scholarly journals Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

2021 ◽  
Vol 17 (08) ◽  
pp. 154
Author(s):  
Shakir Khan
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Prediction Models ◽  
Preventive Measures ◽  
Future Trend ◽  
Control Measures ◽  
World Health ◽  
Seir Model ◽  
The World ◽  
Health Organization

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

scholarly journals Real-time neural network based predictor for cov19 virus spread

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243189
Author(s):  
Michał Wieczorek ◽  
Jakub Siłka ◽  
Dawid Połap ◽  
Marcin Woźniak ◽  
Robertas Damaševičius
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Prediction Models ◽  
Numerical Data ◽  
Real Data ◽  
Future Trend ◽  
World Health ◽  
Number Of Patients ◽  
The World ◽  
Health Organization

Since the epidemic outbreak in early months of 2020 the spread of COVID-19 has grown rapidly in most countries and regions across the World. Because of that, SARS-CoV-2 was declared as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, by The World Health Organization (WHO). That’s why many scientists are working on new methods to reduce further growth of new cases and, by intelligent patients allocation, reduce number of patients per doctor, what can lead to more successful treatments. However to properly manage the COVID-19 spread there is a need for real-time prediction models which can reliably support various decisions both at national and international level. The problem in developing such system is the lack of general knowledge how the virus spreads and what would be the number of cases each day. Therefore prediction model must be able to conclude the situation from past data in the way that results will show a future trend and will possibly closely relate to the real numbers. In our opinion Artificial Intelligence gives a possibility to do it. In this article we present a model which can work as a part of an online system as a real-time predictor to help in estimation of COVID-19 spread. This prediction model is developed using Artificial Neural Networks (ANN) to estimate the future situation by the use of geo-location and numerical data from past 2 weeks. The results of our model are confirmed by comparing them with real data and, during our research the model was correctly predicting the trend and very closely matching the numbers of new cases in each day.

scholarly journals Repercussions of the COVID-19 Health Crisis In the Peruvian Informal Sector

2021 ◽  
pp. 1-9
Author(s):  
Vilma Andia-Choquepuma ◽  
Daniza Juana Leon-Escobedo ◽  
Himer Avila-George ◽  
orge Sánchez-Garcés ◽  
Ruth Elizabeth Villafuerte-Alcántara ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Preventive Measures ◽  
Control Measures ◽  
World Health ◽  
Health Crisis ◽  
Entry Points ◽  
The World ◽  
Prevention And Control Measures ◽  
Health Organization ◽  
And Control

After the World Health Organization declared the outbreak of Coronavirus (COVID-19) a pandemic, the Peruvian government took preventive measures to counteract the spread of the virus by issuing Supreme Decree No. 008-2020-SA. This decree contains prevention and control measures aimed at ports, airports, land entry points, educational centers, transportation and workplaces.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

scholarly journals Coronavirus Disease 2019 (COVID-19): Emerging and Future Challenges for Dental and Oral Medicine

2020 ◽  
Vol 99 (5) ◽  
pp. 481-487 ◽  
Author(s):  
L. Meng ◽  
F. Hua ◽  
Z. Bian
Keyword(s):  
Public Health ◽  
Infection Control ◽  
Control Measures ◽  
World Health ◽  
Infection Control Measures ◽  
Dental Practitioners ◽  
The World ◽  
Essential Knowledge ◽  
Novel Coronavirus ◽  
Health Organization

The epidemic of coronavirus disease 2019 (COVID-19), originating in Wuhan, China, has become a major public health challenge for not only China but also countries around the world. The World Health Organization announced that the outbreaks of the novel coronavirus have constituted a public health emergency of international concern. As of February 26, 2020, COVID-19 has been recognized in 34 countries, with a total of 80,239 laboratory-confirmed cases and 2,700 deaths. Infection control measures are necessary to prevent the virus from further spreading and to help control the epidemic situation. Due to the characteristics of dental settings, the risk of cross infection can be high between patients and dental practitioners. For dental practices and hospitals in areas that are (potentially) affected with COVID-19, strict and effective infection control protocols are urgently needed. This article, based on our experience and relevant guidelines and research, introduces essential knowledge about COVID-19 and nosocomial infection in dental settings and provides recommended management protocols for dental practitioners and students in (potentially) affected areas.

scholarly journals A Bibliometric Analysis of Coronavirus Research in Gulf Cooperation Council Countries

2021 ◽  
Vol 17 (13) ◽  
pp. 99-119
Author(s):  
Ali Mustafa Qamar ◽  
Rehan Ullah Khan ◽  
Suliman Alsuhibany
Keyword(s):  
Saudi Arabia ◽  
New England ◽  
Bibliometric Analysis ◽  
United Arab Emirates ◽  
Middle Eastern ◽  
Gulf Cooperation Council ◽  
World Health ◽  
King Saud University ◽  
The World ◽  
Health Organization

