Computational models to study the infectious disease COVID-19: a review

Q3 Mathematics
Amit Sharma, Gaurang Sharma, Fateh Singh
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引用次数: 0

Abstract

The current COVID-19 pandemic that is still waging in the world is a threat to humanity, and the cure for it is a big challenge for researchers, scientists, and the bio-medical community. However, the vaccine is available nowadays, but the infection is still increasing globally. In this paper, the different types of existing mathematical models related to the COVID-19 outbreak, namely, SI, SIS, SEIS, SIR, SIRS, SEIR, AI, logistic growth model, Poisson model and the expanded models are discussed. The basic reproduction number is one of the most important parameters for predicting the future of COVID-19, and existing models use it to forecast coronavirus disease around the globe. The motive of present study is to elaborate the key factors related to control of pandemic and to introduce the different type of existing mathematical models and applications to the readers under one platform. The initial description of the existing mathematical models gives us better insight of the disease and based on existing literature, future prediction of the spread of COVID-19 can be done more accurately and efficiently.
研究传染病COVID-19的计算模型综述
目前仍在世界范围内肆虐的新冠肺炎疫情是对人类的威胁,对研究人员、科学家和生物医学界来说,治愈新冠肺炎是一个巨大的挑战。然而,现在疫苗是可用的,但全球感染仍在增加。本文讨论了与新冠肺炎疫情相关的不同类型的现有数学模型,即SI、SIS、SEIS、SIR、SIRS、SEIR、AI、logistic增长模型、泊松模型和扩展模型。基本繁殖数是预测COVID-19未来最重要的参数之一,现有模型利用它来预测全球范围内的冠状病毒疾病。本研究的动机是阐述与流行病控制有关的关键因素,并在一个平台下向读者介绍不同类型的现有数学模型和应用。现有数学模型的初步描述使我们能够更好地了解疾病,并且基于现有文献,可以更准确有效地预测COVID-19的未来传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
30
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