scholarly journals Spatio-Temporal Cluster Detection for Legionellosis using Multiple Patient Addresses

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Eric R. Peterson ◽  
Sharon K. Greene
Keyword(s):  
New York ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Onset Date ◽  
Preliminary Evaluation ◽  
Case File ◽  
Temporal Cluster ◽  
Address Data ◽  
Scanning Window ◽  
Spatio Temporal

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.

scholarly journals Temporal cluster of wound botulism in the United Kingdom

2002 ◽  
Vol 6 (9) ◽  
Author(s):  
S O’Brien ◽  
Alasdair Reid ◽  
A C de Benoist
Keyword(s):  
Public Health ◽  
United Kingdom ◽  
Environmental Health ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Laboratory Service ◽  
Wound Botulism ◽  
The Public ◽  
Temporal Cluster ◽  
The United Kingdom

Five clinical cases of wound botulism have been reported to the Public Health Laboratory Service (PHLS) Communicable Disease Surveillance Centre and the Scottish Centre for Infection and Environmental Health since the beginning of February 2002 (1,2).

scholarly journals Investigating a Syndromic Surveillance Signal with Complimentary Data Systems

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Hilary B. Parton ◽  
Robert Mathes ◽  
Jasmine Abdelnabi ◽  
Lisa Alleyne ◽  
Andrea Econome ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Communicable Disease ◽  
Data Systems ◽  
Sales Promotions ◽  
Over The Counter ◽  
Syndromic Surveillance System ◽  
Diarrheal Illness

In early June, the New York City syndromic surveillance system detected five signals in sales of over-the-counter antidiarrheal medications. To determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems. After further investigation of syndromic and other systems, we determined that findings possibly reflected sales promotions but did not suggest increased diarrheal illness in NYC.

scholarly journals Challenges to Implementing Communicable Disease Surveillance in New York City Evacuation Shelters after Hurricane Sandy, November 2012

2015 ◽  
Vol 130 (1) ◽  
pp. 48-53 ◽  
Author(s):  
Alison D. Ridpath ◽  
Brooke Bregman ◽  
Lucretia Jones ◽  
Vasudha Reddy ◽  
HaeNa Waechter ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Hurricane Sandy

scholarly journals Outbreak of Salmonella Enteritidis PT 14b in the United Kingdom - update

2002 ◽  
Vol 6 (41) ◽  
Author(s):  
S O'Brien ◽  
L Ward ◽  
I. S.T. Fisher
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Salmonella Enteritidis ◽  
Communicable Disease ◽  
Enteric Pathogens ◽  
Onset Date ◽  
Laboratory Service ◽  
The Public ◽  
Foreign Travel ◽  
The United Kingdom

One hundred and thirty four confirmed cases of Salmonella Enteritidis PT 14b (not known to be linked with foreign travel) have been reported to the Public Health Laboratory Service Communicable Disease Surveillance Centre in England (PHLS CDSC) by the PHLS Laboratory of Enteric Pathogens (LEP) since 26 September 2002 (1,2). The earliest onset is 3 September 2002 and the latest onset date reported so far is 1 October 2002 (figure). Eight people are known to have been admitted to hospital.

scholarly journals Comparing spatio-temporal methods of non-communicable disease surveillance.

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mico Hamlyn ◽  
Frederic B. Piel
Keyword(s):  
Bayesian Methods ◽  
Disease Surveillance ◽  
Small Area ◽  
Roc Analysis ◽  
Communicable Disease ◽  
Cluster Detection ◽  
Simulation Framework ◽  
Disease Clusters ◽  
Spatio Temporal ◽  
Non Communicable Disease

