Spatial Analysis

The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan [2] enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k - 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2[k] subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.

Objective

We present a new method for efficiently and accurately detecting irregularly-shaped outbreaks by incorporating "soft" constraints, rewarding spatial compactness and penalizing sparse regions.

Detection of the signs of HIV epidemic transition from concentrated to generalized stage is an important issue for many countries including Ukraine. Objective and timely detection of the generalization of HIV epidemic is a significant factor for the development and implementation of appropriate preventive programs. As an additional method for estimating HIV epidemic stage, the spatial analysis of the reported new HIV cases among injection drug use (IDU) and other populations (due to sexual way of transmission) has been recommended. For studying new HIV cases in small societies, Relative Risk (RR) rates are preferred over incidence indicators. Spatial clustering based on the calculation of RR rates allows us to locate the high risk areas of HIV infection with greater accuracy. In our opinion, in the process of epidemic generalization the spatial divergence of epidemic will be observed as well. In particular, clusters with high RR of sexual HIV transmission independent from the clusters with high RR of injection HIV transmission may appear.

Objective

To investigate the utility of spatial analysis in the tracking of the stages of the HIV epidemic at an administrative territory level, using the Odessa region, Ukraine as an example.

The spatial scan statistic [1] is the most used measure for cluster strenght. The evaluation of all possible subsets of regions in a large dataset is computationally infeasible. Many heuristics have appeared recently to compute approximate values that maximizes the logarithm of the likelihood ratio. The Fast Subset Scan [2] finds exactly the optimal irregularly spatial cluster; however, the solution may not be connected. The spatial cluster detection problem was formulated as the classic knapsack problem [3], and modeled as a bi-objective unconstrained combinatorial optimization problem. Dynamic programming relies on the principle that, in an optimal sequence of decisions or choices, each sub-sequence must also be optimal. During the search for a solution it avoids full enumeration by pruning early partial decision solutions that cannot possibly lead to optimal solutions.

Objective

We propose a fast, exact algorithm to make detection and inference of arbitrarily shaped connected spatial clusters in aggregated area maps based on constrained dynamic programming.

The intrinsic variability that exists in the cases counting data for aggregated-area maps amounts to a corresponding uncertainty in the delineation of the most likely cluster found by methods based on the spatial scan statistics [3]. If this cluster turns out to be statistically significant it allows the characterization of a possible localized anomaly, dividing the areas in the map in two classes: those inside and outside the cluster. But, what about the areas that are outside the cluster but adjacent to it, sometimes sharing a physical border with an area inside the cluster? Should we simply discard them in a disease prevention program? Do all the areas inside the detected cluster have the same priority concerning public health actions? The intensity function [2], a recently introduced visualization method, answers those questions assigning a plausibility to each area of the study map to belong to the most likely cluster detected by the scan statistics. We use the intensity function to study cases of diabetes in Minas Gerais state, Brazil.

Objective

Cluster finder tools like SaTScan[1] usually do not assess the uncertainty in the location of spatial disease clusters. Using the nonparametric intensity function[2], a recently introduced visualization method of spatial clusters, we study the occurrence of several non-contageous diseases in Minas Gerais state, in Southeast Brazil.

The Voronoi Based Scan (VBScan)[1] is a fast method for the detection and inference of point data set space-time disease clusters. 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 Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates sub-trees which are the potential space-time clusters, which are evaluated through the scan statistic [2]. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work we modify VBScan to find the best partition dividing the map into multiple low and high risk regions.

Objective

We describe a method to determine the partition of a map consisting of point event data, identifying all the multiple significant anomalies, which may be of high or low risk, thus monitoring the existence of possible outbreaks.

With the increase in GPS enabled devices, pin-point spatial data is an obvious future growth area for cluster detection research. The FBSSS handles binary labelled point data, but requires Monte Carlo testing to obtain inference [1]. In the Bayesian Poisson SSS [2], Monte Carlo is replaced by use of historic data, manifoldly speeding up processing. Following [2], [3] derived the BBSSS, replacing historic data with expert knowledge on cluster relative risk. This paper compares the spatial accuracy of BBSSS and FBSSS using new measure [4] which, being independent of inference level, permits direct comparison between Bayesian and frequentist methods. To compare the spatial accuracy of a Bayesian Bernoulli spatial scan statistic (BBSSS) and the frequentist Bernoulli spatial scan statistic (FBSSS), using benchmark trials.

As part of the epidemiological investigation of an outbreak of Q fever in a factory in Scotland, we aimed to utilise a spatial scan statistic to aid in identification of areas associated with increased relative risk of infection.

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 varying degrees of penalization parameters imposed to the freedom of shape. Even when a fairly complete set of solutions is available, the choice of the most appropriate parameter setting is left to the practitioner, whose decision is often subjective.

Objective

We propose a novel approach to the delineation of irregularly shaped disease clusters, treating it as a multi-objective optimization problem. We present a new insight into the geographic meaning of the cluster solution set, providing a quantitative approach to the problem of selecting the most appropriate solution among the many possible ones.

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).

Objective

This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk. These models are based on methods that explicitly track illness as a traveling wave across local geography.

Objective

To characterize yearly spatial epidemic waves of respiratory illness to identify early hotspots of infection.