Corona Virus (or COVID19) has shown long-term effects on different human body organs, which include lung diseases, kidney malfunctions, heart dysrhythmia, changes in brain nutrient levels, psychological issues, abrupt changes in blood pressure, etc. Due to such a wide variation in the effects on different body parts, it is difficult for researchers to design models that can integrate these effects for treatment recommendations, and future disease prevention scenarios. Thus, this text reviews some of the recently proposed models that efficiently identify effects of COVID19 on different body organs. This review discusses the underlying models in terms of their clinical nuances, functional advantages, contextual limitations, and empirical future scopes. Based on this discussion, researchers will be able to identify optimal models for the identification of different diseases on individual body parts. It was observed that hybrid bio-inspired models, when combined with deep learning-based classification techniques, can efficiently identify these effects. This text also parametrically evaluates these models in terms of their accuracy, precision, classification delay, deployment cost, and scalability parameters, which will allow readers to identify optimal models for their performance specific use cases. To further contemplate this discussion, a novel COVID19 Classification Rank Metric (CCRM), which combines these parameters for comprehensive identification of optimal models is evaluated in this text. Based on this metric, researchers will be able to identify optimal models that can be deployed with high-accuracy, low delay, and highscalability, along with lower cost for clinical scenarios

CoVID19, Body, Organs, Lung, Heart, Brain, Kidney, Classification, Disease, Bioinspired, Machine, Learning, Delay, Accuracy, Precision, Scalability, Cost, Scenarios