Fractional‐order backstepping strategy for fractional‐order model of COVID‐19 outbreak

Abstract

The coronavirus disease (COVID‐19) pandemic has impacted many nations around the world. Recently, new variant of this virus has been identified that have a much higher rate of transmission. Although vaccine production and distribution are currently underway, non‐pharmacological interventions are still being implemented as an important and fundamental strategy to control the spread of the virus in countries around the world. To realize and forecast the transmission dynamics of this disease, mathematical models can be very effective. Various mathematical modeling methods have been proposed to investigate the transmission patterns of this new infection. In this paper, we utilized the fractional‐order dynamics of COVID‐19. The goal is to control the prevalence of the disease using non‐pharmacological interventions. In this paper, a novel fractional‐order backstepping sliding mode control (FOBSMC) is proposed for non‐pharmacological decisions. Recently, new variant of this virus have been identified that have a much higher rate of transmission, so finally the effectiveness of the proposed controller in the presence of new variant of COVID‐19 is investigated.

Stability analysis of fractional epidemic model for two infected classes incorporating hospitalization impact

“…The dynamics of this type of transmission are described using a fractional order derivative by [27][28][29][30][31][32][33], fractional-order backstepping strategy by Veisi and Delavari [34], generalized fractional-order by Xu et al [35], hybrid stochastic fractional order by Sweilam et al [36], El-Borai and El-Nadi [37], Caputo-Fabrizio (CF) and Atangana-Baleanu models non-singular fractional derivatives by Panwar et al [38], Mohammad and Trounev [39], Peter et al [40], Verma and Kumar [41], Sintunavarat and Turab [42], Kolebaje et al [43], optimized fractional order by Alshomrani et al [44], Caputo-Fabrizio derivative by Baleanu et al [45], fractional Chebyshev polynomials by Hadid et al [46], fractal-fractional order by Algehyne and Ibrahim [47], fractional order with fuzzy theory by Verma and Kumar [48], fractional order derivative with Krasnoselskiiʼs fixed point theorem by Verma et al [49], the multifractional characteristics with time-dependent memory indexes by Jahanshahi et al [50], fractional derivative with Riesz wavelets simulation by Mohammad et al [51]. Padmapriya and Kaliyappan [52], Dong et al [53] discussed the model of fuzzy fractional differential systems for the epidemic.…”

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

“…But this consideration may not be practical, as the individual behavior of the population cannot be directly controlled, but realistically its the responsibility of governments to design suitable policies in order the mitigate and control the pandemic. In other works on Lypanuov functions [40] , backstepping control [41] and sliding mode control [42] have considered strategies like pharmacological, isolation, and social distancing based strategies, but these works are largely limited to simulations. In this regard, we believe that the designing of optimal strategies for governmental intervention to be real-world data-driven.…”