COVID-19 was declared a pandemic by World Health Organization in March 2020. Since then, it has attracted the enormous attention of researchers from around the world. The world has gone through previous instances of corona-viruses such as Severe Acute Respiratory Syndrome and Middle Eastern Respiratory Syndrome. Nevertheless, none was of these were of this serious nature as COVID-19. In this research, we carry out a bibliometric analysis of coronavirus research using the Scopus database. However, we restricted ourselves to the Gulf Cooperation Council countries, comprising Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. The analysis was performed using Biblioshiny software. We analyzed 4288 articles written by 24226 researchers from 1994 till 2021, published in 1429 sources. The number of authors per publication is 5.65. A bulk of the research (more than 68%) appeared in the form of articles. More than 43% of the publications appeared in 2020 and more than 44% in 2021. Saudi Arabia appears the most-cited country, followed by Qatar. Journal of Infection and Public Health published the most number of papers, whereas New England Journal of Medicine is the most-cited one. Memish, Z.A. wrote the maximum number of papers. The top source, according to the H-index, is the Journal of Virology. Furthermore, the two most prolific universities are King Saud University and King Abdulaziz University, both from Saudi Arabia. The research uncovered deep learning as a niche theme used in recent publications. The research landscape continues to alter as the pandemic keeps on evolving.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

Machine Learning-Based Approach for Predictive Analytics in Healthcare

2022 ◽  
pp. 182-206
Author(s):  
Sandeep Kumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Mortality Rate ◽  
Chronic Diseases ◽  
Domain Knowledge ◽  
Predictive Analytics ◽  
Future Trend ◽  
Medical Data ◽  
World Health ◽  
Premature Deaths ◽  
Health Organization

In this internet era, due to digitization in every application, a huge amount of data is produced digitally from the healthcare sectors. As per the World Health Organization (WHO), the mortality rate due to the various chronic diseases is increasing each day. Every year these diseases are taking lives of at least 50 million people globally, which includes even premature deaths. These days, machine learning (ML)-based predictive analytics are turning out as effective tools in the healthcare sectors. These techniques can extract meaningful insights from the medical data to analyze the future trend. By predicting the risk of diseases at the preliminary stage, the mortality rate can be reduced, and at the same time, the expensive healthcare cost can be eliminated. The chapter aims to briefly provide the domain knowledge on chronic diseases, the biological correlation between theses disease, and more importantly, to explain the application of ML algorithm-based predictive analytics in the healthcare sectors for the early prediction of chronic diseases.

scholarly journals Safeguarding the Maintenance Hemodialysis Patient Population during the Coronavirus Disease 19 Pandemic

2020 ◽  
Vol 49 (3) ◽  
pp. 259-264 ◽  
Author(s):  
Bjorn Meijers ◽  
Piergiorgio Messa ◽  
Claudio Ronco
Keyword(s):  
Intensive Care ◽  
Hemodialysis Patient ◽  
Preventive Measures ◽  
Geographic Location ◽  
World Health ◽  
Maintenance Hemodialysis ◽  
Social Environments ◽  
The World ◽  
Health Organization ◽  
Social Separation

The World Health Organization has recognized the pandemic nature of the coronavirus disease 19 (COVID-19) outbreak. A large proportion of positive patients require hospitalization, while 5–6% of them may need more aggressive therapies in intensive care. Most governments have recommended social separation and severe measures of prevention of further spreading of the epidemic. Because hemodialysis (HD) patients need to access hospital and dialysis center facilities 3 times a week, this category of patients requires special attention. In this editorial, we tried to summarize the experience of our centers that hopefully may contribute to help other centers and colleagues that are facing the coming wave of the epidemic. Special algorithms for COVID-19 spreading in the dialysis population, recommendations for isolation and preventive measures in positive HD patients, and finally directions to manage logistics and personnel are reported. These recommendations should be considered neither universal nor absolute. Instead, they require local adjustments based on geographic location, cultural and social environments, and level of available resources.

scholarly journals Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

2020 ◽  
Vol 17 (12) ◽  
pp. 4568 ◽  
Author(s):  
Dabiah Alboaneen ◽  
Bernardi Pranggono ◽  
Dhahi Alshammari ◽  
Nourah Alqahtani ◽  
Raja Alyaffer
Keyword(s):  
Saudi Arabia ◽  
Real Time ◽  
World Health ◽  
Logistic Growth ◽  
Final Phase ◽  
Time Data ◽  
Local Prediction ◽  
The World ◽  
Real Time Data ◽  
Health Organization

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

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