Regression Models for Time Series Analysis

Technometrics ◽  
2003 ◽  
Vol 45 (4) ◽  
pp. 364-364
Author(s):  
Bonnie K Ray
Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  

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.


2017 ◽  
Vol 38 (4) ◽  
pp. 430-435 ◽  
Author(s):  
Craig W. Bradley ◽  
Martyn A. C. Wilkinson ◽  
Mark I. Garvey

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


Author(s):  
Michelle Degli Esposti ◽  
Hisham Ziauddeen ◽  
Lucy Bowes ◽  
Aaron Reeves ◽  
Adam M. Chekroud ◽  
...  

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.


2021 ◽  
Vol 15 ◽  
Author(s):  
R S M Lakshmi Patibandla ◽  
B. Tarakeswara Rao ◽  
M. Ramakrishna Murty ◽  
V. Lakshman Narayana

: 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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