Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters.ope

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring

<p>Abstract</p> <p>Background</p> <p>Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic.</p> <p>Results</p> <p>Based on the <it>flexible purely spatial scan statistic</it>, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic.</p> <p>Conclusion</p> <p>The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.</p

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

<p>Abstract</p> <p>Background</p> <p>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 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.</p> <p>Results</p> <p>A 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.</p> <p>Conclusions</p> <p>The 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.</p

Estimating the patterns and consequences of malaria transmission dynamics on fine spatial scales

Plasmodium falciparum is the leading cause of malaria infection and a major cause of morbidity and mortality across the globe, particularly in the African region. The burden of malaria is unevenly distributed, with some countries, districts or even households within villages harboring a disproportionally higher burden. There is an intricate relationship between the mosquito vector, humans and the parasites they carry, and how they interact with the environment. Small movements on a fine-scale lead to the patterns observed in the community. Quantifying transmission dynamics on a fine-scale, how malaria infections spread locally and the processes leading to the observed spatial and temporal distribution patterns is important for many aspects of malaria epidemiology, in particular, the design of targeted interventions against malaria, the design of studies to evaluate the effectiveness of vector control in the field, and the parameterization of mathematical models to predict the likely impact of interventions for settings where data is not available. Mathematical and statistical models have been developed to quantify fine scale malaria transmission dynamics and investigate the effects of interventions. Since data on the spread of vectors and parasites is challenging to collect, it is not available from many endemic settings for analytic methods to provide estimates, or to validate model predictions. Due to variability between settings, findings from one setting cannot be easily generalized. There is thus a need to involve methods that can extract information from imperfect but available datasets, to make the most of the existing data sources from settings with a variety of characteristics. The overall aim of this thesis was to use statistical and mathematical modelling approaches to characterize fine scale malaria transmission dynamics and their consequences on the measurement of heterogeneity on a local scale for targeted interventions. Chapter 2 used an established comprehensive simulator of malaria epidemiology developed at the Swiss Tropical and Public Health Institute (Swiss TPH) to predict the proportion of malaria infections that are in mosquitoes and humans and how this varies by setting specific characteristics. A substantial proportion of infections was predicted to be in mosquitoes, to vary with setting specific characteristics, and in response to interventions. The predictions also highlighted the role of the dynamics of infections in humans and mosquitoes following the introduction or scale-up of interventions. In Chapter 3, a statistical model which takes into account movement between houses in a village to estimate how far and where mosquitoes fly to in the presence of spatial repellents was developed. This was a secondary use of data on mosquito densities. The method evaluation using simulation showed that the model could be used as a potential tool to gain information on mosquito movement, estimating the distance between the houses the mosquitoes were repelled from and the houses they move to, the proportion of mosquitoes repelled, and the proportion of repelled mosquitoes moving to another house as opposed to somewhere outside. However, the trial data needs to contain sufficient information to be able to disentangle the effects of the underlying processes and provide accurate estimates for all the parameters. We found that additional data on the total number of mosquitoes and sufficient numbers of mosquitoes repelled were required in the case of the motivating trial. Findings from the simulations could inform the design of studies and help quantify criteria for trial settings. In Chapter 4, a simulation method was developed and applied to data on parasite genotypes from Kilifi County, Kenya. A previous study found an interaction between time and geographical distance on the genetic difference between pairs of parasite genotypes: genetic differences were lower for pairs of parasite genotypes which were evaluated within a shorter time interval and found within a shorter geographic distance apart. A stochastic individual-based model of malaria infections, people and homesteads was developed and fitted to the genetic differences in order to investigate hypotheses and parameter values consistent with the observed interaction. The observed interaction could be reproduced by the individual-based model. Although hypothesis about immunity to previously seen genotypes, and or a limit on the number of current infections per individual could not be ruled out, they were not necessary to account for the observed interaction. The mean geographical distance between parent and offspring infections was estimated to be 0.40km (95%CI 0.24 – 1.20), in the base model. This was the first modeling study that we know of which has attempted to estimate parameter values and test hypotheses from malaria genotyping data with a low coverage of infections in a setting with moderate transmission. The findings glean some insights on how simulation can be used in quantifying factors driving transmission, and in estimating unknown parameters when analytic methods are limited. The work in Chapter 5 uses the simulation model developed in Chapter 4 to investigate how the method chosen, local seasonality and movement of infections influence the detection of areas of higher transmission on fine spatial scales for targeted interventions. Our findings show that the identification of hotspots was less accurate when there was a gentle decay in risk from the hotspot boundary, the hotspot was irregularly shaped, there was seasonality in the area or when the mean distance between parent and offspring infections was longer. The findings highlight the importance of setting characteristics, the choice of outcome, and method of detection on the accuracy of identifying areas of higher transmission for targeted interventions. The underlying fine scale transmission dynamics should be taken into account when performing and interpreting analyses of heterogeneity for targeted interventions. Taken as a whole, this thesis provides information on the characteristics of transmission dynamics on a fine scale. It highlights that a substantial proportion of malaria infections are in mosquitoes, and places emphasis on the role that vectors, and humans play in the spread of infections and the implications of fine scale movement for the measurement of heterogeneity for targeted interventions. The estimates have implications for the design and evaluation of malaria control and elimination interventions

Non-recurrent traffic congestion detection on heterogeneous urban road networks

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network