Author: Furati, K. M.; Sarumi, I. O.; Khaliq, A. Q. M.
Title: Memory-Dependent Model for the Dynamics of COVID-19 Pandemic Cord-id: f9kfsm3y Document date: 2020_6_28
ID: f9kfsm3y
Snippet: 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 action and public percept
Document: 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 action and public perception are incorporated as part of the time-varying transmission rate. The model simulation is performed using the two-step generalized exponential time-differencing method and tested for data from Wuhan, China. The mean-square errors demonstrate the merit of the fractional-order model and provide a good estimate of the optimal order.
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