Presentation Information
[SS26-04]Mathematical modeling and optimal intervention strategies of COVID-19 transmission dynamics with a case study of India
*Subhas Khajanchi1 (1. Presidency University, 86/1 College Street, Kolkata - 700073, India (India))
Keywords:
Model calibration,Optimal control,Pontryagin maximum principle,Sensitivity analysis
In the absence of effective therapeutics or drugs and with an unknown epidemiological life cycle, predictive mathematical models can aid in understanding both coronavirus disease control and management. In this study, we investigate a model with six stages of infection taken into consideration: susceptible (S), asymptomatic infected (A), symptomatic infected (I ), quarantine (Q), isolation (J ), and recovered (R), collectively termed as SAIQJR. The qualitative behavior of the model and the stability of biologically realistic equilibrium points are investigated in terms of the basic reproduction number. We performed a sensitivity analysis of the basic reproduction number and obtained that the disease transmission rate has an impact in mitigating the spread of diseases. Moreover, considering the non-pharmaceutical and pharmaceutical intervention strategies as control functions, an optimal control problem is implemented to mitigate the disease fatality. To reduce the number of infected individuals and to minimize the cost of the controls, an objective function has been constructed and solved with the aid of Pontryagin’s Maximum Principle. The implementation of an optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time. Extensive numerical simulations show that the implementation of an intervention strategy has an impact on controlling the transmission dynamics of the COVID-19 epidemic.