Mingyi Qian¹, Rodney Goodyear², Guangrong Jiang^3,*, Zhihong Ren³

International Journal of Mental Health Promotion, Vol.22, No.3, pp. 105-107, 2020, DOI:10.32604/IJMHP.2020.013483

Abstract This article has no abstract. More >

Peipeng Yu¹, Zhihua Xia^{1, *}, Jianwei Fei¹, Sunil Kumar Jha^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391

Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms… More >

Ibrahim Arpaci¹, Shadi Alshehabi², Mostafa Al-Emran^{3, *}, Mahmoud Khasawneh⁴, Ibrahim Mahariq⁴, Thabet Abdeljawad^{5, 6, 7}, Aboul Ella Hassanien^{8, 9}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489

Abstract People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency… More >

Jodi L. Feinberg¹, Frank Cecchin^1,2, Arianna Gonzalez¹, Emily Johnson², Dan G. Halpern^1,*

Congenital Heart Disease, Vol.15, No.3, pp. 141-146, 2020, DOI:10.32604/CHD.2020.012039

Abstract Adults with congenital heart disease (ACHD) are likely at increased risk for complications of COVID-19. ACHD centers should prepare to deliver routine cardiac care and support for patients with COVID-19 safely at home, as the number of COVID-19 infections worldwide continues to increase. This brief report aims to share the strategies we have used in our ACHD program to manage and treat our patients during this global health crisis at one of the initial epicenters of the pandemic in New York City, and offer suggestions for preparation for ACHD clinicians. More >

Muhammad Naveed¹, Muhammad Rafiq², Ali Raza³, Nauman Ahmed⁴, Ilyas Khan^{5, *}, Kottakkaran Sooppy Nisar⁶, Atif Hassan Soori¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1401-1414, 2020, DOI:10.32604/cmc.2020.011314

Abstract In this manuscript, the mathematical analysis of corona virus model with time delay effect is studied. Mathematical modelling of infectious diseases has substantial role in the different disciplines such as biological, engineering, physical, social, behavioural problems and many more. Most of infectious diseases are dreadful such as HIV/AIDS, Hepatitis and 2019-nCov. Unfortunately, due to the non-availability of vaccine for 2019- nCov around the world, the delay factors like, social distancing, quarantine, travel restrictions, holidays extension, hospitalization and isolation are used as key tools to control the pandemic of 2019-nCov. We have analysed the reproduction number R_nCov of delayed model. Two… More >