scholarly journals Analysis of twenty-week time-series of confirmed cases of New Coronavirus COVID-19 and their simple short-term prediction for Georgia and Neighboring Countries (Armenia, Azerbaijan, Turkey, Russia) in amid of a global pandemic

2020 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Global Pandemic ◽  
Daily Data ◽  
The Mean ◽  
Upper Level ◽  
Predicted Values ◽  
Study Sites ◽  
Short Term Prediction

Results of a comparative statistical analysis of the daily data associated with New coronavirus COVID-19 infection of confirmed cases (Č) of the population in Georgia (GEO), Armenia (ARM), Azerbaijan (AZE), Turkey (TUR) and Russia (RUS) amid a global pandemic (WLD) in the period from March 14 to July 31, 2020 are presented. The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, a correlation and autocorrelation analysis of the observational data was carried out, the periodicity in the time- series of Č were revealed, the calculation of the interval prediction values of Č taking into account the periodicity in the time-series of observations from August 1 to 31, 2020 (ARM, AZE) and from August 1 to September 11, 2020 (WLD, GEO, TUR, RUS) were carried out. Comparison of real and calculated predictions data on Č in the study sites from August 1 to August 31, 2020 is carried out. It was found that daily, monthly and mean weekly real values of Č for all the studied locations practically fall into the 99% confidence interval of the predicted values of Č for the specified time period. A dangerous situation with the spread of coronavirus infection may arise when the mean weekly values of Č of the 99% upper level of the forecast confidence interval are exceeded within 1-2 weeks. Favorable - when the mean weekly values of Č decrease below 99% of the lower level of the forecast confidence interval.

scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their short-term interval prediction from September 2020 to February 2021

2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Infection Rate ◽  
Maximum Effect ◽  
Regression Equations ◽  
Daily Mortality ◽  
Daily Data ◽  
The World ◽  
Upper Level

AbstractIn the autumn - winter period of 2020, very difficult situation arose in Georgia with the course of the pandemic of the New Coronavirus COVID-19. In particular, in November-December period of 2020, Georgia eight days was rank a first in the world in terms of COVID-19 infection rate per 1 million populations.In this work results of a statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2020 to February 28, 2021 (for I - from December 05, 2020 to February 28, 2021) are presented. It also presents the results of the analysis of ten-day (decade) and two-week forecasting of the values of C, D and I, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/.The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained.Georgia’s ranking in the world for Covid-19 infection and deaths from September 1, 2020 to February 28, 2021 (per 1 million population) was determined. Georgia was in the first place: Infection - November 21, 22, 27, 28 and December 04, 05, 06, 09, 2020; Death - November 22, 2020.A comparison between the daily mortality from Covid-19 in Georgia from September 1, 2020 to February 28, 2021 with the average daily mortality rate in 2015-2019 was made. The largest share value of D from mean death in 2015-2019 was 36.9% (19.12.2020), the smallest - 0.9% (21.09.2020, 24.09.2020 - 26.09.2020).The statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 5450 (05.12.2020), R = 4599 (21.12.2020), D = 53 (19.12.2020), I = 30.1 % (05.12.2020). Maximum mean decade values of investigation parameters are following: C = 4337 (1 Decade of December 2020), R = 3605 (3 Decade of November 2020), D = 44 (2 Decade of December 2020), I = 26.8 % (1 Decade of December 2020).It was found that the regression equations for the time variability of the daily values of C, R and D have the form of a tenth order polynomial.Mean values of speed of change of confirmed -V(C), recovered - V(R) and deaths - V(D) coronavirus-related cases in different decades of months from September 2020 to February 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +104 cases/day (1 Decade of November 2020), V(R) = +94 cases/day (3 Decade of October and 1 Decade of November 2020), V(D) = +0.9 cases/day (1 Decade of November 2020).Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases from 05.12.2020 to 28.02.2021 is carried out. So, the maximum effect of recovery is observed 13-14 days after infection, and deaths - after 13-14 and 17-18 days.The scale of comparing real data with the predicted ones and assessing the stability of the time series of observations in the forecast period in relation to the pre-predicted one was offered.Comparison of real and calculated predictions data of C (23.09.2020-28.02.2021), D (01.01.2021-28.02.2021) and I (01.02.2021-28.02.2021) in Georgia are carried out. It was found that daily, mean decade and two-week real values of C, D and I practically falls into the 67% - 99.99% confidence interval of these predicted values for the specified time periods (except the forecast of C for 13.10.2020-22.10.2020, when a nonlinear process of growth of C values was observed and its real values have exceeded 99.99% of the upper level of the confidence interval of forecast).Alarming deterioration with the spread of coronavirus parameters may arise when their daily values are higher 99.99% of upper level of the forecast confidence interval. Excellent improvement - when these daily values are below 99.99% of the lower level of the forecast confidence interval.The lockdown introduced in Georgia on November 28, 2020 brought positive results. There are clearly positive tendencies in the spread of COVID-19 to February 2021.

