scholarly journals Machine Learning Model for Predicting Number of COVID19 Cases in Countries with Low Number of Tests

2021 ◽  
Author(s):  
Samy Hashim ◽  
Sally Farooq ◽  
Eleni Syriopoulos ◽  
Kai de la Lande Cremer ◽  
Alexander Vogt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care Systems ◽  
European Countries ◽  
Multilinear Regression ◽  
Us States ◽  
Poor Countries ◽  
The Neural Network ◽  
Machine Learning Model ◽  
Care Systems ◽  
Multi Linear Regression

The COVID-19 pandemic has presented a series of new challenges to governments and health care systems. Testing is one important method for monitoring and therefore controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries not every country is able to employ widespread testing. Here we developed machine learning models for predicting the number of COVID-19 cases in a country based on multilinear regression and neural networks models. The models are trained on data from US states and tested against the reported infections in the European countries. The model is based on four features: Number of tests Population Percentage Urban Population and Gini index. The population and number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi linear regression and 0.91 for the neural network model. The model predicts that the actual number of infections in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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.

Machine Learning-Based Smart Health-care Systems

2021 ◽  
pp. 195-208
Author(s):  
Anil Kumar Swain ◽  
Bunil Kumar Balabantaray ◽  
Jitendra Kumar Rout
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Systems ◽  
Care Systems ◽  
Smart Health

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.

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