BACKGROUND
The article presents an application of a model of queues in series queueing system under overloading conditions to estimate the time of detection and identification of coronavirus (COVID-19) infections.
OBJECTIVE
The objective is to present a simplified probabilistic model for assessing the general tendency to estimate the period of time needed to detect and identify already infected citizens before the treatment process really begins.
METHODS
The law of the iterated logarithm is proved for such a system, which shows that the general identification process corresponds to the law of iterated logarithm.
RESULTS
Some numerical examples of a different number of evaluation parameters are provided.
CONCLUSIONS
The modelling results showed that the sojourn time of the patient in the process of coronavirus investigation/detection/identification and treatment in the case of imbalance in the system as a whole increase in accordance with the law of the iterated logarithm. Even if the process of the treatment phases is well arranged and generally balanced, in case of the rate of investigation/detection/identification is lower than the rate of infection, the total number of already infected and unidentified citizens will increase in accordance with the law of the iterated logarithm.
The law of iterated logarithm for subordinated
Gaussian sequences: uniform Wasserstein bounds
