scholarly journals Modeling and Simulation: A study on predicting the outbreak of COVID-19 in Saudi Arabia

2021 ◽  
Author(s):  
Ahmed Msmali ◽  
Zico Mutum ◽  
Idir Mechai ◽  
Abdullah Ahmadini
Keyword(s):  
Saudi Arabia ◽  
Logistic Growth ◽  
Special Focus ◽  
The Novel ◽  
Final Phase ◽  
The World ◽  
Logistic Growth Model ◽  
Novel Coronavirus ◽  
Epidemiological Trends

AbstractThe novel coronavirus (Covid-19) infection has resulted in an ongoing pandemic affecting health system and economy of more than 200 countries around the world. Mathematical models are used to predict the biological and epidemiological trends of an epidemic and develop methods for controlling it. In this work, we use mathematical model perspective to study the role of behavior change in slowing the spread of the COVID-19 disease in Saudi Arabia. The real-time updated data from 1st May 2020 to 8th January 2021 is collected from Saudi Ministry of Health, aiming to provide dynamic behaviors of the pandemic in Saudi Arabia. During this period, it has infected 297,205 people, resulting in 6124 deaths with the mortality rate 2.06 %. There is weak positive relationship between the spread of the infection and mortality (R2 =0.412). We use Susceptible-Exposed-Infection-Recovered (SEIR) mode, the logistic growth model and with special focus on the exposed, infection and recovery individuals to simulate the final phase of the outbreak. The results indicate that social distancing, good hygienic conditions, and travel limitation are the crucial measures to prevent further spreading of the epidemic.

scholarly journals Modeling and Simulation: A Study on Predicting the Outbreak of COVID-19 in Saudi Arabia

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ahmed Msmali ◽  
Mutum Zico ◽  
Idir Mechai ◽  
Abdullah Ahmadini
Keyword(s):  
Saudi Arabia ◽  
Positive Relationship ◽  
Logistic Growth ◽  
Special Focus ◽  
The Novel ◽  
Final Phase ◽  
Logistic Growth Model ◽  
Novel Coronavirus ◽  
Spread Of Infection

The novel coronavirus disease (COVID-19) has resulted in an ongoing pandemic affecting the health system and economy of more than 200 countries worldwide. Mathematical models are used to predict the biological and epidemiological tendencies of an epidemic and to develop methods for controlling it. In this work, we use a mathematical model perspective to study the role of behavior change in slowing the spread of COVID-19 in Saudi Arabia. The real-time updated data from March 2, 2020, to January 8, 2021, were collected from the Saudi Ministry of Health, aiming to provide dynamic behaviors of the epidemic in Saudi Arabia. During this period, 363,692 people were infected, resulting in 6293 deaths, with a mortality rate of 1.73%. There was a weak positive relationship between the spread of infection and mortality R 2 = 0.459 . We used the susceptible-exposed-infection-recovered (SEIR) model, a logistic growth model, with a special focus on the exposed, infected, and recovered individuals to simulate the final phase of the outbreak. The results indicate that social distancing, hygienic conditions, and travel limitations are crucial measures to prevent further spread of the epidemic.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

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.

scholarly journals Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling

2021 ◽  
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Short Term ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractSince the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.

scholarly journals Role of Flavonoids against COVID-19

2021 ◽  
Vol 9 (1) ◽  
pp. 47-55
Author(s):  
Ambreen Fatima ◽  
Yasir H. Siddique
Keyword(s):  
Treatment Strategy ◽  
Protective Role ◽  
The Novel ◽  
Loss Of Life ◽  
The World ◽  
Health Promoting ◽  
Ancient Times ◽  
Novel Coronavirus ◽  
Old Drugs

The novel coronavirus disease (COVID-19) has entered a threatening stage all over the world. Many lives have been lost, and many more are in need of treatment. The mild symptoms may include fever and dry cough, but in severe cases, it could lead to pneumonia and ultimately death in some instances. Though medical scientists all over the globe are working hard to develop a treatment for this disease, yet no definite cure has been found. To date, the treatment strategy is based on adopting strategies to break the transmission of the virus and repurposing of the old drugs to prevent the loss of life. Among the various potent candidates, flavonoids may play a protective role in these times. Studies have already proven various health-promoting properties of flavonoids in earlier viral diseases, like SARS and MERS. Since ancient times, been plants have used to treat a number of human diseases. Different phytoproducts have been previously described to inhibit the replication of numerous viruses. Despite the positive reports for plant-based medications, no successful clinical trials on phytoproducts as anti-COVID agents have been conducted to date. This review highlights the efficacy of flavonoids as a treatment strategy either alone or in combination with other drugs.

scholarly journals Monitoring and Epidemiological Trends of Coronavirus Disease (COVID-19) Around The World

2020 ◽  
Author(s):  
ARNAB SAHA ◽  
Komal Gupta ◽  
Manti Patil ◽  
Urvashi
Keyword(s):  
World Health ◽  
The Novel ◽  
Health Production ◽  
The Social ◽  
The World ◽  
Big Data Technologies ◽  
Long Time ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Epidemiological Trends

