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Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

Computers, Materials & Continua 2021年第3期66卷 2265-2282页

作者： Fahad Ahmad Saleh N.Almuayqil Mamoona Humayun Shahid Naseem Wasim Ahmad Khan kashaf junaid Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia Division of Computer Science&Information Technology University of EducationLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesJouf UniversitySakakaAljouf72341Saudi Arabia

COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.However,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet defined.In this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and Pakistan.Significant environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables.The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases.However,age and the human development index had a negative influence on the cases.The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases.During training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of 0.91.Likewise,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an area.However,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given

关键词： Public health sustainable development artificial intelligence SARSCoV-2 shallow single-layer perceptron neural network binary classification gaussian process regression

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Potential Inhibitory Effect of Vitamins Against COVID-19

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Computers, Materials & Continua 2021年第1期66卷 707-714页

作者： kashaf junaid Sumera Qasim Humaira Yasmeen Hasan Ejaz Abdullah Alsrhani Muhammad Ikram Ullah Fahad Ahmad Abdul Rehman Department of Clinical Laboratory Sciences College of Applied Medical SciencesJouf UniversitySakakaAl JoufSaudi Arabia College of Pharmacy Jouf UniversitySakakaAl JoufSaudi Arabia Department of Microbiology and Molecular Genetics The Women’s UniversityMultanPakistan Department of Computer Sciences Kinnaird College for WomenLahorePakistan Department of Microbiology and Molecular Genetics University of the PunjabLahorePakistan

Coronavirus disease 2019(COVID-19)is a current pandemic that has affected more than 195 countries worldwide.In this severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pandemic,when treatment strategies are not yet clear and vaccines are not available,vitamins are an excellent choice to protect against this viral infection.The rationale behind this study was to examine the inhibitory effect of vitamins B,C,and D against the main protease of SARSCoV-2 and angiotensin-converting enzyme 2(ACE2),which have critical rolesin the immune system.Molecular docking,performed by using MOE-Dock of the Chemical Computing Group,was used to understand the mechanism.The vitamins all docked within the active sites of the Mpro(PDB ID:6LU7)and ACE2 receptor proteins(PDB ID:6VW1).Vitamins B and C delivered maximum energy scores against both targets,while vitamin D displayed a binding energy score of−7.9532 kcal/mol for M^(pro) and−7.9297 for ACE2.The efficiency of all three vitamins is higher than the binding energy score of chloroquine(−6.889 kcal/mol),which is now under clinical trials.The use of vitamins is beneficial,being immune system restorative,and they also act as anti-COVID agents.Although the potential beneficial effects of vitamin B and C are revealed through docking studies,further clinical trials are required for the validation of these results.

关键词： SARS-CoV-2 vitamins docking antivirals chemical computing

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