scholarly journals Comparing Automated Cluster Detection Methods for Carbapenem-Resistant Enterobacteriaceae (CRE): Rule-Based Versus Statistical

2020 ◽  
Vol 41 (S1) ◽  
pp. s27-s27
Author(s):  
Rany Octaria ◽  
Hannah Griffith ◽  
Matthew Estes ◽  
Caleb Wiedeman ◽  
Allison Chan ◽  
...  
Keyword(s):  
Public Health ◽  
Detection Method ◽  
Space Time ◽  
Cluster Detection ◽  
Detection Methods ◽  
Onset Date ◽  
Time Interval ◽  
Outbreak Response ◽  
Rule Based ◽  
Zip Code

Background: The Tennessee (TN) Department of Health (TDH) has been identifying clusters of reportable conditions using the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE), a cluster detection method using space-time scan permutation statistics based on patient ZIP code. CRE are reportable in Tennessee; isolate submission is required for carbapenemase (CP) production and resistance mechanism (eg, KPC gene) testing. The Council for Outbreak Response: Healthcare-Associated Infections (HAI) and Antimicrobial-Resistant (AR) Pathogens (CORHA) released proposed thresholds of reporting CRE to public health. Thresholds vary by healthcare facility type and regional epidemiology. The TDH HAI/AR program currently runs a daily automated SAS code using the CORHA reporting threshold to help public health identify suspect KPC clusters. We evaluated our rule-based CORHA method against 2 space-time statistic-based methods for KPC cluster detection in Tennessee. Methods: Simulations for each cluster detection method were performed using retrospective CP-CRE surveillance data for 2018. Simulations were conducted using (1) CORHA reporting thresholds by facility case count to flag clusters of 2 or more cases within 28 days, (2) ESSENCE using patient residence ZIP code and the earliest of collection date or symptom onset date as is used for other reportable conditions in Tennessee, and (3) a modified space-time statistical method using SaTScan in which reporting facility, rather than a geographic location, was used as space variable to detect within-facility clusters within 1–28 days. We compared the number and overlap of cases and clusters identified with each method. Univariate logistic regression with CORHA flagging as predictor and flagging by each ESSENCE or CORHA method as outcome variables, were used to compare cases tagged by each method pair, respectively. Results: Of 183 KPC CP-CRE cases, 54 (30.6%) were flagged as part of suspect clusters by at least 1 method. Simulations generated 16 alerts (36 cases) using CORHA, 10 clusters (25 cases) using modified SaTScan, and 10 clusters (20 cases) using standard ESSENCE protocol. Among KPC CP-CRE cases flagged by CORHA, 12 (33.3%) were also flagged by modified SaTScan and 2 (5%) by ESSENCE. A case flagged using CORHA method has 5.15 (95% CI, 2.10–12.64) times higher odds of also being flagged by the modified SaTScan method compared to cases not flagged by CORHA. Conclusions: An algorithm based on CORHA thresholds for reporting CRE to public health had strong agreement with modified SaTScan, a space-time method. We intend to explore the extension of the time interval for ESSENCE.Funding: NoneDisclosures: None

scholarly journals Detection of COVID-19 case clusters in Québec, May–October 2020

2021 ◽  
Author(s):  
Germain Lebel ◽  
Élise Fortin ◽  
Ernest Lo ◽  
Marie-Claude Boivin ◽  
Matthieu Tandonnet ◽  
...  
Keyword(s):  
Public Health ◽  
Space Time ◽  
Cluster Detection ◽  
Detection Analysis ◽  
Health Authorities ◽  
Dissemination Area ◽  
Public Health Authorities ◽  
Health Surveillance Data ◽  
Software Information ◽  
Regional Public Health

Abstract Objectives The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved. Methods New COVID-19 cases are supplied by the “Trajectoire de santé publique” information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan software. Information on detected clusters is disseminated daily via an online interactive mapping interface. Results The number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montréal region was the most affected. Conclusion Considering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.

scholarly journals Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0172383 ◽  
Author(s):  
Boris Kauhl ◽  
Jeanne Heil ◽  
Christian J. P. A. Hoebe ◽  
Jürgen Schweikart ◽  
Thomas Krafft ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Space Time ◽  
Cluster Detection ◽  
Surveillance Data ◽  
Detection Methods ◽  
Weighted Regression ◽  
Time Analysis ◽  
Regression Modelling

scholarly journals Detection of a Salmonellosis Outbreak using Syndromic Surveillance in Georgia

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Rene Borroto ◽  
Jessica Pavlick ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Food Poisoning ◽  
Cluster Detection ◽  
Free Text ◽  
Family Reunion ◽  
Text Query ◽  
Diarrhea Syndrome ◽  
Zip Code