Microbiological Determination of Neomycin in Feeds: Collaborative Study

1989 ◽  
Vol 72 (2) ◽  
pp. 237-241
Author(s):  
Gerald L Stahl ◽  
D Dal Kratzer ◽  
Charles W Kasson
Keyword(s):  
Statistical Analysis ◽  
Confidence Interval ◽  
Salt Concentration ◽  
Agar Medium ◽  
Collaborative Study ◽  
Tris Buffer ◽  
Official Method ◽  
The Mean ◽  
Swine Feed

Abstract A modification of the AOAC microbiological determination of neomycin in feeds was collaboratively studied by 12 laboratories. The official method was modified by substituting a constant salt concentration diluent for the feed extract diluent, preparing the agar medium in tris buffer, and performing the test with a monolayer plating system. Each laboratory performed single assays on 8 samples in a randomized sequence. The samples included duplicates of a cattle and swine feed at 2 different marketed concentrations. The mean recovery across all laboratories was 110.7% of theory with a range of means of 69.4-128.6 across the 12 laboratories. The results of one laboratory and 2 additional values from different laboratories were deemed outliers and excluded from statistical analysis. The statistical analysis gave a confidence interval of ± 26% for individual assays.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals Evaluation of Respiratory Muscular Strength Compared to Predicted Values in Patients with Stroke

2020 ◽  
Vol 17 (3) ◽  
pp. 1091 ◽  
Author(s):  
Sarah Maria Ramos ◽  
Daniela Maciel da Silva ◽  
Daniela Vieira Buchaim ◽  
Rogério Leone Buchaim ◽  
Mauro Audi
Keyword(s):  
Statistical Analysis ◽  
Muscle Strength ◽  
Respiratory Muscles ◽  
Mean Value ◽  
P Value ◽  
Men And Women ◽  
Mean Values ◽  
Significant Difference ◽  
The Mean ◽  
Predicted Values

The purpose of this study was to evaluate the inspiratory and expiratory muscle strength of individuals affected by stroke and to compare it with the predicted values in the literature considering their corresponding age. Respiratory muscle strength was evaluated in 22 elderly people who had sequels of stroke, four with right hemiparesis, 16 with left hemiparesis and two with bilateral, of ages ranging from 34 to 82 years. The collected data were submitted to statistical analysis using a Mann–Whitney test to evaluate if there was a significant difference in the average data collected when compared with a mean of the predicted data in the literature. Fourteen men and eight women were evaluated, who obtained mean values of 71.85 cmH2O and 57.75 cmH2O, respectively, for a maximal inspiratory pressure (MIP), and when compared to the predicted values for men and women, 105.41 cmH2O (p-value 0.0019) and 80.57 cmH2O (p-value 0.00464) were significantly lower. For a maximal expiratory pressure (MEP), the mean value obtained for men was 62.28 cmH2O and 49.5 cmH2O for women, whereas the predicted values in the literature were 114.79 cmH2O (p-value < 0.0001) and 78, 46 cmH2O (p-value 0.0059), respectively. In the statistical analysis, it was possible to notice that the studied population did not reach the predicted age indexes and that there was a significant difference between the median columns. In conclusion, there is a weakness in the respiratory muscles of hemiparetic men and women due to stroke.

Agreement between procalcitonin measurements using the new point-of-care testing ichroma™ reader and the automated Kryptor instrument

2019 ◽  
Vol 43 (3) ◽  
pp. 169-172
Author(s):  
Elisabetta Stenner ◽  
Giulia Barbati ◽  
Nicole West ◽  
Fabia Del Ben ◽  
Francesca Martin ◽  
...  
Keyword(s):  
Decision Making ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Point Of Care ◽  
Total Error ◽  
Kappa Statistic ◽  
Point Of Care Testing ◽  
Serum Samples ◽  
Reference Change Value ◽  
The Mean

