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具有社交距离参数的分数模型用于COVID-19传播的早期估计。

Fractional Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread.

作者信息

Chandra Saroj Kumar, Bajpai Manish Kumar

机构信息

Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.

出版信息

Arab J Sci Eng. 2022;47(1):209-218. doi: 10.1007/s13369-021-05827-w. Epub 2021 Jun 23.

DOI:10.1007/s13369-021-05827-w
PMID:34178570
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8220443/
Abstract

COVID-19 disease has come up as a life-threatening outbreak at end of 2019. It has impacted almost all countries in the world. The major source of COVID-19 is a novel beta coronavirus. COVID-19 had a great impact on world throughout the year 2020. Now, the situation is becoming normal due to the invention of the vaccine. All major countries started large vaccination drives. Mathematical models are used to study the impact of different measures used to decrease pandemics. Mathematical models such as susceptible-infected-removed model and susceptible-exposed-infected-removed are used to predict the spread of diseases. But these models are not suitable to predict COVID-19 spread due to various preventive measures (social distancing and quarantine) applied to reduce spread. Hence, in the present manuscript, a novel fractional mathematical model with a social distancing parameter has been proposed to provide early COVID-19 spread estimation. Fractional calculus provides flexibility in choosing arbitrary order of derivative which controls data sensitivity. The model has been validated with real data set. It has been observed that the proposed model is highly accurate in spread estimation.

摘要

2019年末，新型冠状病毒肺炎（COVID-19）疫情爆发，对生命构成威胁。它几乎影响了世界上所有国家。COVID-19的主要病原体是一种新型β冠状病毒。2020年全年，COVID-19对全球产生了巨大影响。如今，由于疫苗的发明，情况正趋于正常。所有主要国家都启动了大规模疫苗接种行动。数学模型被用于研究为减少疫情所采取的不同措施的影响。诸如易感-感染-移除模型和易感-暴露-感染-移除模型等数学模型被用于预测疾病的传播。但由于为减少传播而采取的各种预防措施（社交距离和隔离），这些模型并不适合预测COVID-19的传播。因此，在本手稿中，提出了一种具有社交距离参数的新型分数阶数学模型，以提供COVID-19早期传播估计。分数阶微积分在选择控制数据敏感性的任意阶导数方面提供了灵活性。该模型已通过真实数据集进行验证。据观察，所提出的模型在传播估计方面具有很高的准确性。

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