Regression Models for Time Series Analysis

2002 ◽  
Author(s):  
Benjamin Kedem ◽  
Konstantinos Fokianos
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Regression Models ◽  
Series Analysis

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

The Effect of Universal Decolonization With Screening in Critical Care to Reduce MRSA Across an Entire Hospital

2017 ◽  
Vol 38 (4) ◽  
pp. 430-435 ◽  
Author(s):  
Craig W. Bradley ◽  
Martyn A. C. Wilkinson ◽  
Mark I. Garvey
Keyword(s):  
Time Series ◽  
United Kingdom ◽  
Time Series Analysis ◽  
Regression Models ◽  
Kernel Regression ◽  
Break Point ◽  
University Hospitals ◽  
Series Analysis ◽  
The United Kingdom ◽  
Break Points

OBJECTIVETo describe the effect of universal methicillin-resistant Staphylococcus aureus (MRSA) decolonization therapy in a large intensive care unit (ICU) on the rates of MRSA cases and acquisitions in a UK hospital.DESIGNDescriptive study.SETTINGUniversity Hospitals Birmingham (UHB) NHS Foundation Trust is a tertiary referral teaching hospital in Birmingham, United Kingdom, that provides clinical services to nearly 1 million patients every year.METHODSA break-point time series analysis and kernel regression models were used to detect significant changes in the cumulative monthly numbers of MRSA bacteremia cases and acquisitions from April 2013 to August 2016 across the UHB system.RESULTSPrior to 2014, all ICU patients at UHB received universal MRSA decolonization therapy. In August 2014, UHB discontinued the use of universal decolonization due to published reports in the United Kingdom detailing the limited usefulness and cost-effectiveness of such an intervention. Break-point time series analysis of MRSA acquisition and bacteremia data indicated that break points were associated with the discontinuation and subsequent reintroduction of universal decolonization. Kernel regression models indicated a significant increase (P<.001) in MRSA acquisitions and bacteremia cases across UHB during the period without universal decolonization.CONCLUSIONWe suggest that routine decolonization for MRSA in a large ICU setting is an effective strategy to reduce the spread and incidence of MRSA across the whole hospital.Infect Control Hosp Epidemiol 2017;38:430–435

scholarly journals Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis

2021 ◽  
Author(s):  
Michelle Degli Esposti ◽  
Hisham Ziauddeen ◽  
Lucy Bowes ◽  
Aaron Reeves ◽  
Adam M. Chekroud ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Psychiatric Disorders ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Admission Rates ◽  
Series Analysis ◽  
Binomial Regression ◽  
Bed Days

Abstract Purpose It is unclear how hospitals are responding to the mental health needs of the population in England, against a backdrop of diminishing resources. We aimed to document patterns in hospital activity by psychiatric disorder and how these have changed over the last 22 years. Methods In this observational time series analysis, we used routinely collected data on all NHS hospitals in England from 1998/99 to 2019/20. Trends in hospital admissions and bed days for psychiatric disorders were smoothed using negative binomial regression models with year as the exposure and rates (per 1000 person-years) as the outcome. When linear trends were not appropriate, we fitted segmented negative binomial regression models with one change-point. We stratified by gender and age group [children (0–14 years); adults (15 years +)]. Results Hospital admission rates and bed days for all psychiatric disorders decreased by 28.4 and 38.3%, respectively. Trends were not uniform across psychiatric disorders or age groups. Admission rates mainly decreased over time, except for anxiety and eating disorders which doubled over the 22-year period, significantly increasing by 2.9% (AAPC = 2.88; 95% CI: 2.61–3.16; p < 0.001) and 3.4% (AAPC = 3.44; 95% CI: 3.04–3.85; p < 0.001) each year. Inpatient hospital activity among children showed more increasing and pronounced trends than adults, including an increase of 212.9% for depression, despite a 63.8% reduction for adults with depression during the same period. Conclusion In the last 22 years, there have been overall reductions in hospital activity for psychiatric disorders. However, some disorders showed pronounced increases, pointing to areas of growing need for inpatient psychiatric care, especially among children.

Predictive Time Series Analysis and Forecasting of COVID-19 Dataset

2021 ◽  
Vol 15 ◽  
Author(s):  
R S M Lakshmi Patibandla ◽  
B. Tarakeswara Rao ◽  
M. Ramakrishna Murty ◽  
V. Lakshman Narayana
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Regression Models ◽  
Series Analysis ◽  
Health Authorities ◽  
Prediction Systems

: The extent of COVID-19 dataset within the entire ecosphere is dangerous for mankind. The possessions of a number of the most important financial practicality are anxious obligations to the massive impurity and transmissibility of this syndrome. To the rising extent of the cases and its ensuing hassle on the supervision and health authorities, approximately prediction systems would be vital to forecast the spread of cases within the imminent. In this paper, we've analyzed different regression models of machine learning for the forecasting of COVID-19, the prediction of varied constraints like a rise within the range of confirmed cases, amount of recovered cases and closed cases, etc. The levels attained by the recommended proposal are considered to predict the COVID-19 with the data supported statistic problems for day wise and week wise cases predictions.

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