COVID-19 has struck fear into populaces all through the world and shocked the worldwide restorative community, with the World Health Organization (WHO) pronouncing it a widespread as it were approximately three months after the flare-up of the infection. A new different virus (primarily called ‘Novel Coronavirus 2019-nCoV’) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China in December 2019 and rapidly spread to other parts of China and other countries around the world. The outbreak of the novel coronavirus disease (COVID-19) has caused more than 850,000 people infected and approx. 40000 of deaths in more than 190 countries up to March 2020, extremely affecting economic and social development. Presently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects. This paper describes the usage of practical GIS and mapping dashboards and applications for monitoring the coronavirus epidemic and related activities as they spread around the world. At the facts level, in the generation of massive data, information no longer come on the whole from the authorities but are gathered from greater diverse enterprises. As of now and for a long time in the future, the improvement of GIS should be fortified to create a data-driven framework for fast information securing, which implies that GIS ought to be utilized to fortify the social operation parameterization of models and methods, particularly when giving back for social administration.

scholarly journals Flow in the Time of COVID-19: Findings from China

2020 ◽  
Author(s):  
Kate Sweeny ◽  
Kyla Rankin ◽  
Xiaorong Cheng ◽  
Lulu Hou ◽  
Fangfang Long ◽  
...  
Keyword(s):  
Online Survey ◽  
Well Being ◽  
Coping Resources ◽  
The Novel ◽  
The World ◽  
Novel Coronavirus

In February 2020, the Novel Coronavirus (COVID-19) was raging in Wuhan, China and quickly spreading to the rest of the world. This period was fraught with uncertainty for those in the affected areas. The present investigation examined the role of two potential coping resources during this stressful period of uncertainty: flow and mindfulness. Participants in Wuhan and other major cities affected by COVID-19 (N = 5115) completed an online survey assessing experiences of flow, mindfulness, and well-being. Longer quarantine was associated with poorer well-being; flow and mindfulness predicted better well-being on some measures. However, flow—but not mindfulness—moderated the link between quarantine length and well-being, such that people who experienced high levels flow showed little or no association between quarantine length and poorer well-being. These findings suggest that engaging in flow-inducing activities may be a particularly effective way to protect against the deleterious effects of a period of quarantine.

Impact of Corona virus Outbreak on Climate and air Quality

2021 ◽  
Vol 16 ◽  
Author(s):  
Nikita Jatai ◽  
Tanu Sharma ◽  
Karan Veer
Keyword(s):  
Public Health ◽  
Acute Respiratory Distress Syndrome ◽  
Distress Syndrome ◽  
Public Health Emergency ◽  
The Novel ◽  
Symptomatic Case ◽  
The People ◽  
The World ◽  
Novel Coronavirus

All over the world, there is a new target of public health emergency looming the world along with an appearance and distribution of the novel coronavirus disease (2019-nCoV) also known as Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2). This Virus initially generated in bats and then after transferred to a human being over unknown animal playing the role of mediator in Wuhan, China in December 2019. This virus is passed by breathing or in contact with an infected person’s droplets. The Incubation period is between 2 to 14 days for COVID-19, that is the time between exposure of the virus (person becoming infected) and symptom on that person, is on an average of 5-6 days, however it can goes up to 14 days. Throughout this period, which can be also known as “pre-symptomatic” period, some of the infected patients or persons can be contagious. That is why, transferal from a pre-symptomatic case can happen before the symptoms onset. Where there is few number of case studies and reports, pre-symptomatic transferal has been documented via contact with someone who is diagnosed with virus and increase investigation of that particular clusters of total confirmed cases. The main problem is that the symptoms are just like the regular flu that are cough, fever, sore throat, fatigue and breathlessness. This virus is moderate or mild in most of the people, but in elder ones, it may proceed to pneumonia, multi-organ dysfunction and Acute Respiratory Distress Syndrome (ARDS). Coronavirus has significant consequences on the Health system, mainly on cardiovascular diseases and on the environment.

scholarly journals Characterizing two outbreak waves of COVID-19 in Spain using phenomenological epidemic modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253004
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Total Duration ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

Since the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and construct a short-term forecast of 60 days in the near time horizon, in relation to the expected total duration of the pandemic.

scholarly journals Bayesian Reasoning and Artificial Intelligence against COVID-19

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Michael Gr. Voskoglou ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Artificial Intelligence ◽  
World Economy ◽  
Potential Role ◽  
Reproduction Number ◽  
Case Based Reasoning ◽  
Bayesian Reasoning ◽  
The Novel ◽  
The World ◽  
Novel Coronavirus

The article focuses on the potential role of Probability Theory and Artificial Intelligence in the battle against the pandemic of COVID-19, which, starting from China on December 2019, has created a chaos in the world economy and the lives of people, causing hundreds of thousands of deaths until now. After discussing the importance of the reproduction number Ro of the viruses, the Bayesian Probabilities are used for measuring the creditability of the diagnostic tests for the novel coronavirus. Artificial Intelligence designs are also described which are used as tools against COVID-19 and a Case-Based Reasoning expert system is proposed for the COVID-19 diagnosis.

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