Optimizing the maximum reported cluster size in the spatial scan statistic for survival data

Background The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. Results We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. Conclusions Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images

Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented.

A time-course study of actively stained mouse brains: DTI parameter and connectomic stability over one year

ABSTRACTWhile the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed ex-vivo tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy across twelve representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first two weeks after fixation. The same parameters were stable over a two to eight-week window after fixation which means confounds from tissue stability over that scanning window are minimal. Quantitative connectomes were analyzed over the same time period with extension out to one year. While there is some change in the scalar metrics at one year after fixation, these changes are sufficiently small, particularly in white matter to support reproducible connectomes over a period ranging from two weeks to one year post fixation. These findings delineate a stable scanning period during which brain volumes, diffusion scalar metrics and connectomes are remarkably stable.

Scanning window analysis of non-coding regions within normal-tumor whole-genome sequence samples

Genomics has benefited from an explosion in affordable high-throughput technology for whole-genome sequencing. The regulatory and functional aspects in non-coding regions may be an important contributor to oncogenesis. Whole-genome tumor-normal paired alignments were used to examine the non-coding regions in five cancer types and two races. Both a sliding window and a binning strategy were introduced to uncover areas of higher than expected variation for additional study. We show that the majority of cancer associated mutations in 154 whole-genome sequences covering breast invasive carcinoma, colon adenocarcinoma, kidney renal papillary cell carcinoma, lung adenocarcinoma and uterine corpus endometrial carcinoma cancers and two races are found outside of the coding region (4 432 885 in non-gene regions versus 1 412 731 in gene regions). A pan-cancer analysis found significantly mutated windows (292 to 3881 in count) demonstrating that there are significant numbers of large mutated regions in the non-coding genome. The 59 significantly mutated windows were found in all studied races and cancers. These offer 16 regions ripe for additional study within 12 different chromosomes—2, 4, 5, 7, 10, 11, 16, 18, 20, 21 and X. Many of these regions were found in centromeric locations. The X chromosome had the largest set of universal windows that cluster almost exclusively in Xq11.1—an area linked to chromosomal instability and oncogenesis. Large consecutive clusters (super windows) were found (19 to 114 in count) providing further evidence that large mutated regions in the genome are influencing cancer development. We show remarkable similarity in highly mutated non-coding regions across both cancer and race.

Detecting spatial-temporal clusters of violent behavior in South Korea with space-time permutation scan statistics

Purpose The purpose of this paper is to detect spatial-temporal clusters of violence in Gwanak-gu, Seoul with space-time permutation scan statistics (STPSS) and identifies the temporal threshold for such detection to alert law enforcement officers quickly. Design/methodology/approach The case study was the Gwanak Police Station Call Database 2017 where civilian calls reporting violence were georeferenced with coordinated points. In analyzing the database, this study used the STPSS requiring only individual case data, such as time and location, to detect clusters of investigated phenomena. This study executed a series of experiments using different minimum and maximum temporal thresholds in detecting clusters of violence. Findings Results of the STPSS analyses with different temporal thresholds detected spatial-temporal clusters in Gwanak-gu. Number, location and duration of clusters depended on the temporal settings of the scanning window. Among four models, a model allowing the possible clusters to be detected within a 7-day minimum and 30-day maximum temporal threshold was more representative of reality than other models. Originality/value This study illustrates the clustering of violence with the STPSS by detecting spatial-temporal clusters of violence and identifying the appropriate temporal threshold in detecting such clusters. Identification of such a threshold is useful to alert law enforcement officers quickly and enables them to allocate their resources optimally.

Spatio-Temporal Cluster Detection for Legionellosis using Multiple Patient Addresses

ObjectiveTo improve timeliness and sensitivity of legionellosis clusterdetection in New York City (NYC) by using all addresses availablefor each patient in one analysis.IntroductionThe Bureau of Communicable Disease (BCD) at the NYCDepartment of Health and Mental Hygiene performs daily automatedanalyses using SaTScan to detect spatio-temporal clusters for37 reportable diseases.1Initially, we analyzed one address per patient,prioritizing home address if available. On September 25, 2015, aBCD investigator noticed two legionellosis cases with similar workaddresses. A third case was identified in a nearby residential facility,and an investigation was initiated to identify a common exposuresource. Four days later, after additional cases living nearby werereported, the SaTScan analysis detected a corresponding cluster.In response to this signaling delay, we implemented a multiple address(MA) analysis to improve upon single address (SA) analyses by usingall location data available on possible exposure sites.2MethodsPositiveLegionellatest results for NYC residents are reported toBCD with patient demographic and address data. BCD interviews allcases to elicit additional locations of potential exposure and enters theaddresses into a disease surveillance database (Maven). Addressesare assigned X/Y coordinates in near real-time via integration with ageocoding webservice.We used the prospective space-time permutation scan statistic inSaTScan,3enabling the advanced input feature on the spatial neighborstab to “include location ID in the scanning window if at least one setof coordinates is included.” This option considered a case as includedin a given cluster ifanyof the case’s addresses were within the cluster.The case file included: unique case ID (as the location ID), number ofcases, onset date, and day of week. The coordinate file included: caseID and X/Y coordinates for each address per case, resulting in one ormore rows per case. We searched for alive clusters with a temporalrange of 2 to 30 days and a maximum spatial size of 50% of observedcases. The study period was 1 year. Monte Carlo simulations (N=999)were used to determine statistical significance.We mimicked prospective surveillance to determine when theSeptember 2015 cluster would have been detected had this analysisbeen in place, by performing daily SA and MA analyses fromSeptember 21 (when the first outbreak-linked case was reported)to September 29 (when the initial SaTScan analysis signaled). Anycluster with a recurrence interval (RI)≥100 days was summarized ina map and linelist. Prospective, automated analyses were launchedin April 2016 and run daily using Microsoft Task Scheduler, SAS9.4, and SaTScan 9.4.1. Signals through July 2016 were summarized.ResultsIn mimicked prospective analysis, the SA and MA SaTScananalyses identified clusters of 13 and 11 cases, respectively, startingSeptember 27, 2015. The MA cluster was more spatially focused(2.11 km vs. 5.42 km) and more unlikely to occur by chance alone(RI of 16,256 days vs. 8,758 days). In prospective analyses, a MAcluster of 6 cases was identified on July 5, 2016 with a radius of1.69 km (RI=100 days). On July 6, the MA cluster case countincreased to 7 and maintained the same radius (RI=685 days), whilea cluster of the same 7 cases was identified by the SA analysis witha larger radius (1.97 km) and lower RI (292 days). The RI for bothclusters peaked on July 7 (MA: 2348 days, SA: 713 days).ConclusionsIn preliminary evaluation, the MA analysis facilitated clusterdetection using non-residential possible exposure sites, such asworkplaces. Timeliness was slightly improved, but the larger practicalbenefit was identifying more spatially focused clusters. Smallerclusters are useful for more precisely targeting legionellosis infectionsource identification and remediation activities, especially in urbanenvironments with high population and building densities.