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Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19
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 di...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269951/ https://www.ncbi.nlm.nih.gov/pubmed/32834580 http://dx.doi.org/10.1016/j.chaos.2020.109959 |
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author | Zhang, Yong Yu, Xiangnan Sun, HongGuang Tick, Geoffrey R. Wei, Wei Jin, Bin |
author_facet | Zhang, Yong Yu, Xiangnan Sun, HongGuang Tick, Geoffrey R. Wei, Wei Jin, Bin |
author_sort | Zhang, Yong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7269951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72699512020-06-05 Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 Zhang, Yong Yu, Xiangnan Sun, HongGuang Tick, Geoffrey R. Wei, Wei Jin, Bin Chaos Solitons Fractals Article 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. Elsevier Ltd. 2020-09 2020-06-04 /pmc/articles/PMC7269951/ /pubmed/32834580 http://dx.doi.org/10.1016/j.chaos.2020.109959 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Zhang, Yong Yu, Xiangnan Sun, HongGuang Tick, Geoffrey R. Wei, Wei Jin, Bin Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title | Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title_full | Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title_fullStr | Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title_full_unstemmed | Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title_short | Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 |
title_sort | applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269951/ https://www.ncbi.nlm.nih.gov/pubmed/32834580 http://dx.doi.org/10.1016/j.chaos.2020.109959 |
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