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.

COVID‐19 pandemic disease spread by the SARS‐COV‐2 single‐strand structure RNA virus, belongs to the 7 th generation of the coronavirus family. Following an unusual replication mechanism, it’s extreme ease of transmissivity has put many counties under lockdown. With 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 the COVID‐19 virus and identifies countries that have shown early signs of containment until 26 th March 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% to 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.

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Summary Using a thermogravimetric analyzer, the thermal stability of pure eutectic phase change material (PEPCM) (LiNO3 + NaCl) and composite eutectic PCM (CEPCM) mixture (ie, PCM containing 9% expanded graphite [EG]) was examined. PEPCM and CEPCM degradation kinetics were studied using model free kinetics methods. The activation energy of both PCM samples was evaluated using the Kissinger‐Akahira‐Sunose (KAS), Flynn‐Wall‐Ozawa (FWO), Starink, Friedman and Vyazovkin kinetic models. The calculated activation energies for Vyazovkin, Frideman, Ozawa, KAS and Starink techniques for PEPCM were 80.62‐149.2, 108.1‐180.18, 83.74‐136.17, 73.55‐127.02 and 74.64‐149.9 kJ/mol, respectively. Likewise, the activation energy of CEPCM vary between 59.4‐161.41, 83.97‐188.69, 57.1‐147.32, 54.19‐137.43 and 55‐160.65 kJ/mol. Hybrid neural networks such as ANN‐PSO and ANFIS were used to model the degradation of PCM samples. The type of PCM, the heating rate, and the temperature were applied as input parameters, while the sample's mass loss was utilized as an output parameter. The created hybrid models are capable of effectively predicting experimental TGA data.

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