scholarly journals Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

2021 ◽  
Vol 18 (12) ◽  
pp. 6680
Author(s):  
David Meintrup ◽  
Martina Nowak-Machen ◽  
Stefan Borgmann
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Health Care Systems ◽  
Case Fatality ◽  
Multiple Regression Model ◽  
European Countries ◽  
Three Phase ◽  
Strongly Connected ◽  
Care Systems ◽  
Second Wave

(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model (R2=85.0%), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

FORECASTING CONSUMER PRICE INDEX (CPI) USING TIME SERIES MODELS AND MULTI REGRESSION MODELS (ALBANIA CASE STUDY)

2018 ◽  
Author(s):  
Eralda Gjika Dhamo ◽  
Llukan Puka ◽  
Oriana Zaçaj
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Consumer Price Index ◽  
Multiple Regression Model ◽  
Time Series Models ◽  
Financial Indicators ◽  
Consumer Prices ◽  
Approach Time

In this work we analyse the CPI index as the official index to measure inflation in Albania, Harmo-nized Indices of Consumer Prices (HICPs) as the bases for comparative measurement of inflation in European countries and other financial indicators that may affect CPI. This study is an attempt to model CPI based on combination of multiple regression model with time series forecasting models. In the first approach, time series models were used directly on the CPI time series index to obtain the forecast. In the second approach, the time series models (SARIMA, ETS) were used to model and simulate forecast for each subcomponent with significant correlation to CPI and then use the multiple regression model to obtain CPI forecast. The projection of this indicator is important for understand-ing the country's economic and social development. This study helps researchers in the field of time series modeling, economic analysis and investments.

scholarly journals Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3996 ◽  
Author(s):  
Marwen Elkamel ◽  
Lily Schleider ◽  
Eduardo L. Pasiliao ◽  
Ali Diabat ◽  
Qipeng P. Zheng
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Multiple Regression Model ◽  
Electricity Demand ◽  
Degree Days ◽  
Population Number ◽  
Demand Prediction

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

Health Care System Reforms in Western European Countries: The Relevance of Health Care Organization

1996 ◽  
Vol 26 (2) ◽  
pp. 239-251 ◽  
Author(s):  
Javier Elola
Keyword(s):  
Health Care ◽  
Social Security ◽  
Health Care Systems ◽  
European Countries ◽  
Care Organization ◽  
Political Commitment ◽  
Social Security Systems ◽  
Security Systems ◽  
Care Systems ◽  
Western European

The problems within the health care systems of western European countries, and their current attempts at reform, can be analyzed by comparing those countries having national health service (NHS) systems with those having social security systems. There are important differences in the structures, processes, and outcomes of these two types of health care systems, and thus in the problems they face. Greater cost control, equity, and, possibly, efficiency in improving the population's health are the advantages of NHS systems; however, public satisfaction is lower than in social security systems. Attempts to overcome this trade-off between the outcomes of the two types of health care systems are the main goal of the reforms. To achieve this goal, there has been a trend toward convergence of NHS and social security systems. For the NHS systems of Latin-rim countries, however, which have received less political commitment and public support than those elsewhere, this means a return to the former social security systems—a trend that may reintroduce the problems associated with these types of systems but without any evidence that public satisfaction will increase.

scholarly journals Hospitalization for ischemic stroke was affected more in independent cases than in dependent cases during the COVID-19 pandemic: An interrupted time series analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261587
Author(s):  
Hiroyuki Nagano ◽  
Jung-ho Shin ◽  
Tetsuji Morishita ◽  
Daisuke Takada ◽  
Susumu Kunisawa ◽  
...  
Keyword(s):  
Time Series ◽  
Ischemic Stroke ◽  
Hospital Admissions ◽  
Health Care Systems ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
State Of Emergency ◽  
Its Analysis ◽  
Care Systems ◽  
The Impact