ObjectiveTo determine the merits of different surveillance methods for cluster detection, in particular when used in conjuction with small area data. This will be investigated using a simulated framework. This is with a view to support further surviellance work using real small area data.IntroductionHealth surveillance is well established for infectious diseases, but less so for non-communicable diseases. When spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. Our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. SatScan and Cusum) and Bayesian (incl. BaySTDetect and Dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease.MethodsCount data were generated using various random effects (RE). A subset of areas was randomly given an increased relative risk (RR) to simulate disease clusters. Simulations were conducted in R using a grid of 625 areas. We used 12 times= nteps within a hierarchical Poisson model. Multiple values of model parameters, including REs and the RR within clusters, were then tested. The range of RE (values) was derived from real-world data from England on common and rare diseases. RR ranging between 1.2 and 1.8 were tested to reflect both low and high exposures to pollutants and other risk factors. ROC analysis, based on 50 simulations, was used to assess the performance of each statistical method for each combination of parameter values.ResultsOur ROC analysis suggested that SaTScan usually had the highest specificity at low sensitivities (<0.5), although its maximum sensitivity was often lower than when using the Bayesian methods. In scenarios where the RR within clusters was lower, all methods had less sensitivity at a given specificity. Cusum usually performed quite similarly to SatScan, while the two Bayesian methods considered often misidentified a high proportion of disease clusters. P-values generated by SaTScan need to be considered with caution as they did not relate closely with the sensitivity or specificity of the ROC curves from our simulations.ConclusionsReal-world investigations of spatio-temporal signals (e.g. disease clusters) are often complex and time consuming. Identifying the best method to reduce the risks of identifying false positives and of missing real clusters is therefore essential. Despite the inherent constraints of theoretical simulations, such a framework allows to objectively assess the performance of different methods. Overall, our simulation framework suggested that SatScan would usually be the easiest, most user-friendly and best performing space-time methods for non-communicable disease surveillance.

HIV and other STIs in the United Kingdom: further increases

2003 ◽  
Vol 7 (48) ◽  
Author(s):  
◽  
Keyword(s):  
United Kingdom ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
People Living With Hiv ◽  
Health Protection Agency ◽  
Protection Agency ◽  
England And Wales ◽  
The United Kingdom ◽  
The Uk ◽  
Living With Hiv

The Health Protection Agency Communicable Disease Surveillance Centre for England and Wales and others have reported that the number of people living with HIV in the UK has increased

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

scholarly journals Improved inference for areal unit count data using graph-based optimisation

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Duncan Lee ◽  
Kitty Meeks ◽  
William Pettersson
Keyword(s):  
Random Effects ◽  
Count Data ◽  
Prior Distribution ◽  
Disease Surveillance ◽  
Sharing Rule ◽  
Markov Random ◽  
Spatial Correlation Structure ◽  
Conditional Autoregressive ◽  
Spatio Temporal ◽  
Special Case

AbstractSpatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

scholarly journals A spatio-temporal cluster analysis of structurally deficient bridges in the contiguous USA

2020 ◽  
Vol 4 ◽  
pp. 100034
Author(s):  
R.W. Amin ◽  
S. Kocak ◽  
H.E. Sevil ◽  
G.P. Peterson ◽  
J.T. Hamilton ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Temporal Cluster ◽  
Spatio Temporal

DETERMINANTS OF LEISURE-TIME PHYSICAL ACTIVITY: EVIDENCE FROM MALAYSIA

2014 ◽  
Vol 59 (02) ◽  
pp. 1450017 ◽  
Author(s):  
YONG KANG CHEAH ◽  
ANDREW K. G. TAN
Keyword(s):  
Physical Activity ◽  
Disease Surveillance ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Communicable Disease ◽  
Smoking Status ◽  
Sample Selection ◽  
Health Lifestyle ◽  
Non Communicable Disease ◽  
Heckman’S Sample Selection Model

This paper examines how socio-demographic and health-lifestyle factors determine participation and duration of leisure-time physical activity in Malaysia. Based on the Malaysia Non-Communicable Disease Surveillance-1 data, Heckman's sample selection model is employed to estimate the probability to participate and duration on physical activity. Results indicate that gender, age, years of education and family illness history are significant in explaining participation probability in leisure-time physical activity. Gender, income level, smoking-status and years of education are significant in explaining the weekly duration conditional on participation, whereas smoking-status and years of education are significant in determining the unconditional level of leisure-time physical activity.

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