Idris Nasir Abdullahi

TL;DR: It was showed that SARS-CoV-2 has persistently undergone significant mutations in various parts of its non-structural proteins (NSPs), including NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp), which could be a potential target for vaccine development.

TL;DR: This study examined various forms of mathematical models that are relevant for the containment, risk analysis, and features of COVID‐19 and placed greater emphasis on the extension of the Susceptible–Infectious–Recovered (SIR) models for policy relevance in the time of CO VID‐19.

TL;DR: An in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective and a new perspective for solving the challenges identified as direction for future research are presented.

TL;DR: The study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals in smart cities across the world and proposed framework has the potential for helping national healthcare systems in curtailing the CO VID-19 pandemic in smart Cities.

TL;DR: This present study critically reviewed available and latest research progress on the genetics and ecology of SARS-CoV-2, as well as the influence of climatic factors on the spread of CO VID-19, and discussed how these concepts could be harnessed for COVID-19 control and further scientific advancements in resolving the pandemic.