A PROBABILISTIC MODEL FOR THE ASSESSMENT OF QUEUING TIME OF CORONAVIRUS DISEASE (COVID-19) PATIENTS USING QUEUING MODEL

The spreading of coronavirus (COVID-19) has increased exponentially throughout the world, and still, no vaccine is available for the treatment of patients. The load has increased tremendously in the hospitals where the resources are minimal. The queuing theory is applied for the multi-server system, to identify the queue time of the patients in hospitals for the identification and confirmation of disease. This paper presents a sequential queuing model for estimating the time of the detection and identification of infections in severe loading conditions. The goal is to present a simplified probabilistic model to determine the general behaviour to predict how long the treatment cycle takes to diagnose and classify people already infected. For this type of method, the law of the isolated logarithm is proved, showing that the general process of recognition is in line with the right of iterated logarithm. There are some graphical representations of the various measurement criteria. The results of the modelling showed that the patient's waiting period in the course of inquiries, detections, detecting or treating coronaviruses in the event of imbalances in the system as a whole rise following the logarithm rule.