ArticleChaos, solitons, and fractals2020
Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 61 citations in OpenAlex.
- Decoding Viral Transmission Dynamics in Structured Populations Using a Microfluidic Herd-Immunity-on-a-Chip Platform.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A fractional-order mathematical model for analyzing the pandemic trend of COVID-19.Mathematical methods in the applied sciences · 2022Article
- A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing.Expert systems · 2022Article
- How efficient is contact tracing in mitigating the spread of COVID-19? a mathematical modeling approach.Applied mathematical modelling · 2022Article
- Mathematical analysis of the dynamics of COVID-19 in Africa under the influence of asymptomatic cases and re-infection.Mathematical methods in the applied sciences · 2022Article
- Investigation of a time-fractional COVID-19 mathematical model with singular kernel.Advances in continuous and discrete models · 2022Article
- Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Fractional model for the spread of COVID-19 subject to government intervention and public perception.Applied mathematical modelling · 2021Article
- A fractional-order compartmental model for the spread of the COVID-19 pandemic.Communications in nonlinear science & numerical simulation · 2021Article
- Modeling COVID-19 spreading dynamics and unemployment rate evolution in rural and urban counties of Alabama and New York using fractional derivative models.Results in physics · 2021Article
- Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach.Epidemiologia (Basel, Switzerland) · 2021Article
- A fractional order SITR mathematical model for forecasting of transmission of COVID-19 of India with lockdown effect.Results in physics · 2021Article
- Fractional optimal control problem for an age-structured model of COVID-19 transmission.Chaos, solitons, and fractals · 2021Article
- A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19.Chaos, solitons, and fractals · 2021Article
- CASE-CF: Context Aware Smart Epidemic Control Framework.New generation computing · 2021Article
- Article
- Fractional Model and Numerical Algorithms for Predicting COVID-19 with Isolation and Quarantine Strategies.International journal of applied and computational mathematics · 2021Article
- Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia.Chaos, solitons, and fractals · 2020Article
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6 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Fractional calculus provides a promising tool for modeling fractional dynamics in computational biology, and this study tests the applicability of fractional-derivative equations (FDEs) for modeling the dynamics and mitigation scenarios of the novel coronavirus for the first time. The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the evolution dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed over the last three months (up to 3/22/2020) in China. The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The validated SEIR model was then applied to predict COVID-19 evolution in the United States, Italy, Japan, and South Korea. In addition, a time FDE model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the FDE model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread. Therefore, caution is needed when applying FDEs to model the coronavirus outbreak, since specific COVID-19 kinetics may not exhibit nonlocal behavior. Particularly, the spread of COVID-19 may be affected by the rapid improvement of health care systems which may remove the memory impact in COVID-19 dynamics (resulting in a short-tailed recovery curve), while the death toll and mitigation of COVID-19 can be captured by the time FDEs due to the nonlocal, memory impact in fatality and human activities.
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