Fractional model for the spread of COVID-19 subject to government intervention and public perception

• Published in: Applied Mathematical Modelling Vol. 95; pp. 89 - 105
• Main Authors: Furati, K.M., Sarumi, I.O., Khaliq, A.Q.M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.07.2021; Elsevier BV
• Subjects: Algorithms; Coronaviruses; COVID-19; Disease control; Disease transmission; Epidemiology models; Fractional models; Fractional SEIR models; Generalized exponential time differencing (GETD); Mitigation measures; Perception; Perceptions; Simulation; COVID-19; Fractional SEIR models; Generalized exponential time differencing (GETD); Mitigation measures; Epidemiology models; Fractional models
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2021.02.006

•A fractional-order model for simulating the spread of COVID-19 is proposed.•Impact of governmental action and public perception.•Time-varying transmission rate based on the actions and perceptions.•Simulations using generalized exponential time-differencing (GETD).•Improved data fitting using fractional-orders rather than integer order. COVID-19 pandemic has impacted people all across the world. As a result, there has been a collective effort to monitor, predict, and control the spread of this disease. Among this effort is the development of mathematical models that could capture accurately the available data and simulate closely the futuristic scenarios. In this paper, a fractional-order memory-dependent model for simulating the spread of COVID-19 is proposed. In this model, the impact of governmental interventions and public perception are incorporated as part of the nonlinear time-varying transmission rate. In addition, an algorithm for approximating the optimal values of the fractional order and strength of governmental interventions is provided. This approach makes our model suitable for capturing the given data set and consequently reliable for future predictions. The model simulation is performed using the two-step generalized exponential time-differencing method and tested for data from Mainland China, Italy, Saudi Arabia and Brazil. The simulation results demonstrate that the fractional order model calibrates to the data better than its integer order counterpart. This observation is further endorsed by the calculated error metrics.