scholarly journals Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yas Al-Hadeethi ◽  
Intesar F El Ramley ◽  
M. I. Sayyed
Keyword(s):  
Saudi Arabia ◽  
Prediction Models ◽  
Initial Boundary ◽  
System Of Differential Equations ◽  
Temporal Response ◽  
Effort Level ◽  
Health Authorities ◽  
Convolution Model ◽  
Temporal Profiles ◽  
Death Cases

AbstractMany published infection prediction models, such as the extended SEIR (E-SEIR) model, are used as a study and report tool to aid health authorities to manage the epidemic plans successfully. These models face many challenges, mainly the reliability of the infection rate predictions related to the initial boundary conditions, formulation complexity, lengthy computations, and the limited result scope. We attribute these challenges to the absence of a solution framework that encapsulates the interacted activities that manage: the infection growth process, the infection spread process and the health effort process. In response to these challenges, we formulated such a framework first as the basis of our new convolution prediction model (CPM). CPM links through convolution integration, three temporal profile levels: input (infected and active cases), transformational (health efforts), and output functions (recovered, quarantine, and death cases). COVID-19 data defines the input and output temporal profiles; hence it is possible to deduce the cumulative efforts temporal response (CETR) function for the health effort level. The new CETR function determines the health effort level over a period. Also, CETR plays a role in predicting the evolution of the underlying infection and active cases profiles without a system of differential equations. This work covers three countries: Saudi Arabia, France, and Canada.

scholarly journals COVID-19—Awareness and Practice of Dentists in Saudi Arabia

2021 ◽  
Vol 18 (1) ◽  
pp. 330
Author(s):  
Bassel Tarakji ◽  
Mohammad Zakaria Nassani ◽  
Faisal Mehsen Alali ◽  
Anas B. Alsalhani ◽  
Nasser Raqe Alqhtani ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Infection Control ◽  
Dental Treatment ◽  
Clinical Symptoms ◽  
Dental Clinic ◽  
Health Authorities ◽  
Environmental Surfaces ◽  
Guidelines And Recommendations ◽  
Current Guidelines ◽  
Dental Professionals

Dental professionals have a major role in the fight against the spread and transmission of COVID-19. This study aimed to evaluate awareness and practice of dentists in Saudi Arabia regarding COVID-19 and the utilization of infection control methods. A 24-item questionnaire was developed and distributed through social media to 627 dentists working in Saudi Arabia. 177 questionnaires were completed (28.2% response rate). Most dentists were aware about the transmission, incubation time and main clinical symptoms of COVID-19. Almost 83% of the respondents appreciate the risk of droplets, aerosols and airborne particles in transmission of COVID-19 in the dental clinic. Among the common practices of participants are measuring patient’s body temperature before undertaking a dental treatment (88.7%), cleaning the environmental surfaces at the dental clinic after each patient (91.5%) and restriction of dental treatment to emergency cases (82.5%). It seems that practicing dentists in Saudi Arabia are fairly aware about COVID-19. The practices of the surveyed dentists appear to be mostly consistent with the current guidelines and recommendations for infection control of COVID-19 in the dental clinic. Some drawbacks in knowledge and a number of inappropriate practices can be identified and require the attention of health authorities.

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 Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Nahla F. Omran ◽  
Sara F. Abd-el Ghany ◽  
Hager Saleh ◽  
Abdelmgeid A. Ali ◽  
Abdu Gumaei ◽  
...  
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Disease Outbreaks ◽  
Handling Time ◽  
Series Data ◽  
Learning Methods ◽  
Death Cases

The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait.

scholarly journals Assessment of knowledge, attitude and practice of diabetic people in Najran, Kingdom of Saudi Arabia

2017 ◽  
Vol 5 (5) ◽  
pp. 2150 ◽  
Author(s):  
Walid G. Babikr ◽  
Abdullah I. Aedh ◽  
Awad Mohamed Ahmed ◽  
Ahmed Abdelraheem ◽  
Mohammed Alasmary ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Educational Program ◽  
Attitude Questionnaire ◽  
Kingdom Of Saudi Arabia ◽  
Cross Sectional ◽  
Knowledge Questionnaire ◽  
Attitude And Practice ◽  
Health Authorities ◽  
Level Of Knowledge ◽  
Knowledge Attitude And Practice

