Spatial Analysis

Legionellosis is a respiratory disease that can lead to serious illness such as pneumonia, and can even result in death. Since 2010, increased reports of legionellosis have been received in Toronto during the summer months and led to a five-fold increase by 2012. This underscored the need to rule out common sources through a rapid assessment of exposure data (i.e., locations visited) for any spatio-temporal links. Legionella bacteria from a single source can affect individuals at distances as great as 10 km (1) but dispersion of Legionella bacteria is generally within 1 km of the source (2). This information was used to describe an area of potential risk around each exposure location. Adding temporal information from dates of potential exposures could provide a useful tool for outbreak detection. An automated tool was developed to link spatial and temporal data to assess need for further follow up.

Objective:

To develop an outbreak detection tool which uses spatial information related to temporally clustered legionellosis cases reported in Toronto, Canada.

Spatial cluster analysis is considered an important technique for the elucidation of disease causes and epidemiological surveillance. Kulldorff's spatial scan statistic, defined as a likelihood ratio, is the usual measure of the strength of geographic clusters. The circular scan, a particular case of the spatial scan statistic, is currently the most used tool for the detection and inference of spatial clusters of disease.

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 in an even manner for all possible cluster sizes. We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

Objective

We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise. It has been suggested that the syphilis epidemic in the US is a "rare example of unforced, endogenous oscillations in disease incidence, with an 8-11-yr period that is predicted by the natural dynamics of syphilis infection, to which there is partially protective immunity." While the time series of aggregate case counts seems to support this claim, between 1990 and 2010 there seems to have been a significant change in the spatial distribution of the syphilis epidemic. It is unclear if this change can also be attributed to "endogenous" factors or whether it is due to exogenous factors such as behavioral changes (e.g., the widespread use of the internet for anonymous sexual encounters). For example, it is pointed out that levels of syphilis in 1989 were abnormally high in counties in North Carolina (NC) immediately adjacent to highways. The hypothesis was that this may be due truck drivers and prostitution, and/or the emerging cocaine market. Our results indicate that syphilis distribution in NC has changed since 1989, diffusing away from highway counties.

Objective

To study the spatial distribution of syphilis at the county level for specific states and nationally, and to determine how this might have changed over time in order to improve disease surveillance.

The spatial scan statistic proposed by Kulldorff has been widely used in spatial disease surveillance and other spatial cluster detection applications. In one of its versions, such scan statistic was developed for inhomogeneous Poisson process. However, the underlying Poisson process may not be suitable to properly model the data. Particularly, for diseases with very low prevalence, the number of cases may be very low and zero excess may cause bias in the inferences.

Lambert introduced the zero-inflated Poisson (ZIP) regression model to account for excess zeros in counts of manufacturing defects. The use of such model has been applied to innumerous situations. Count data, like contingency tables, often contain cells having zero counts. If a given cell has a positive probability associated to it, a zero count is called a sampling zero. However, a zero for a cell in which it is theoretically impossible to have observations is called structural zero.

Objective

The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisson processes. However, real data may present substantial departure from the underlying Poisson process. One of the possible departures has to do with zero excess. Some studies point out that when applied to zero-inflated data the spatial scan statistic may produce biased inferences. Particularly, Gomez-Rubio and Lopez-Quılez argue that Kulldorff’s scan statistic may not be suitable for very rare diseases problems. In this work we develop a closed-form scan statistic for cluster detection of spatial count data with zero excess.

Ordering-based approaches [1,2] and quadtrees [3] have been introduced recently to detect multiple spatial clusters in point event datasets. The Autonomous Leaves Graph (ALG) [4] is an efficient graph-based data structure to handle the communication of cells in discrete domains. This adaptive data structure was favorably compared to common tree-based data structures (quad-trees). An additional feature of the ALG data structure is the total ordering of the component cells through a modified adaptive Hilbert curve, which links sequentially the cells (the orange curve in the example of Figure 1).

Objective

To detect multiple significant spatial clusters of disease in case-control point event data using the Autonomous Leaves Graph and the spatial scan statistic.

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control. Most spatio-temporal models have been developed for retrospective analyses of complete data sets (4). However, data in public health registries accumulate over time and sequential analyses of all the data collected so far is a key concept to early detection of disease outbreaks. When the analysis of spatially aggregated data on multiple diseases is of interest, the use of multivariate models accounting for correlations across both diseases and locations may provide a better description of the data and enhance the comprehension of disease dynamics.

Objective

This study deals with the development of statistical methodology for on-line surveillance of small area disease data in the form of counts. As surveillance systems are often focused on more than one disease within a predefined area, we extend the surveillance procedure to the analysis of multiple diseases. The multivariate approach allows for inclusion of correlation across diseases and, consequently, increases the outbreak detection capability of the methodology

Seasonality has a major effect on the spatial and temporal (i.e. spatiotemporal) dynamics of natural systems and their populations (1). Although the seasonality of influenza in temperate countries is widely recognized, inter-regional spread of influenza in the United States has not been well characterized.

Objective

To study the seasonality of influenza in the United States between 1972 and 2007 through the evaluation of the timing, velocity, and spatial spread of annual epidemic cycles.