ABSTRACT
Due to its complex nature, Coronavirus has perplexed many researchers worldwide and endangered the lives of people across the globe forcing them to take social distancing and close geographical borders in order to control the spread of the disease. This study, therefore, seeks to develop a new mathematical model which delineates the dynamics of COVID-19. This latter takes into account the common characteristics that typify not only this disease as well as the infectious undetected cases, but also the ones under medical treatment. Thus we maintain that the more infected people are figured out and treated, the better we prevent the spread of Covid-19. In this paper, we study a stochastic epidemic model of Covid-19 with quarantine alone or in combination with treatment measures. Firstly, we establish the existence and uniqueness of the global positive solution to the proposed model. Secondly, we establish conditions for extinction and persistence in mean. Finally, to put on validation of the theorical results we present some numerical simulations.
ABSTRACT
This paper studied a stochastic epidemic model of the spread of the novel coronavirus (COVID-19). Severe factors impacting the disease transmission are presented by white noise and compensated Poisson noise with possibly infinite characteristic measure. Large time estimates are established based on Kunita’s inequality rather than Burkholder-Davis-Gundy inequality for continuous diffusions. The effect of stochasticity is taken into account in the formulation of sufficient conditions for the extinction of COVID-19 and its persistence. Our results prove that environmental fluctuations can be privileged in controlling the pandemic behavior. Based on real parameter values, numerical results are presented to illustrate obtained results concerning the extinction and the persistence in mean of the disease. [ABSTRACT FROM AUTHOR] Copyright of Stochastic Analysis & Applications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
In a Covid-19 susceptible-infected-recovered-dead model with time-varying rates of transmission, recovery, and death, the parameters are constant in small time intervals. A posteriori parameters result from the Euler-Maruyama approximation for stochastic differential equations and from Bayes’ theorem. Parameter estimates and 10-day predictions are performed based on Moroccan and Italian Covid-19 data. Mean absolute errors and mean square errors indicate that predictions are of good quality. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Population Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
In this paper, we analyze a stochastic coronavirus (COVID-19) epidemic model which is perturbed by both white noise and telegraph noise incorporating general incidence rate. Firstly, we investigate the existence and uniqueness of a global positive solution. Then, we establish the stochastic threshold for the extinction and the persistence of the disease. The data from Indian states, are used to confirm the results established along this paper.