Probability Analysis of a Stochastic Non-Autonomous SIQRC Model with Inference
Mathematics
; 11(8):1806, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2298655
ABSTRACT
When an individual with confirmed or suspected COVID-19 is quarantined or isolated, the virus can linger for up to an hour in the air. We developed a mathematical model for COVID-19 by adding the point where a person becomes infectious and begins to show symptoms of COVID-19 after being exposed to an infected environment or the surrounding air. It was proven that the proposed stochastic COVID-19 model is biologically well-justifiable by showing the existence, uniqueness, and positivity of the solution. We also explored the model for a unique global solution and derived the necessary conditions for the persistence and extinction of the COVID-19 epidemic. For the persistence of the disease, we observed that Rs0>1, and it was noticed that, for Rs<1, the COVID-19 infection will tend to eliminate itself from the population. Supplementary graphs representing the solutions of the model were produced to justify the obtained results based on the analysis. This study has the potential to establish a strong theoretical basis for the understanding of infectious diseases that re-emerge frequently. Our work was also intended to provide general techniques for developing the Lyapunov functions that will help the readers explore the stationary distribution of stochastic models having perturbations of the nonlinear type in particular.
Mathematics; stochastic model; air; environmental noise; persistence; numerical simulation; Infectious diseases; Humidity; Random variables; Mathematical models; Severe acute respiratory syndrome coronavirus 2; Signs and symptoms; Perturbation; Epidemics; Quarantine; Stochastic models; Viral diseases; Graphical representations; Liapunov functions; Coronaviruses; Ventilation; COVID-19; Disease transmission
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Mathematics
Year:
2023
Document Type:
Article
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