A study of the COVID-19 epidemic in India using the SEIRD model

IF 0.6 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Quantitative Biology Pub Date : 2021-01-01 DOI:10.15302/j-qb-021-0260

R. Banerjee, S. Bhattacharjee, P. Varadwaj

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引用次数: 1

Abstract

Background: The coronavirus pandemic (COVID-19) is causing a havoc globally, exacerbated by the newly discovered SARS-CoV-2 virus. Due to its high population density, India is one of the most badly effected countries from the first wave of COVID-19. Therefore, it is extremely necessary to accurately predict the state-wise and overall dynamics of COVID-19 to get the effective and efficient organization of resources across India. Methods: In this study, the dynamics of COVID-19 in India and several of its selected states with different demographic structures were analyzed using the SEIRD epidemiological model. The basic reproductive ratio R0 was systemically estimated to predict the dynamics of the temporal progression of COVID-19 in India and eight of its states, Andhra Pradesh, Chhattisgarh, Delhi, Gujarat, Madhya Pradesh, Maharashtra, Tamil Nadu, and Uttar Pradesh. Results: For India, the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October, 2020. Furthermore, we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar results. The early imposition of a nation-wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly. Conclusion: After comparing our calculations using India's data to the real life dynamics observed in Italy and Russia, we can conclude that the SEIRD model can predict the dynamics of COVID-19 with sufficient accuracy.

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基于SEIRD模型的印度COVID-19疫情研究

背景:新发现的SARS-CoV-2病毒加剧了冠状病毒大流行(COVID-19)在全球范围内造成的破坏。由于人口密度高，印度是受第一波COVID-19影响最严重的国家之一。因此，准确预测2019冠状病毒病的州和整体动态，以有效和高效地组织印度各地的资源，是非常必要的。方法:本研究采用SEIRD流行病学模型，分析了2019冠状病毒病在印度及其几个具有不同人口结构的选定邦的动态。系统估计了基本生殖比率R0，以预测2019冠状病毒病在印度及其八个邦(安得拉邦、恰蒂斯加尔邦、德里、古吉拉特邦、中央邦、马哈拉施特拉邦、泰米尔纳德邦和北方邦)的时间进展动态。结果:对于印度，SEIRD模型计算显示，预计感染高峰将出现在2020年10月中旬左右。此外，我们将模型情景与每日感染病例的高斯拟合进行了比较，并获得了类似的结果。全国范围内的早期封锁减少了感染病例的数量，但大大推迟了感染高峰的出现。结论:将我们使用印度数据计算的结果与意大利和俄罗斯观察到的现实动态进行比较后，我们可以得出结论，SEIRD模型可以足够准确地预测COVID-19的动态。

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

Quantitative Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

5.00

自引率

3.20%

发文量

264

期刊介绍： Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.