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Depicts a flow chart of the proposed SEIQRD model.
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In the present study, the model will be divided into six compartments [see Fig 1].
Time series solution.. Time series of all classes correspondence to Table 2 taking phi = 0.98 of system (3.3).
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Fig 2(A)-2(E) depict the nature of all cases corresponding to phi = 0.98.
Dynamical behavior.. Dynamic of all classes over time for various values of phi = 0.8, 0.85, 0.90, 0.95.
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Fig 3(A)-3(F) depict all individuals' behavior over time for various fractional orders phi. Fig 3(A) depicts that the number of susceptible individuals increases when phi changes from 0.8 to 0.95. An increase value of phi leads to decrease in the exposed rate in the exposed population in Fig 3(B). Fig 3(C) that number of infected individuals increases when phi changes from 0.8 to 0.95. Fig 3(D) depicts that the number of quarantined individuals increases with time when phi decreases. Fig 3(E). Fig 3(F) depicts that the number of death individuals increase with time when phi increases. If the order of derivatives for the same set of parametric values is changed, the results will be changed (Fig 3).
Variation of RCovid 19 under alpha.
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Fig 4 depicts that the values of RCovid 19 decrease when alpha increase. COVID-19 disease (Figs 4 and 5). Currently, research on a vaccine to avert the COVID-19 pandemic is showing promising results, with Pfizer claiming that their vaccine has a 95% effectiveness rate.
Dynamics of I(t) under alpha and phi.
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The impact of alpha and phi on the Infected individuals (I(t)) is depicted in Fig 5(A) and 5(b). COVID-19 disease (Figs 4 and 5). Currently, research on a vaccine to avert the COVID-19 pandemic is showing promising results, with Pfizer claiming that their vaccine has a 95% effectiveness rate.
Graph of the infected class of the proposed model (3.3) and real infected data [Table 4].
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