A Study of Morbidity and Mortality from COVID-19 in India

Dalia Essam Eissa, E. Rashed, M. Eissa
{"title":"A Study of Morbidity and Mortality from COVID-19 in India","authors":"Dalia Essam Eissa, E. Rashed, M. Eissa","doi":"10.28991/scimedj-2022-0401-03","DOIUrl":null,"url":null,"abstract":"The recent Human Coronavirus 2019 (hCoV-19) pandemic has devastated the whole world and impacted all aspects of human life. One of the most comprehensively recorded data for this outbreak is the daily morbidities and mortalities record. The analysis of this dataset would provide insight into the pattern and progression of this disease. The present study focused on the quantitative investigation and descriptive statistical examination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as part of a series of evaluations for this epidemic in the primarily affected geopolitical regions. The year 2021 is worse than 2020 in terms of the recorded daily newly emerging cases and deaths, and there are no signs that there would be an improvement in 2022, as could be estimated from early warning signs, even if there could be an apparent decline in the outbreak waves. India is one of the major countries that have been adversely affected by this global pandemic. The present study addressed this nation as a detailed record of COVID-19 cases and deaths extracted from a chronologically arranged dataset for the newly emerged cases and deaths on a daily basis. Cumulative counts were calculated and logarithmically transformed. Two significant peaks - embracing multiple waves - were observed with tailing for morbidity and mortality, which were highly correlated. There were no signs of a recession in the outbreak census. However, relative calm periods between waves might be detected. There were rising trends in morbidities and mortalities with a clustering tendency upon examination of the run charts. The Morgan-Morgan-Finney (MMF) model was found to demonstrate the best-fitting non-linear curve for the transformed cumulative database. Derivatization of the model equation demonstrated a factor that could be used in the assessment of the outbreak effect numerically to show influence on the impacted population. Doi: 10.28991/SciMedJ-2022-0401-03 Full Text: PDF","PeriodicalId":74776,"journal":{"name":"SciMedicine journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SciMedicine journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28991/scimedj-2022-0401-03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The recent Human Coronavirus 2019 (hCoV-19) pandemic has devastated the whole world and impacted all aspects of human life. One of the most comprehensively recorded data for this outbreak is the daily morbidities and mortalities record. The analysis of this dataset would provide insight into the pattern and progression of this disease. The present study focused on the quantitative investigation and descriptive statistical examination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as part of a series of evaluations for this epidemic in the primarily affected geopolitical regions. The year 2021 is worse than 2020 in terms of the recorded daily newly emerging cases and deaths, and there are no signs that there would be an improvement in 2022, as could be estimated from early warning signs, even if there could be an apparent decline in the outbreak waves. India is one of the major countries that have been adversely affected by this global pandemic. The present study addressed this nation as a detailed record of COVID-19 cases and deaths extracted from a chronologically arranged dataset for the newly emerged cases and deaths on a daily basis. Cumulative counts were calculated and logarithmically transformed. Two significant peaks - embracing multiple waves - were observed with tailing for morbidity and mortality, which were highly correlated. There were no signs of a recession in the outbreak census. However, relative calm periods between waves might be detected. There were rising trends in morbidities and mortalities with a clustering tendency upon examination of the run charts. The Morgan-Morgan-Finney (MMF) model was found to demonstrate the best-fitting non-linear curve for the transformed cumulative database. Derivatization of the model equation demonstrated a factor that could be used in the assessment of the outbreak effect numerically to show influence on the impacted population. Doi: 10.28991/SciMedJ-2022-0401-03 Full Text: PDF
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
新冠肺炎在印度的发病率和死亡率研究
最近发生的2019年人类冠状病毒(hCoV-19)大流行摧毁了整个世界,影响了人类生活的方方面面。本次疫情最全面记录的数据之一是每日发病率和死亡率记录。对该数据集的分析将有助于深入了解这种疾病的模式和进展。本研究的重点是对严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)进行定量调查和描述性统计检查,作为对主要受影响地缘政治区域的这一流行病进行一系列评估的一部分。就每日记录的新出现病例和死亡人数而言,2021年比2020年更糟糕,而且从早期预警迹象可以估计,即使爆发波可能明显减少,也没有迹象表明2022年情况会有所改善。印度是受到这一全球流行病不利影响的主要国家之一。本研究对这个国家进行了详细的COVID-19病例和死亡记录,这些记录是从按时间顺序排列的每天新出现的病例和死亡数据集中提取的。计算累积计数并进行对数变换。在发病率和死亡率方面,观察到两个显著的峰(包含多个波),两者高度相关。在疫情普查中没有出现经济衰退的迹象。然而,波浪之间的相对平静期可能会被探测到。发病率和死亡率呈上升趋势,经运行图检查呈聚类趋势。发现Morgan-Morgan-Finney (MMF)模型对转换后的累积数据库具有最佳拟合的非线性曲线。模型方程的推导证明了一个因子,该因子可用于数值评估爆发效应,以显示对受影响人群的影响。Doi: 10.28991/SciMedJ-2022-0401-03全文:PDF
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 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
8 weeks
期刊最新文献
Comparative Study on Prediction of Survival Event of Heart Failure Patients Using Machine Learning and Statistical Algorithms Systematic Review on the Effects of Food on Mental Health via Gut Microbiome Giant Fungating Borderline Phyllodes Tumor of the Breast Community Preparedness, Acceptability, and Uptake of UTT Services in PHC Facilities Effect of Season on Blood Transfusion Patterns: A Retrospective Study
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1