Background: This cross-sectional hospital based study aimed at determining the level of knowledge, attitude and practice of diabetes among local people of Najran, Saudi Arabia.Methods: We aimed to investigate the levels of knowledge, attitude and practice among diabetic people in Najran area.Results: 10% of the participants scored >7, 28% scored >5 and 62% scored 5 and less in Knowledge questionnaire. None [0.00%] of the participants scored 7 or more out of the attitude questionnaire. 100% of the participants scored 5 and less out of 12. 100% of the participants scored >6 and 0% scored 12 or more in the practice questionnaire.Conclusions: Our study revealed that the level of knowledge, attitude and practice of diabetes in the area of Najran is very poor. We suggest that a structured educational program to be adopted by the health authorities in Saudi Arabia.

scholarly journals Coronavirus respiratory illness in Saudi Arabia

2012 ◽  
Vol 6 (10) ◽  
pp. 692-694 ◽  
Author(s):  
Mohammed N. Al-Ahdal ◽  
Ahmed Ali Al-Qahtani ◽  
Salvatore Rubino
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Severe Acute Respiratory Syndrome ◽  
Viral Infection ◽  
Molecular Analysis ◽  
Respiratory Illness ◽  
Human Infection ◽  
Gulf Region ◽  
Health Authorities ◽  
The World

Although viruses that belong to the coronavirus family are known since the 1930s, they only gained public health attention when they were discovered to be the causative agent of the severe acute respiratory syndrome (SARS) outbreak in China in 2002–2003. On 22 September 2012, the Ministry of Health (MOH) in Saudi Arabia announced the detection of what was described as a “rare pattern” of coronavirus respiratory infection in three individuals, two Saudi citizens and one person from the Gulf Region. Neither Saudi citizen survived the infection. Molecular analysis of the isolates showed that the virus belongs to the genus beta-coronavirus. It is not known if the new isolates are circulating in the population or has recently diverged. The emergence of these novel isolates that resulted in fatal human infection ascertains that health authorities all over the world must be vigilant for the possibility of new global pandemics due to novel viral infection.

scholarly journals Prediction of the Epidemic Strength of COVID-19 in the Holy Places of Saudi Arabia during the Forthcoming Hajj Season 2020

2020 ◽  
Vol 1 (3) ◽  
pp. 125-134
Author(s):  
Alheadary W ◽  
Azim MA
Keyword(s):  
Mathematical Modeling ◽  
Saudi Arabia ◽  
Time Span ◽  
Saturation Point ◽  
Expected Number ◽  
Holy Places ◽  
Precautionary Measures ◽  
Hajj Season ◽  
Death Cases

The first outbreak of the COVID-19 epidemic in Saudi Arabia was reported on March 2nd, 2020. Every year more than 2 million people come from more than 188 countries to Saudi Arabia to perform pilgrimage (Hajj in Arabic). Therefore, extrapolating the epidemic strength during the Hajj season (end of July) in the holy places has become essential. In this paper, we employ the power of mathematical modeling to infer the epidemic intensity over a 300 days’ time span in Saudi Arabia generally and the Holy places specifically. In particular, we obtain the following epidemiological insights such as the number of infections, the daily infection increase, the expected number of death cases, and the epidemic peak. Results indicate that, the epidemic peak has already been reached in both Makka and Madina. In addition, the number of infections will reach its saturation point by the first week of October 2020 as the daily increase in the number of infections will diminish. This means that, Hajj can be conducted safely only by reducing the number of pilgrims and providing suitable sets of preventive and precautionary measures.

scholarly journals A Call for Consistency in the Official Naming of the Disease Caused by Severe Acute Respiratory Syndrome Coronavirus 2 in Non-English Languages