ObjectiveDescribe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia.IntroductionEvidence about the value of syndromic surveillance data for outbreak detection is limited (1). In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018.MethodsSendSS-SS and SAS were used for cluster detection of Emergency Department (ED) patients with similar Chief Complaint (CC), Triage Notes (TN), or Discharge Diagnoses (DDx) by facility, time of ED visit, and zip code / county of residence. A SAS-based free-text query related to food poisoning in the CC and DDx fields was also performed on a daily basis. County- and hospital-specific charting of the Diarrhea syndrome was also conducted in SendSS-SS, whereas county- and zip code-specific charting of the same syndrome were done in both SendSS-SS and SAS (2).ResultsOn Sunday July 29th, 2018, three children and three adults were seen within 18 hours at the ED of Hospital A in Camden County, Georgia. All patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held the day before. This early cluster was detected by the SAS-based free-text query of ‘food poisoning’ and the SAS-based cluster detection tool for patients with Diarrhea syndrome. The District Epidemiologists (DE) in the Coastal Health District were notified on Monday, July 30th, 2018. One-year high daily spikes of the Diarrhea syndrome occurred from July 29th to July 31st, 2018 in a local hospital ED (Fig 1), Camden County, and zip code 31548. Two HIPAA-compliant line lists with a total of 27 patients seen at EDs were emailed to the DEs to support active case finding. No further spikes of the Diarrhea syndrome were detected in Camden County during the 2-week period after the 3-day spike.ConclusionsSyndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring.References1.R Hopkins, C Tong, H Burkom, et al. A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017; 132(Supplement1): 116S-126S.2. G Zhang, A Llau, J Suarez, E O'Connell, E Rico, R Borroto, F Leguen. Using ESSENCE to Track a Gastrointestinal Outbreak in a Homeless Shelter in Miami-Dade County, 2008. Advances in Disease Surveillance. 2008; 5:139. 

An evaluation of SaTScan for the prospective detection of space-timeCampylobacterclusters in the North East of England

2013 ◽  
Vol 141 (11) ◽  
pp. 2354-2364 ◽  
Author(s):  
G. J. HUGHES ◽  
R. GORTON
Keyword(s):  
Detection Method ◽  
Space Time ◽  
Cluster Detection ◽  
Routine Laboratory ◽  
Epidemiological Investigation ◽  
Circular Window ◽  
North East ◽  
The North ◽  
Laboratory Surveillance ◽  
Sporadic Cases

SUMMARYCampylobacteriosis is the commonest cause of bacterial enteritis in England yet the epidemiology of apparently sporadic cases is not well understood. Here we evaluated the feasibility of applying a space-time cluster detection method to routine laboratory surveillance data in the North East of England by simulating prospective weekly space-time cluster detection using SaTScan as if it had been performed for 2008–2011. From the 209 simulated weekly runs using a circular window, 20 distinct clusters were found which contained a median of 30 cases (interquartile range 15–66) from a median population of ∼134 000 persons. This corresponds to detection of a new cluster every 10 weeks. We found significant differences in age, sex and deprivation score distributions between areas within clusters compared to those without. The results of this study suggest that space-time detection ofCampylobacterclusters could be used to find groups of cases amenable to epidemiological investigation.

Dualism loop and string

2019 ◽  
Author(s):  
Vitaly Kuyukov
Keyword(s):  
Space Time ◽  
Time Interval

the holographic space-time interval leads to the concept of dualism between loops and strings.

A brief review of diagnosis of small ruminants brucellosis

2020 ◽  
Vol 28 ◽  
Author(s):  
Jingjing Ren ◽  
Qisheng Peng
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Economic Impact ◽  
False Positive ◽  
Brucella Melitensis ◽  
Small Ruminants ◽  
Animal Husbandry ◽  
Detection Methods ◽  
Immunological Tests

: Brucellosis caused by bacteria of the genus of Brucella remains a major zoonosis in the widely world, which is an infectious disease with a severe economic impact on animal husbandry and public health. The genus of Brucella includes ten species and the most prevalent is Brucella melitensis. The diagnosis of Brucella melitensis ruminant brucellosis is based on bacteriological and immunological tests. The use of vaccines and the false-positive serological reactions (FPSR) caused by other cross-reacting bacteria represent the immunological contexts. This complex context results in the development of the large number of diagnosis of Brucella melitensis brucellosis. The aim of this article is to briefly review the detection methods and compare the superiorities of different tests.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3649
Author(s):  
Yosuke Tomita ◽  
Tomoki Iizuka ◽  
Koichi Irisawa ◽  
Shigeyuki Imura
Keyword(s):  
Gold Standard ◽  
Detection Method ◽  
Standard Measure ◽  
Detection Methods ◽  
Phase Identification ◽  
Foot Pressure ◽  
Pressure System ◽  
Speed Skating ◽  
Stance Time ◽  
Integrated Detection

Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating.

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