Abstract Background To evaluate if procalcitonin (PCT) measurements made using the new point-of-care testing (POCT) ichroma™ are interchangeable with those made using Kryptor. Methods Serum samples (n = 117) were processed sequentially on Kryptor and ichroma™. Statistical analysis was performed using Passing-Bablok (PB) regression and the Bland-Altman (BA) test. Cohen’s kappa statistic was used to calculate the concordance at the clinically relevant cutoffs. Results PB regression did not show a significant deviation from linearity; proportional and constant differences were observed between ichroma™ and Kryptor. The 95% confidence interval (CI) of the mean bias percentage was very large, exceeding the maximum allowable total error (TE) (approximately 20%) and the clinical reference change value (about 60%). However, the concordance between methods at the clinically relevant cutoffs was strong, with the exception of the 0.25 ng/mL cutoff, which was moderate. Conclusions Our data suggest that ichroma™ is not interchangeable with Kryptor, so cannot be mixed; one must choose one instrument only and be consistent. However, while the strong concordance at the clinically relevant cutoffs allows us to consider ichroma™ a suitable option to Kryptor to support clinicians’ decision-making, nevertheless the moderate agreement at the 0.25 ng/mL cutoff recommends caution in interpreting the data around this cutoff.

Bootstrap generated confidence interval for time averaged measure

2015 ◽  
Vol 06 (03) ◽  
pp. 1550030
Author(s):  
Jinsoo Park ◽  
Haneul Lee ◽  
Yun Bae Kim
Keyword(s):  
Statistical Analysis ◽  
Confidence Interval ◽  
Output Analysis ◽  
Data Set ◽  
Autocorrelation Structure ◽  
The Central Limit Theorem ◽  
Stationary Bootstrap ◽  
Moving Block Bootstrap ◽  
The Mean ◽  
Mean Queue Length

In the simulation output analysis, there are some measures that should be calculated by time average concept such as the mean queue length. Especially, the confidence interval of those measures might be required for statistical analysis. In this situation, the traditional method that utilizes the central limit theorem (CLT) is inapplicable if the output data set has autocorrelation structure. The bootstrap is one of the most suitable methods which can reflect the autocorrelated phenomena in statistical analysis. Therefore, the confidence interval for a time averaged measure having autocorrelation structure can also be calculated by the bootstrap methods. This study introduces the method that constructs these confidence intervals applying the bootstraps. The bootstraps proposed are the threshold bootstrap (TB), the moving block bootstrap (MBB) and stationary bootstrap (SB). Finally, some numerical examples will be provided for verification.

scholarly journals ARIMA modelling of predicting COVID-19 infections

2020 ◽  
Author(s):  
W.Regis Anne ◽  
S.Carolin Jeeva
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Director General ◽  
Global Pandemic ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Short Term Prediction

AbstractThe World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.

scholarly journals Evaluation of development of Apple Inc. stock price time series

2022 ◽  
Vol 132 ◽  
pp. 01012
Author(s):  
Jakub Horák ◽  
Dominik Kaisler
Keyword(s):  
Time Series ◽  
Economic Crisis ◽  
Stock Prices ◽  
Stock Price ◽  
Common Factor ◽  
Global Market ◽  
Global Pandemic ◽  
Daily Data ◽  
Difficult Times ◽  
Analysis Of Time Series

The paper deals with the the development of a specific company’s stock price time series. The aim of the paper is to use the time series method for a detailed analysis and evaluation of the development of Apple Inc. stock prices. Daily data from 2000 to 2020, daily data from the period of the economic crisis between 2007 and 2009 and daily data from the Covid-19 pandemic period from March 2020 to the end of the same year are used. The data, from the period of 2000 - 2020 show a gradual increase in Apple’s stock prices. The most common factor leading to the increase in stock prices is the launch of a new product or service on the global market. On the contrary, the reason for the decline in stock prices is customer dissatisfaction, the excess of demand over supply, or the political situation. The analysis of time series for the period of the economic crisis points to the fact that thanks to the development, innovation and constant introduction of new products into the market, the company was not significantly affected by the crisis and neither were stock prices. Naturally, there were some fluctuations in prices, but at the end of 2009, the company even reached the highest stock prices in its history to date. The analysis of time series during the global pandemic of Covid-19 shows a steady rise in stock prices. Currently, the company sells more and more products and introduces new services that help us work, study or entertain ourselves in these difficult times, in the safety of our homes.

scholarly journals ARIMA Model in Predicting Banking Stock Market Data

2018 ◽  
Vol 12 (11) ◽  
pp. 309 ◽  
Author(s):  
Mohammad Almasarweh ◽  
S. AL Wadi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Short Term Prediction

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

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