2020 ◽  
Vol 14 (3) ◽  
pp. e25-e26
Author(s):  
Lu Dong ◽  
Zhe Li ◽  
Isaac Chun Hai Fung
Keyword(s):  
Saudi Arabia ◽  
Hong Kong ◽  
Chinese Language ◽  
Mainland China ◽  
World Health ◽  
Official Language ◽  
Native Languages ◽  
Literal Translation ◽  
Health Authorities ◽  
Health Organization

ABSTRACTWe investigated the adoption of World Health Organization (WHO) naming of COVID-19 into the respective languages among the Group of Twenty (G20) countries, and the variation of COVID-19 naming in the Chinese language across different health authorities. On May 7, 2020, we identified the websites of the national health authorities of the G20 countries to identify naming of COVID-19 in their respective languages, and the websites of the health authorities in mainland China, Hong Kong, Macau, Taiwan and Singapore and identify their Chinese name for COVID-19. Among the G20 nations, Argentina, China, Italy, Japan, Mexico, Saudi Arabia and Turkey do not use the literal translation of COVID-19 in their official language(s) to refer to COVID-19, as they retain “novel” in the naming of this disease. China is the only G20 nation that names COVID-19 a pneumonia. Among Chinese-speaking jurisdictions, Hong Kong and Singapore governments follow the WHO’s recommendation and adopt the literal translation of COVID-19 in Chinese. In contrast, mainland China, Macau, and Taiwan refer to COVID-19 as a type of pneumonia in Chinese. We urge health authorities worldwide to adopt naming in their native languages that are consistent with WHO’s naming of COVID-19.

scholarly journals A new grey quadratic polynomial model and its application in the COVID-19 in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianbo Zhang ◽  
Zeyou Jiang
Keyword(s):  
Prediction Models ◽  
Early Stage ◽  
Quadratic Polynomial ◽  
Grey Model ◽  
Grey Prediction ◽  
New Model ◽  
Polynomial Term ◽  
Death Cases ◽  
Analytical Expressions ◽  
Model Technique

AbstractThis paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19.

scholarly journals Community Pharmacists’ Opinions towards Poor Prescription Writing in Jazan, Saudi Arabia

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1077
Author(s):  
Saad Saeed Alqahtani
Keyword(s):  
Saudi Arabia ◽  
Descriptive Analysis ◽  
Community Pharmacists ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional ◽  
Significance Level ◽  
Health Authorities ◽  
Chi Square Test ◽  
Prescription Writing

Avoidance of medication errors is imperative for the safe use of medications, and community pharmacists are uniquely placed to identify and resolve the errors that may arise due to poorly handwritten prescriptions. Purpose: To explore the opinion and attitudes of community pharmacists towards poor prescription writing and their suggestions to overcome this concern. Methods: A cross-sectional, self-administered survey was conducted among the community pharmacists in the Jazan region, Saudi Arabia. Descriptive analysis and chi-square test were used at 5% p-value (p > 0.05) as the significance level. Results: The response rate for the survey was 78.66%, and 140 community pharmacists agreed to participate. Among the study subjects, the majority (73.57%) had a bachelor’s degree. Nearly three-fourths (3/4) of the pharmacists (72.29%) chose to send the patient back to the prescriber when they found difficulty in interpreting the information from an illegible prescription. As many as 80.71% of the pharmacists believed that poorly handwritten prescriptions were the cause of actual errors when dispensing medications. The most commonly encountered problem due to poorly handwritten prescriptions was the commercial name of medicine, which was reported by around two-thirds (67.86%) of the pharmacists. The use of e-prescription was suggested by 72.86% of the pharmacists as a probable solution to encounter this problem. Conclusion: Our findings highlight the belief and attitudes of community pharmacists in the region and their opinions to solve this impending problem of poor prescription writing. Continuous professional development courses can be adopted to tackle the problem. Additionally, health authorities can work on incorporating and facilitating the use of e-prescription in the community sector, which can be a boon to physicians, pharmacists, and patients. Proper and extensive training is however needed before the implementation of e-prescribing.

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