Biology and Behavior of Severe Acute Respiratory Syndrome Coronavirus Contagion with Emphasis on Treatment Strategies, Risk Assessment, and Resilience

The emergence of novel pathogens is a well-known epidemiological risk; however, the unexpected emergence of a truly novel coronavirus-mediated pandemic due to SARS-CoV-2 underscored the significance of understanding this contagion. The pandemic, due to novel coronavirus, termed COVID-19, caused unprecedented social, economic, and educational disruptions on a scale never seen before. In addition to social protocols, safe, effective, and affordable vaccines were developed within months, the cornerstone of the mitigation of this pandemic. We present an overview of the evolution of the pandemic from a historical perspective and describe its biology and behavior, especially the immunological aspects of the disease. We further provide an overview of therapeutics, treatment, and vaccine development to mitigate SARS-CoV-2. It is critical to understand the transmission mechanism of the disease to control and mitigate its progression. We describe cohort studies to identify secondary and tertiary syndromes. The transmission characteristics help its diagnosis and detection. During the pandemic, a lot of emphasis was placed on personal protection equipment. It is now concluded that the virus particles are spread by aerosol dispersion. While the recommended distance may not have been sufficient, the use of personal protective equipment and social distancing was helpful in close-quarters environments. Such protocols, in conjunction with safe and effective vaccines and personal hygiene, are among the safe practices. While we learn from our experience, this review provides a holistic overview of the pandemic and encapsulates the event in a historical context. In doing so, we hope to understand the SARS-CoV-2 virus and take sufficient precautionary measures to mitigate consequences during any subsequent similar pandemics. In addition to a wide-spectrum automated analytics system introduced by the authors earlier, we propose the use of artificial intelligence in conjunction with data analytics to minimize the risk of speculatively diagnosing agents incorrectly by employing a novel concept of cloud-based presumptive diagnosis.