Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19

Zhang, Yong; Yu, Xiangnan; Sun, Hong; Tick, Geoffrey R.; Wei, Wei; Jin, Bin

doi:10.1016/j.chaos.2020.109959

Cited by 53 publications

(40 citation statements)

References 33 publications

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“…Most of these models are applicable to describe disease spreading with a long incubation period, such as COVID-19. Based on data-driven, a modified SEIR model was proposed to analyze and predict COVID-19 spread [23]. Considering the effect of quarantine, B. K. Mishra et.al have proposed three quarantine models to analyze COVID-19 spreading [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Zhang

Liu

2021

PLoS ONE

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The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China. It caused a worldwide outbreak and a major threat to global health. It is important to design prediction and control strategies to restrain its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading of COVID-19. First, considering the effect of control measures, such as government investment, media publicity, medical treatment, and law enforcement in epidemic spreading. Then, the infection rates are optimized by genetic algorithm (GA) and a modified susceptible-infected-quarantined-recovered (SIQR) epidemic spreading model is proposed. In addition, the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model, which can obtain the optimal predictive model and control measures. Simulation results show that the proposed hybrid intelligence algorithm has good predictive ability. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19.

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Section: Introductionmentioning

confidence: 99%

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Zhang

Liu

2021

PLoS ONE

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“…However, the epidemic outbreaks regularly follow a non-Markovian process [7] , [10] , [11] , [12] . It means that the past states of the dynamical systems contribute to its current conditions, which can be claimed as “memory,” contrary to a Markovian process where the previous states do not affect the evolution [13] , [14] , [15] , [16] . Therefore, a mathematical description with the capabilities of capturing the contribution of past states is essential.…”

Section: Introductionmentioning

confidence: 99%

A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19

Jahanshahi

Muñoz‐Pacheco

Bekiros

et al. 2021

Chaos, Solitons & Fractals

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“…Since the outbreak of COVID-19 pandemic, a variety of fractional-order models for the spread of this disease are formulated [11] , [12] , [13] , [14] These models differ mainly in the considered compartments, mitigation effects, multi-fractional orders, and parameter forms.…”

Section: Introductionmentioning

confidence: 99%

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

Furati

Sarumi

2021

Applied Mathematical Modelling

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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.

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Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19

Cited by 53 publications

References 33 publications

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19

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

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