Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning.

The coronavirus disease 2019 (COVID-19) pandemic spread by the single-stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS-CoV-2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection-related independent variables to predict early containment. COVID-19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.

Systematic literature review on novel corona virus SARS-CoV-2: a threat to human era.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the seventh-generation coronavirus family causing viral pandemic coronavirus disease (COVID-19) across globe affecting millions of people. The objectives of this study are to (1) identify the major research themes in COVID-19 literature, (2) determine the origin, symptoms and modes of transmission of COVID, (3) recommend the intervention and mitigation strategies adopted by the Governments globally against the spread of COVID-19 and the traumatization among the public? and (4) study the possible drugs/treatment plans against COVID-19. A systematic literature review and comprehensive analysis of 38 research articles on COVID-19 are conducted. An integrated Research focus parallel-ship network and keyword co-occurrence analysis are carried out to visualize the three research concepts in COVID-19 literature. Some of our observations include: (1) as SARS-CoV-2's RNA matches ~ 96% to SARS-CoV, it is assumed to be transmitted from the bats. (2) The common symptoms are high fever, dry cough, fatigue, sputum production, shortness of breath, diarrhoea etc. (3) A lockdown across 180 affected counties for more than a month with social-distancing and the precautions taken in SARS and MERS are recommended by the Governments. (4) Researchers' claim that nutrition and immunity enhancers and treatment plans such as arbidol, lopinavir/ritonavir, convalescent plasma and mesenchymal stem cells and drugs including remdesivir, hydroxychloroquine, azithromycin and favipiravir are effective against COVID-19. This complied report serves as guide to help the administrators, researchers and the medical officers to adopt recommended intervention strategies and the optimal treatment/drug against COVID-19.