Mohammad Zuber¹, John Valerian Corda¹, Milad Ahmadi², B. Satish Shenoy¹, Irfan Anjum Badruddin^3,*, Ali E. Anqi³, Kamarul Arifin Ahmad⁴, S. M. Abdul Khader⁵, Leslie Lewis⁶, Mohammad Anas Khan⁷, Sarfaraz Kamangar³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1185-1199, 2020, DOI:10.32604/cmes.2020.015015 - 15 December 2020

Abstract The novel coronavirus responsible for COVID-19 has spread to several countries within a considerably short period. The virus gets deposited in the human nasal cavity and moves to the lungs that might be fatal. As per safety guidelines by the World Health Organization (WHO), social distancing has emerged as one of the major factors to avoid the spread of infection. However, different guidelines are being followed across the countries with regards to what should be the safe distance. Thus, the current work is an attempt to understand the virus deposition pattern in the realistic human… More >

M. H. A. Biswas^1,*, M. A. Islam¹, S. Akter², S. Mandal², M. S. Khatun¹, S. A. Samad¹, A. K. Paul¹, M. R. Khatun¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1033-1060, 2020, DOI:10.32604/cmes.2020.012792 - 15 December 2020

Abstract COVID-19 is one of the most highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has already led the entire world to health and economic crisis. It has invaded the whole universe all most every way. The present study demonstrates with a nine mutually exclusive compartmental model on transmission dynamics of this pandemic disease (COVID-19), with special focus on the transmissibility of symptomatic and asymptomatic infection from susceptible individuals. Herein, the compartmental model has been investigated with mathematical analysis and computer simulations in order to understand… More >

Avaneesh Singh^*, Manish Kumar Bajpai

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 991-1031, 2020, DOI:10.32604/cmes.2020.012503 - 15 December 2020

Abstract We have proposed a new mathematical method, the SEIHCRD model, which has an excellent potential to predict the incidence of COVID-19 diseases. Our proposed SEIHCRD model is an extension of the SEIR model. Three-compartments have added death, hospitalized, and critical, which improves the basic understanding of disease spread and results. We have studied COVID-19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. After estimating model parameters based on available clinical data, the model will propagate and forecast dynamic… More >

Avaneesh Singh^*, Saroj Kumar Chandra, Manish Kumar Bajpai

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 967-990, 2020, DOI:10.32604/cmes.2020.011601 - 15 December 2020

Abstract COVID-19 disease has emerged as one of the life threatening threat to the society. A novel beta coronavirus causes it. It began as unidentified pneumonia of unknown etiology in Wuhan City, Hubei province in China emerged in December 2019. No vaccine has been produced till now. Mathematical models are used to study the impact of different measures used to decrease pandemic. Mathematical models have been designed to estimate the numbers of spreaders in different scenarios in the present manuscript. In the present manuscript, three different mathematical models have been proposed with different scenarios, such as More >

Majid Monajjemi^1,*, Rahim Esmkhani², Fatemeh Mollaamin¹, Sara Shahriari³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 907-926, 2020, DOI:10.32604/cmes.2020.012846 - 15 December 2020

Abstract Current understanding about how the virus that causes COVID-19 spreads is largely based on what is known about similar coronaviruses. Some of the Natural products are suitable drugs against SARS-CoV-2 main protease. For recognizing a strong inhibitor, we have accomplished docking studies on the major virus protease with 4 natural product species as anti COVID-19 (SARS-CoV-2), namely “Vidarabine”, “Cytarabine”, “Gemcitabine” and “Matrine” which have been extracted from Gillan’s leaves plants. These are known as Chuchaq, Trshvash, Cote-Couto and Khlvash in Iran. Among these four studied compounds, Cytarabine appears as a suitable compound with high effectiveness… More >

Panagiotis G. Asteris^1,*, Maria G. Douvika¹, Chrysoula A. Karamani¹, Athanasia D. Skentou¹, Katerina Chlichlia², Liborio Cavaleri³, Tryfon Daras⁴, Danial J. Armaghani⁵, Theoklis E. Zaoutis⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 815-828, 2020, DOI:10.32604/cmes.2020.013280 - 12 October 2020

Abstract The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each More >

Xing Deng^1,2, Haijian Shao^1,2,*, Liang Shi³, Xia Wang^4,5, Tongling Xie⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920 - 12 October 2020

Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are provided to compare with More >

Flavia M. Wehrle^1,2,3, Markus A. Landolt^3,4,5, Beatrice Latal^1,3, Sarah Rometsch⁶, Matthias Greutmann^7,*

Congenital Heart Disease, Vol.15, No.5, pp. 301-308, 2020, DOI:10.32604/CHD.2020.013078 - 23 September 2020

Abstract Background: The risk for a severe disease course in case of infection with SARS-CoV-2 in young adults with congenital heart disease is largely unknown, potentially leading to uncertainty and anxiety among affected patients. This study aims to investigate health-related concerns, health-related quality of life and psychological adjustment in patients with congenital heart disease compared to healthy peers during the COVID-19 pandemic. Methods: One-hundred patients with congenital heart disease and 50 controls (M = 29.7, SD = 3.8 years) were recruited. They completed an online survey including the assessment of health-related concerns regarding COVID-19, the 12-item Short Form… More >

Sanaa Al-Marzouki¹, Farrukh Jamal², Christophe Chesneau^3,*, Mohammed Elgarhy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 437-458, 2020, DOI:10.32604/cmes.2020.011521 - 18 September 2020

Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with More >

Tahir Abbas Khan¹, Sagheer Abbas¹, Allah Ditta², Muhammad Adnan Khan^{3, *}, Hani Alquhayz⁴, Areej Fatima³, Muhammad Farhan Khan⁵

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2591-2605, 2020, DOI:10.32604/cmc.2020.011892 - 16 September 2020

Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer… More >