic algorithm for finding spatial clusters in a map divided in a finite number of regions, whose adjacency is defined by a graph structure. Instead of specifying locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter the usual adjacency induced by th...

We discuss and implement a new strategy for spatial cluster detection. A test statistic based on the likelihood ratio is used, as formulated by Kulldorff and Nagarwalla. Differently from these authors, our test is not restricted to the detection of clusters with fixed shape, such as rectangular or circular shape, but it looks for connected clusters...

Naus’s early 1965 paper [Naus (1965)] on spatial scan statistics paved the way for a considerable amount of research on geographic-based statistical analysis, inspiring intensive work in the most diverse contexts and applications, including epidemiology, syndromic surveillance, criminality and environmental sciences. Following one line of work, sev...

OBJECTIVE Situations where a disease cluster does not have a regular shape are fairly common. Moreover, maps with multiple clustering, when there is not a clearly dominating primary cluster, also occur frequently. We would like to develop a method to analyze more thoroughly the several levels of clustering that arise naturally in a disease map divi...

We propose a plug-in method to estimate the optimal bandwidth to be used in the definition of the kernel estimator of a distribution function. The empirical characteristic function is used to define the estimator. Our method is based on the ideas given by Chiu (1991). We compared the results of our method with other ones such as Bowman et al (1998)...

RESUMO A identificação de anomalias (clusters) na distribuição espaço-temporal de eventos geo-referenciados em mapas geográficos tem recebido recentemente considerável atenção da comu-nidade científica [7]. As metodologias já desenvolvidas aplicam-se a uma ampla gama de proble-mas, como epidemiologia, criminalidade, marketing e comunicações; nesses...

We propose a new graph based strategy for the detection of spatial clusters of arbitrary geometric form in a map of geo-referenced populations and cases. Our test statistic is based on the likelihood ratio test previously formulated by Kulldorff and Nagarwalla for circular clusters. A new technique of adaptive simulated annealing is developed, focu...

OBJECTIVE Irregularly shaped clusters occur naturally in disease surveillance, but they are not well defined. The num-ber of possible clusters increases exponentially with the number of regions in a map. This concurs to re-duce the power of detection, motivating the utiliza-tion of some kind of penalty function to avoid exces-sive freedom of shape....