COVID-19 and India: what next?

IF 2.1 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Information Discovery and Delivery Pub Date : 2020-07-24 DOI:10.1108/IDD-08-2020-0098

Ramesh Behl, Manit Mishra

{"title":"COVID-19 and India: what next?","authors":"Ramesh Behl, Manit Mishra","doi":"10.1108/IDD-08-2020-0098","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan.\n\n\nDesign/methodology/approach\nThe study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used.\n\n\nFindings\nThe study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states.\n\n\nPractical implications\nThe results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution.\n\n\nOriginality/value\nThe emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.\n","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":"1 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/IDD-08-2020-0098","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Discovery and Delivery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/IDD-08-2020-0098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}

引用次数: 1

Abstract

Purpose The study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan. Design/methodology/approach The study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. Findings The study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states. Practical implications The results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution. Originality/value The emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.

查看原文本刊更多论文

新冠肺炎与印度：下一步怎么办？

目的该研究旨在根据2020年4月1日至2020年6月20日期间公开的新冠肺炎数据，对印度及其五个感染率最高的邦：马哈拉施特拉邦、泰米尔纳德邦、德里、古吉拉特邦和拉贾斯坦邦进行预测建模，康复和死亡（SIRD）流行病预测建模框架。基本再现数R0是通过使用RStudio包R0的指数增长方法导出的。反映SIRD模型的微分方程已在Jupyter Notebook平台上使用Python 3.7.4求解。对于可视化，使用Python Matplotlib 3.2.1包。发现该研究提供了与印度及其五个州有关的高峰日期、新冠肺炎感染高峰数量和结束日期的见解。实际意义研究结果微妙地表明了彻底消除感染所需的努力。政治领导层和行业元老可以利用它来制定和执行经济政策。原创/价值如果不整合数据科学算法和流行病学理论框架，就不可能出现关于新冠肺炎生命周期的清晰画面。这项研究融合了这两个学科，根据印度及其五个州的新冠肺炎数据进行预测建模。对繁殖数量（R0）、易感人群（S）、有效接触率（ß）和病死率（S）等关键参数的人群特异性颗粒和客观评估已被用于为重症患者生成新冠肺炎生命周期模式的可视化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Information Discovery and Delivery INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

5.40

自引率

4.80%

发文量

期刊介绍： Information Discovery and Delivery covers information discovery and access for digital information researchers. This includes educators, knowledge professionals in education and cultural organisations, knowledge managers in media, health care and government, as well as librarians. The journal publishes research and practice which explores the digital information supply chain ie transport, flows, tracking, exchange and sharing, including within and between libraries. It is also interested in digital information capture, packaging and storage by ‘collectors’ of all kinds. Information is widely defined, including but not limited to: Records, Documents, Learning objects, Visual and sound files, Data and metadata and , User-generated content.