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.

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.

scholarly journals Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1342 ◽  
Author(s):  
Yong Fan ◽  
Litang Hu ◽  
Hongliang Wang ◽  
Xin Liu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Analytical Solutions ◽  
Pumping Test ◽  
Learning Methods ◽  
Pumping Tests ◽  
Machine Learning Methods ◽  
Series Analysis ◽  
Pumping Wells

Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.

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

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