Exploration of the novel corona virus transition graphs with petrinet modeling

Abstract

Corona virus (CoV) is a group of viruses with non-bifurcated, single-stranded, and positive-sense RNA genomes. Apart from infecting several economically significant vertebrates (such as pigs and chickens), it is reported in the recent literature that six main types of CoVs infect the human hosts and cause lung infections. In animals, CoVs cause several diseases, including pneumonia, gastrointestinal tract, and central nervous system diseases. In humans, the CoVs work as respiratory tract diseases, and the new CoVs can penetrate the barrier between other species and humans and can cause a high mortality rate. In the course of this study, a novel approach to networking, based on the density-dependent differential equations, is adopted for the precise explanation of the propagation of the virus and the effect of quarantine on it. An infectious disease model with a time delay is suggested based on the conventional infectious disease model. To describe the viral infection period and treatment time, the time differential is used. Using the epidemic data released in real-time, the minimum error is obtained firstly through the inversion of the numerical simulation parameter; then we simulate the development pattern of the epidemic according to the dynamics system; finally, the effectiveness of quarantine steps is compared and analyzed. With the help of a discrete model, the transformations are documented in detail that is difficult to evaluate numerically. The provided numerical results are in close agreement with the experimental findings. The modeling of Petri nets (PNs) used has proven to be a successful method. The current research strategy can help the public to gain awareness of the disease spread, which is highly desired.