The article is regarding infectious disease, which has been affecting people all around the world since December 2019. Write-up gives a brief introduction of the Coronavirus from its emergence to the final solution of its exit. It gives us a glimpse of the biological structure of the virus, precautions to be taken in care to reduce the spread of the disease. The description shows us the consequences of the virus-like the mortality rate and the life expectancy rate. The article also presents a few vaccines development projects as a whole to fight COVID-19. It also describes the efficacy of vaccination and the post-vaccination symptoms. In the end, the write-up presents real-time neural network-based applications to fight COVID-19 and a few machine learning and deep learning techniques to diagnose a COVID-19 infected person. The final part of the article concludes with an artificial neural network and statistical neural network-based approaches to identify the rate of infection risk in a country.

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Objectives: To find an efficient security routing model based on trust adaptability by considering various transmission parameters that influence the node's behavior. Methods: Enhanced Collaborative Trust Based Approach (ECTBA) was applied to isolate the malicious nodes from routing by computing their enhanced collaborative trust value based on the node's behavior using transmission parameters. Parameters that influence the node's behavior like the number of data packets and control packets forwarded, dropped, or misrouted by the node are quantified to compute direct trust value and neighbor reputation. Node's Enhanced Collaborative trust value was generated by the combination of direct and neighbor observations. Findings: The proposed strategy is compared with several cases like the Direct Trust Based Approach (DTBA), where routing involves trustworthy nodes categorized based on only direct trust, existing methods like Belief-dependent trust evolution method(BETM), Novel extended trust-dependent method (NETM) where routing is done with nodes that are categorized as trustworthy depending on direct and indirect observations and simple AODV routing performed with all the possible random nodes without any trust detection. The performance parameters of the proposed ECTBA exhibit a success rate of 10.2% in false positives detection (FPD),network throughput of 438.12 Kbps,and packet delivery ratio (PDR) of 92.3%.This method proves to be a better method when compared with the traditional trust-based security methods(BETM and NETM) in terms of efficiency. Novelty: This research suggested a novel and fine-tuned method for quantifying a node's trustworthiness and for secure routing that coupled the direct and indirect observations into enhanced collaborative trust https://www.indjst.org/

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