COVID - 19感染a建模方法的数学洞察

K. Arora, Pooja Khurana, Deepak Kumar, Bhanu Sharma
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引用次数: 0

摘要

数学在传染病研究中的应用日益丰富。疾病的复杂性适合于定量方法,因为它给事态的新转变带来了困难和机会。因此,计算模型通过帮助澄清成分和给出可批准的定量期望来证明流行病学研究。定量模型的持续扩展倾向于对流行病(COVID-19)的开始以及治疗反应和反对进行大量查询。这些模型使研究人员能够更好地理解物理现象。计算模型可以补充探索性和临床研究,但也可以挑战流量标准,重新分类我们对驱动流行病学的系统的理解,并塑造未来的研究。©2021 Scrivener Publishing LLC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mathematical Insight of COVID‐19 Infection—A Modeling Approach
Application of mathematics has gotten progressively abundant in epidemic disease research. The complexity of disease is appropriate to quantitative methodologies as it gives difficulties and chances to new turns of events. Thusly, computational modeling demonstrating to epidemiology research by assisting with clarifying components and by giving quantitative expectations that can be approved. The ongoing extension of quantitative models tends to numerous inquiries with respect to Epidemic disease (COVID-19) inception, and treatment reactions and opposition. These models have allowed researchers to better understand the physical phenomena. Computational models can supplement exploratory and clinical investigations, yet additionally challenge flow standards, reclassify our comprehension of systems driving epidemiology and shape future research. © 2021 Scrivener Publishing LLC.
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