Open AccessA fractional-order model of COVID-19 with a strong Allee effect considering the fear effect spread by social networks to the community and the existence of the silent spreaders during the pandemic stage
Author(s) -
Ali Yousef
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022560
Subject(s) - allee effect , covid-19 , transmission (telecommunications) , order (exchange) , social psychology , stability (learning theory) , epidemic model , psychology , sociology , computer science , disease , medicine , economics , telecommunications , demography , population , finance , pathology , machine learning , infectious disease (medical specialty)
End of 2019, the world has experienced a virus known as COVID-19, which almost changed everything in our daily and social lives. Every day, experts in medicine, economics, finance, and many different fields inform the community through the media or social networks about the virus, the effects, and changes in our "new life". The virus is highly transmittable and shows different mutated forms. Therefore, to describe this attractive event, many mathematical models and studies have been applied to work on the infections and transmission risks of COVID-19. However, another discussion in the community besides the virus's transmission effect isthe fear of getting infected and dying from the corona. People who have never heard about this virus before 2019 face uncertain and different information about the virus from the media, social networks, and health organizations. This paper proposes a mathematical model of FDEs with a strong Allee effect about the novel coronavirus COVID-19, including the community's fear effect spread through the media and different networks. The primary target is to emphasize the psychological pressure during and after the lockdown. Using the Routh-Hurwitz Criteria, we analyze the local stability of two critical points: disease-free and co-existing. In the end, we use MATLAB 2019 to implement simulation studies that support the theoretical findings.