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

Author(s):  
Dabiah Alboaneen ◽  
Bernardi Pranggono ◽  
Dhahi Alshammari ◽  
Nourah Alqahtani ◽  
Raja Alyaffer

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.

2020 ◽  
Author(s):  
Hameed K. Ebraheem ◽  
Nizar Alkhateeb ◽  
Hussein Badran ◽  
Ali Hajjiah ◽  
Ebraheem Sultan

Abstract BackgroundThe global spread of the COVID-19 pandemic has been one of the most challenging tasks the world has faced since the last pandemic outbreak of 1918. Early on countries felt the strength and persistence of the virus infections spreading with no means of estimating the dispersion rates. Officials in infected countries followed several guidelines set by the World Health Organization (WHO) to try and flatten the infection curve and maintain a low number of infectives. Nonetheless, the virus kept on spreading with impunity and all predictions of how containments or peak detections have been a fail so far. Therefore, a need for a more accurate model to predict the peaking of infections and help guide officials on what best to enact as a measure of public health safety from a multitude of choices outlined by the WHO. Earlier studies of compartmental model of Susceptible-Infected-Recovered (SIR) did not predict the peaking of a hot spots flairs of viral infections and a new model needed to provide a more realistic results to serve public officials battling the pandemic worldwideMethodsA new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The predictions of the new model are compared to real data obtained from four countries, namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produces a sensible and more accurate representation of the real time data. The reproductive number 𝑅0 is defined for the model for values of recovery time delay 𝜏2 of the infective case.ResultsIncorporating two delay periods that corresponds to the duration of the incubational and recovery periods measured for COVID-19 gives a more accurate prediction of the peak pandemic infections per geographical area. The parameter variations in the model 𝛽,𝛾,𝛼,𝜏1,𝑎𝑛𝑑 𝜏2 makeup different cases corresponding to different situations. The variations are estimated a priori based on what is being observed and collected data of an infected region to give officials better guidelines on what health policies should be enacted in the future.2 of 15ConclusionsThe empirical data provided by WHO show that the proposed new SIR model gives a better more accurate prediction of COVID-19 pandemic spreading curve. The model is shown to closely fit real time data for four countries. Simulation results are consistent with data and generated curves are well constrained. The parameters can be varied and adjusted for producing and/or reproduction of numbers within the range of each country


Author(s):  
Ali Mustafa Qamar ◽  
Rehan Ullah Khan ◽  
Suliman Alsuhibany

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.


Author(s):  
Shakir Khan

<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>


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243189
Author(s):  
Michał Wieczorek ◽  
Jakub Siłka ◽  
Dawid Połap ◽  
Marcin Woźniak ◽  
Robertas Damaševičius

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.


Author(s):  
Salem Mohammed Hassan Alharthi ◽  
Laila Mohammed Alanazi ◽  
Dalal Jumah Alturaif ◽  
Wesam Yousef Othman Alibrahim ◽  
Waleed Yahya Binammar ◽  
...  

As reported on 23rd May 2021, there are a total of 167,313,629 confirmed cases of Covid-19 all across the world with a mortality of about 3,473,851 whereas in Saudi Arabia 439,847 were registered cases of covid-19 and 7,237 deaths. According to the World Health Organization (WHO) a total of 12,244,264 people were vaccinated with Covid-19 vaccination. Covid-19 is a respiratory infectious disease. More recent researches on SARS-COV-2 suggests the entry of the virus into the host cell using the host entry factors like TMPRSS-2, TMPRSS-4 and ACE-2 in the oral tissues. The spike proteins of the SARS COV-2 attaches to the ACE-2 and TMPRSS2 of the salivary gland. Saliva provides the lubrication of the oral cavity, initiation of digestion and provides immunity in host. A complete research of all the articles was done using databases like: SCOPUS, PUBMED, EMBASE and WEB OF SCIENCE. In case of SARS CoV-2, the salivary glands act as reservoir for the virus. Intake of these viruses present in infectious saliva droplets found in the air would lead to the transmission of infection to an individual. Saliva is more efficient when compared to the blood as it doesn't clot. A reduced secretion of saliva is observed in patients post the covid 19 disease.


2020 ◽  
Author(s):  
Ashish Kalraiya ◽  
Ben Walker ◽  
Shiron Rajendran ◽  
Sayinthen Vivekanantham ◽  
Danny Sharpe ◽  
...  

