ABSTRACT
The Covid-19 disease caused by the novel Corona Virus (SARS-2) spread like a wildfire and scientists across the whole world have been trying to find a cure for the disease and as such resorted to all methods available. The tool of artificial intelligence (AI) and data science has proven very useful in this regard for rapid drug invention and development. In this paper, tending along the same line, four different deep neural networks (DNNs) based models (bi-directional long short-term memory (LSTM) with attention, constrained graph variational autoencoders (CGVAE), edge memory neural network (EENN) and connectivity map (CMAP) based DNN have been proposed for usage in drug Invention of highly effective lead molecules for the disease COVID-19. The models have been evaluated and performed well with the highest performance given by the bi-directional LSTM model with validity of 98.7%, uniqueness of 99.8% and originality of 97.4%.
ABSTRACT
The present pregression of the highly contagious novel coronavirus [COVID-19] has been testing the healthcare system globally pressing the medical staff everywhere. Present and future healthcare workers’ updated knowledge, proper attitude towards the pandemic and adequate preventive practices are of paramount importance for the combat effectiveness of the healthcare burden.This study assessed the knowledge, practice and attitudes regarding COVID-19 among the nursing students of a tertiary care center in Eastern India. KAP scores were compared with their socio-demographic variables. Inter-relation between knowledge, attitude and practice was also assessed.Out of the 131 students participated in the study the KAP parameters were not significantly different based on the socio-demographic factors. Though knowledge and attitude parameters were positively correlated among them, it was found that practice was negatively correlated to both knowledge and attitude. This finding can be attributed to them being non-exposed and inexperienced in the regular healthcare activities as well to the fact that Indian population was to some extent unprepared to cope up with this type of epidemic for a long time. Training on the updated knowledge along with exposure to simulated environment with scheduled supervision to reflect the behavior of the students is of great importance so that in extreme situation, the trainee students can also come handy into utilization if needed.
ABSTRACT
Objective: The study aimed to assess knowledge, attitude, practices, and perception (KAP) toward COVID-19 among the population of eight North Eastern (NE) states of India. Methods: A cross-sectional study from June 30 to July 13, 2020 was carried out through a self-reported, structured questionnaire that was circulated online to participants of age group of 18 years or above. Convenient sampling was used to recruit respondents for the study. Results: The study received responses from 8309 participants. Key findings revealed that most respondents had good knowledge of preventive measures and common symptoms of COVID-19. The majority of the respondents showed a good attitude and adopted preventive practices. The mean score of knowledge was 7.137, attitude was 16.132, practice was 9.379, and perception was 13.583. The scores of four KAP categories significantly differed across most of the demographic variables (p<0.001). The majority of people took homoeopathic medicine as prophylaxis for immune booster. Conclusion: The study highlights that the focus on behavioral change communication in all the NE states could be strengthened, especially in rural areas. Advocacy based on the comprehensive list of symptoms for COVID-19 may also be bolstered. There is scope for strategically promoting knowledge, immunity boosting, and self-care practices suggested in the AYUSH systems of medicine.
ABSTRACT
Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Covid-19 number of rising cases and death cases in India, USA, France, and UK, considering the progressive trends of China and South Korea. In this paper, three cases are considered to analyze the outbreak of Covid-19 pandemic viz., (i) forecasting as per the present trend of rising cases of different countries (ii) forecasting of one week following up with the improvement trends as per China and South Korea, and (iii) forecasting if followed up the progressive trends as per China and South Korea before a week. The results have shown that ANN can efficiently forecast the future cases of COVID 19 outbreak of any country. The study shows that the confirmed cases of India, USA, France and UK could be about 50,000 to 1,60,000, 12,00,000 to 17,00,000, 1,40,000 to 1,50,000 and 2,40,000 to 2,50,000 respectively and may take about 2 to 10 months based on progressive trends of China and South Korea. Similarly, the death toll for these countries just before controlling could be about 1600 to 4000 for India, 1,35,000 to 1,00,000 for USA, 40,000 to 55,000 for France, 35,000 to 47,000 for UK during the same period of study.