Background The pandemic of the coronavirus disease 2019 (COVID-19) has affected health care systems globally. The aim of our study is to assess the impact of the COVID-19 pandemic on the number of hospital admissions for ischemic stroke by severity in Japan. Methods We analysed administrative (Diagnosis Procedure Combination—DPC) data for cases of inpatients aged 18 years and older who were diagnosed with ischemic stroke and admitted during the period April 1 2018 to June 27 2020. Levels of change of the weekly number of inpatient cases with ischemic stroke diagnosis after the declaration of state of emergency were assessed using interrupted time-series (ITS) analysis. The numbers of patients with various characteristics and treatment approaches were compared. We also performed an ITS analysis for each group (“independent” or “dependent”) divided based on components of activities of daily living (ADL) and level of consciousness at hospital admission. Results A total of 170,294 cases in 567 hospitals were included. The ITS analysis showed a significant decrease in the weekly number of ischemic stroke cases hospitalized (estimated decrease: −156 cases; 95% confidence interval (CI): −209 to −104), which corresponds to −10.4% (95% CI: −13.6 to −7.1). The proportion of decline in the independent group (−21.3%; 95% CI: −26.0 to −16.2) was larger than that in the dependent group (−8.6%; 95% CI: −11.7 to −5.4). Conclusions Our results show a marked reduction in hospital admissions due to ischemic stroke after the declaration of the state of emergency for the COVID-19 pandemic. The independent cases were affected more in proportion than dependent cases.

scholarly journals Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

2020 ◽  
Author(s):  
Prashant Verma ◽  
Mukti Khetan ◽  
Shikha Dwivedi ◽  
Shweta Dixit
Keyword(s):  
Time Series ◽  
Health Care Systems ◽  
Medical Personnel ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Time Series Methodology ◽  
Near Future

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

scholarly journals DEALING WITH OUTLIERS AND INFLUENTIAL POINTS WHILE FITTING REGRESSION

2017 ◽  
Vol 22 (1) ◽  
pp. 61-65
Author(s):  
Chuda Prasad Dhakal
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Time Series Data ◽  
Multiple Regression Model ◽  
International Rice Research Institute ◽  
Series Data ◽  
Influential Points ◽  
Production Forecasting ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

Dealing with outliers and influential points while fitting regression is recognizing them, identifying the reasons to their existence in the process and employing the best alternatives to lessen their effect to the fitted regression model. In this paper, before considering elimination of outliers and the influential points while fitting a regression, as they contain important information, issues why unusual observations (possible outliers) appear in the process and how to analyze them to detect if they were real outliers, have been discussed thoroughly. And, when detected as outliers and influential points, to investigate and eliminate their effect in the fitted model, analytic procedures; leverage value, studentized residuals and cook's distance were carefully employed to optimize a multiple regression model for rice production forecasting in Nepal. This model was fitted with 35 years (1961-1995) time series data, collected from Ministry of Agriculture and Cooperatives, Food and Agriculture Organization Statistics Database, International Rice Research Institute and Department of Hydrology and Metrology which to its end was consisted of the three predictors, price at harvest, rural population and area harvested.Journal of Institute of Science and TechnologyVolume 22, Issue 1, July 2017, Page: 61-65

scholarly journals Multiple Regression Model Fitted for Rice Production Forecasting in Nepal: A Case of Time Series Data

2018 ◽  
Vol 2 ◽  
pp. 89-98
Author(s):  
Chuda Prasad Dhakal
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Rural Population ◽  
Time Series Data ◽  
Multiple Regression Model ◽  
Rice Production ◽  
Series Data ◽  
Agricultural Labor ◽  
Production Forecasting

Background: Fitting a multiple regression model is always challenging and the level of difficulty varies according to the purpose for which it is fitted. Two major difficulties that arise while fitting a multiple regression model for forecasting are selecting 'potential predictors' from numerous possible variables to influence on the forecast variable and investigating the most appropriate model with a subset of the potential predictors.Objective: Purpose of this paper is to demonstrate a procedure adopted while fitting multiple regression model (with an attempt to optimize) for rice production forecasting in Nepal.Materials and Methods: This study has used fifty years (1961-2010) of time series data. A list of twenty-one predictors thought to impact on rice production was scanned based upon past literature, expert's hunches, availability of the data and the researcher's insight which left eleven possible predictors. Further, these possible predictors were subjected to family of automated stepwise methods which left five ‘potential predictors’ namely harvested area, rural population, farm harvest price, male agricultural labor force and, female agricultural labor force. Afterwards, best subset regression was performed in Minitab Version 16 which finally left three 'appropriate predictors' that best fit the model namely harvested area, rural population and farm harvest price.Results: The model fit was significant with p < .001. Also, all the three predictors were found highly significant with p < 0.001. The model was parsimonious which explained 93% variation in rice production with 54% overlapping predictive work done. Forecast error was less than 5%.Conclusion: Multiple regression model can be used in rice production forecasting in the country for the enhanced ease and efficiency.Nepalese Journal of Statistics, Vol. 2, 89-98

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