Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes

BackgroundThe 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 the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated.ResultsA fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented.ConclusionsThe ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.

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“…Hence, the proof follows the same way as performed in [31], replacing the Euclidean distance by Voronoi distance.…”

Section: Methodsmentioning

confidence: 99%

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

et al. 2011

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“…The latter is a powerful tool for characterising geographic variations in risk, and can be used to test explicit hypotheses about putative risk factors. Recent developments that facilitate the efficient detection of irregular cluster shapes have helped to broaden the application of spatial cluster detection methods to micro-spatial scales (Wieland et al, 2007). At these scales, clusters may point to highly localised factors of the physical or social environment that could explain local variations in risk.…”

Section: Discussionmentioning

confidence: 99%

Locating irregularly shaped clusters of infection intensity

Yiannakoulias

Wilson

Kariuki³

et al. 2010

Geospat Health

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Abstract. Patterns of disease may take on irregular geographic shapes, especially when features of the physical environment influence risk. Identifying these patterns can be important for planning, and also identifying new environmental or social factors associated with high or low risk of illness. Until recently, cluster detection methods were limited in their ability to detect irregular spatial patterns, and limited to finding clusters that were roughly circular in shape. This approach has less power to detect irregularly-shaped, yet important spatial anomalies, particularly at high spatial resolutions. We employ a new method of finding irregularly-shaped spatial clusters at micro-geographical scales using both simulated and real data on Schistosoma mansoni and hookworm infection intensities. This method, which we refer to as the "greedy growth scan", is a modification of the spatial scan method for cluster detection. Real data are based on samples of hookworm and S. mansoni from Kitengei, Makueni district, Kenya. Our analysis of simulated data shows how methods able to find irregular shapes are more likely to identify clusters along rivers than methods constrained to fixed geometries. Our analysis of infection intensity identifies two small areas within the study region in which infection intensity is elevated, possibly due to local features of the physical or social environment. Collectively, our results show that the "greedy growth scan" is a suitable method for exploratory geographical analysis of infection intensity data when irregular shapes are suspected, especially at micro-geographical scales.

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“…Given a set of data points, a Voronoi diagram is a partition of the space into cells, where a cell corresponding to a given data point is a locus of all points of space closest to this data point. Voronoi tessellation is commonly used in various fields of natural and medical sciences (Okabe et al, 1992, 2000; Aurenhammer, 1993; Ebeling and Wiedenmann, 1993; Ramella et al, 2001; Dupanloup et al, 2002; Wieland et al, 2007; Bishnu and Bhattacherjee, 2009; Kao et al, 2010; Edla and Jana, 2011), and in geographic information systems to define the partition cell, or catchment areas containing individual sites by their influence (Okabe et al, 1992, 2000). …”

Section: Introductionmentioning

confidence: 99%

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

Chen

Mukerjee²,

Dorfman³

et al. 2017

Front. Genet.

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Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases.

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Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes

Cited by 30 publications

References 31 publications

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

Locating irregularly shaped clusters of infection intensity

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

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