Impact of molecular testing on reported Clostridoides difficile infection rates

2019 ◽  
Vol 41 (3) ◽  
pp. 306-312 ◽  
Author(s):  
Iulian Ilieş ◽  
James C. Benneyan ◽  
Tiago Barbieri Couto Jabur ◽  
Arthur W. Baker ◽  
Deverick J. Anderson
Keyword(s):  
Change Point Detection ◽  
Nucleic Acid Amplification ◽  
Detection Methods ◽  
Molecular Testing ◽  
Infection Rates ◽  
Hospital Level ◽  
The Mean ◽  
Point Detection ◽  
The Impact ◽  
Individual Hospital

AbstractBackground:The reported incidence of Clostridoides difficile infection (CDI) has increased in recent years, partly due to broadening adoption of nucleic acid amplification tests (NAATs) replacing enzyme immunoassay (EIA) methods. Our aim was to quantify the impact of this switch on reported CDI rates using a large, multihospital, empirical dataset.Methods:We analyzed 9 years of retrospective CDI data (2009–2017) from 47 hospitals in the southeastern United States; 37 hospitals switched to NAAT during this period, including 24 with sufficient pre- and post-switch data for statistical analyses. Poisson regression was used to quantify the NAAT-over-EIA incidence rate ratio (IRR) at hospital and network levels while controlling for longitudinal trends, the proportion of intensive care unit patient days, changes in surveillance methodology, and previously detected infection cluster periods. We additionally used change-point detection methods to identify shifts in the mean and/or slope of hospital-level CDI rates, and we compared results to recorded switch dates.Results:For hospitals that transitioned to NAAT, average unadjusted CDI rates increased substantially after the test switch from 10.9 to 23.9 per 10,000 patient days. Individual hospital IRRs ranged from 0.75 to 5.47, with a network-wide IRR of 1.75 (95% confidence interval, 1.62–1.89). Reported CDI rates significantly changed 1.6 months on average after switching to NAAT testing (standard deviation, 1.9 months).Conclusion:Hospitals that switched from EIA to NAAT testing experienced an average postswitch increase of 75% in reported CDI rates after adjusting for other factors, and this increase was often gradual or delayed.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

2020 ◽  
Vol 12 (6) ◽  
pp. 1008 ◽  
Author(s):  
Ana Militino ◽  
Mehdi Moradi ◽  
M. Ugarte
Keyword(s):  
Error Probability ◽  
Type I Error ◽  
Remote Sensing Data ◽  
Change Point Detection ◽  
Change Points ◽  
Detection Methods ◽  
Type I ◽  
Power Of The Test ◽  
Type I Error Probability ◽  
Point Detection

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.

scholarly journals Evaluating the Impact of Increased Fuel Cost and Iran’s Currency Devaluation on Road Traffic Volume and Offenses in Iran, 2011–2019

Safety ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 49
Author(s):  
Milad Delavary ◽  
Zahra Ghayeninezhad ◽  
Martin Lavallière
Keyword(s):  
Time Series ◽  
Change Point ◽  
Road Traffic ◽  
Traffic Volume ◽  
Change Point Detection ◽  
Fuel Cost ◽  
Currency Devaluation ◽  
General Decline ◽  
Point Detection ◽  
The Impact

Trends and underlying patterns should be identified in the timely distribution of road traffic offenses to increase traffic safety. In this study, a time series analysis was used to study the incidence rate of road traffic violations on Iranian rural roads. Road traffic volume and offenses data from March 2011 to October 2019 were aggregated. Interrupted time series were used to evaluate the impact of increasing fuel cost in June of 2013 and July of 2014 and the currency devaluation of Rial vs. US dollars in July of 2017 on trends and patterns, traffic volume, and number of offenses. A change-point detection (CPD) analysis was also used to identify singular changes in the frequency of traffic offenses. Results show a general decline in the number of overtaking and speeding offenses of −24.31% and −13.23%, respectively, due to the first increase in fuel cost. The second increase only reduced overtaking by 20.97%. In addition, Iran’s currency devaluation reduced the number of overtaking offenses by 26.39%. Modeling a change-point detection and a Mann-Kendall Test of traffic offenses in Iran, it was found that the burden of violations was reduced.

scholarly journals Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

2020 ◽  
Author(s):  
Simon Letzgus
Keyword(s):  
Wind Turbine ◽  
Change Point ◽  
Significant Loss ◽  
Change Point Detection ◽  
Training Data ◽  
Change Points ◽  
Detection Methods ◽  
Full Potential ◽  
Normal Behaviour ◽  
Point Detection

Abstract. Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. The data is predominantly used to gain insights into turbine condition without the need for additional sensing equipment. Most successful approaches apply semi-supervised anomaly detection methods, also called normal behaivour models, that use clean training data sets to establish healthy component baseline models. However, one of the major challenges when working with wind turbine SCADA data in practice is the presence of systematic changes in signal behaviour induced by malfunctions or maintenance actions. Even though this problem is well described in literature it has not been systematically addressed so far. This contribution is the first to comprehensively analyse the presence of change-points in wind turbine SCADA signals and introduce an algorithm for their automated detection. 600 signals from 33 turbines are analysed over an operational period of more than two years. During this time one third of the signals are affected by change-points. Kernel change-point detection methods have shown promising results in similar settings but their performance strongly depends on the choice of several hyperparameters. This contribution presents a comprehensive comparison between different kernels as well as kernel-bandwidth and regularisation-penalty selection heuristics. Moreover, an appropriate data pre-processing procedure is introduced. The results show that the combination of Laplace kernels with a newly introduced bandwidth and penalty selection heuristic robustly outperforms existing methods. In a signal validation setting more than 90 % of the signals were classified correctly regarding the presence or absence of change-points, resulting in a F1-score of 0.86. For a change-point-free sequence selection the most severe 60 % of all CPs could be automatically removed with a precision of more than 0.96 and therefore without a significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data pre-processing which is key for data driven methods to reach their full potential. The algorithm is open source and its implementation in Python publicly available.

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

Sign in / Sign up

Export Citation Format

Share Document