BACKGROUND Covid-19 is exacerbating pre-existing pressures on healthcare systems. Frontline staff are relying more than usual on effective logistics and infrastructure to deliver patient care, for example provision of PPE, stock, facilities and equipment. Staff must adapt their ways of working in response to new challenges. Traditional communication channels within hospitals are often inefficient and not digitised, preventing healthcare organisations from adequately supporting staff and providing efficient solutions to problems. OBJECTIVE This study deployed the MediShout mobile phone application (app) to capture real-time data, on problems with logistics and infrastructure occurring in hospitals during the Covid-19 pandemic. The main objectives were to determine whether; healthcare staff would use the app, reporting led to immediate improvements, and data-collection could drive long-term transformational change and improve responses to future pandemics. METHODS The app was used by staff to report issues with logistics and infrastructure across two hospital emergency departments (EDs) at Imperial College Healthcare Trust, UK. These reports were acted upon by senior physicians and nurses, operational managers and service helpdesks. Data was collected from the start of the first peak of Covid-19 in the UK, between March and April 2020. Data from each report were retrospectively analysed across multiple categories, including problem description and time of submission. To gauge the impact of each issue on clinical care, reports were scored against an impact scoring tool using a modified version of the World Health Organisation’s ‘quality of care’ definition. RESULTS During this study, 94 reports were submitted. Reporting peaks were observed at times corresponding to clinical handovers. Peaks were also observed when changes had occurred to existing processes within the EDs. Impact analysis highlighted that every report sent had ‘impact’ or ‘significant impact’ on various aspects of care, including efficiency, patient safety and timely treatment. CONCLUSIONS The MediShout app captured valuable real-time data from frontline staff during the peak of Covid-19. Staff readily adopted the digital technology as it provided a more efficient way to resolve issues. This enabled hospitals to better allocate scarce resources, such as PPE, to those who needed it most. This study suggests listening to the voice of frontline staff during times of crisis allows more effective responses. Capturing data during pandemics is critical for healthcare organisations to learn lessons and maintain control. During this study, it was established that most problems occurred due to changes in practice, such as dividing EDs into Covid-19 and non-Covid-19 zones, rather than increased caseload. Logistical and infrastructure issues were categorised as being “material” (stock, equipment, medicines, or estates and facilities) or “workflow” (task-management, new ways of working, infection control and communication) in nature. This provides healthcare organisations with a methodical tool for risk-assessing and coordinating future pandemic responses. CLINICALTRIAL n/a


2019 ◽  
Vol 16 (9) ◽  
pp. 3783-3791
Author(s):  
Hala Alrumaih ◽  
Mohammed Alawairdhi

A great wave of diabetes is sweeping the world and it does not exclude continent, country or society and this has led international organizations such as the World Health Organization to sound the alarm after the high rates of deaths from complications of this epidemic. In this regard, the University Diabetes Center (UDC) was established in the Kingdom of Saudi Arabia to provide medical care for people with diabetes among other medical issues. As part of UDC, an ontology center has been constructed to explain the domain of the UDC using Protégé. The discussion herein will center on how emerging diabetes centers can benefit from the UDC experiment in diabetes treatment.


2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zablon K. Njiru ◽  
Cecilia K. Mbae ◽  
Gitonga N. Mburugu

The World Health Organization has targeted Human African Trypanosomiasis (HAT) for elimination by 2020 with zero incidence by 2030. To achieve and sustain this goal, accurate and easy-to-deploy diagnostic tests for Gambian trypanosomiasis which accounts for over 98% of reported cases will play a crucial role. Most needed will be tools for surveillance of pathogen in vectors (xenomonitoring) since population screening tests are readily available. The development of new tests is expensive and takes a long time while incremental improvement of existing technologies that have potential for xenomonitoring may offer a shorter pathway to tools for HAT surveillance. We have investigated the effect of including a second set of reaction accelerating primers (stem primers) to the standardT. brucei gambienseLAMP test format. The new test format was analyzed with and without outer primers. Amplification was carried out using Rotorgene 6000 and the portable ESE Quant amplification unit capable of real-time data output. The stem LAMP formats indicated shorter time to results (~8 min), were 10–100-fold more sensitive, and indicated higher diagnostic sensitivity and accuracy compared to the standard LAMP test. It was possible to confirm the predicted product using ESE melt curves demonstrating the potential of combining LAMP and real-time technologies as possible tool for HAT molecular xenomonitoring.


Author(s):  
Yatharth Khansali

COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.


2021 ◽  
Vol 6 (6) ◽  
pp. 12-16
Author(s):  
Nader Alber ◽  
Mohamed Dabour

This paper aims at testing the significance of each of Covid-19 pandemic and social distancing on banks’ asset quality, using a sample of 30 banks representing 10 countries according to GMM technique. Data have been collected from the World Health Organization during 2020. The research covers 10 countries (Egypt; Saudi Arabia; Indonesia; Germany; France; Russia; India; Mexico; South Korea and Nigeria) where 3 banks have been investigated from each country. Results indicate that banks’ asset quality measured by Average change of nonperforming loans ratio seems to be sensitive to Covid-19 spread, measured by Average cases of COVID-19. Besides, findings support the effect of social distancing, measured by each of average staying at residential and average social distancing for retail-recreation. It’s important to pinpoint that results do not support the effect of each of average deaths of Covid-19 and average social distancing for workplaces, residential, grocery pharmacy, parks and transit stations.


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