对一个中等收入国家 COVID-19 流行病的描述性分析及前瞻性见解

Norvin P. Bansilan, Jomar F. Rabajante
{"title":"对一个中等收入国家 COVID-19 流行病的描述性分析及前瞻性见解","authors":"Norvin P. Bansilan,&nbsp;Jomar F. Rabajante","doi":"10.1016/j.health.2024.100320","DOIUrl":null,"url":null,"abstract":"<div><p>The outbreak of COVID-19 unleashed an unprecedented global pandemic, profoundly impacting lives and economies worldwide. Recognizing its severity, the World Health Organization (WHO) swiftly declared it a public health emergency of international concern. In response to this crisis, collaborative efforts have been underway to control the disease and minimize its health and socio-economic impacts worldwide. The COVID-19 epidemic curve holds vital insights into the history of exposure, transmission, testing, tracing, social distancing measures, community lockdowns, quarantine, isolation, and treatment, offering a comprehensive perspective on the nation’s response. One approach to gaining crucial insights is through meticulous analysis of available datasets, empowering us to effectively inform future strategies and responses. This study aims to provide descriptive data analytics of the COVID-19 pandemic in the Philippines, summarizing the country’s fight by visualizing epidemiological and mobility datasets, revisiting scientific papers and news articles, and creating a timeline of the critical issues faced during the pandemic. By leveraging these multifaceted analyses, policymakers and health authorities can make informed decisions to enhance preparedness, expand inter-agency cooperation, and effectively combat future public health crises. This study seeks to serve as a valuable resource, guiding nations worldwide in comprehending and responding to the challenges posed by COVID-19 and beyond.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100320"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000224/pdfft?md5=b4bbf16b2a3cd55d8c9db39d5f349d1d&pid=1-s2.0-S2772442524000224-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A descriptive analytics of the COVID-19 pandemic in a middle-income country with forward-looking insights\",\"authors\":\"Norvin P. Bansilan,&nbsp;Jomar F. Rabajante\",\"doi\":\"10.1016/j.health.2024.100320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The outbreak of COVID-19 unleashed an unprecedented global pandemic, profoundly impacting lives and economies worldwide. Recognizing its severity, the World Health Organization (WHO) swiftly declared it a public health emergency of international concern. In response to this crisis, collaborative efforts have been underway to control the disease and minimize its health and socio-economic impacts worldwide. The COVID-19 epidemic curve holds vital insights into the history of exposure, transmission, testing, tracing, social distancing measures, community lockdowns, quarantine, isolation, and treatment, offering a comprehensive perspective on the nation’s response. One approach to gaining crucial insights is through meticulous analysis of available datasets, empowering us to effectively inform future strategies and responses. This study aims to provide descriptive data analytics of the COVID-19 pandemic in the Philippines, summarizing the country’s fight by visualizing epidemiological and mobility datasets, revisiting scientific papers and news articles, and creating a timeline of the critical issues faced during the pandemic. By leveraging these multifaceted analyses, policymakers and health authorities can make informed decisions to enhance preparedness, expand inter-agency cooperation, and effectively combat future public health crises. This study seeks to serve as a valuable resource, guiding nations worldwide in comprehending and responding to the challenges posed by COVID-19 and beyond.</p></div>\",\"PeriodicalId\":73222,\"journal\":{\"name\":\"Healthcare analytics (New York, N.Y.)\",\"volume\":\"5 \",\"pages\":\"Article 100320\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772442524000224/pdfft?md5=b4bbf16b2a3cd55d8c9db39d5f349d1d&pid=1-s2.0-S2772442524000224-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Healthcare analytics (New York, N.Y.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772442524000224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare analytics (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772442524000224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

COVID-19 的爆发引发了一场史无前例的全球大流行,对全世界的生命和经济造成了深远影响。世界卫生组织(WHO)认识到这一疾病的严重性,迅速宣布其为国际关注的公共卫生紧急事件。为应对这一危机,各方通力合作,努力控制疫情,将其对全球健康和社会经济的影响降至最低。COVID-19 疫情曲线对接触、传播、检测、追踪、社会隔离措施、社区封锁、检疫、隔离和治疗的历史具有重要的启示意义,为国家的应对措施提供了一个全面的视角。获得重要见解的方法之一是对现有数据集进行细致分析,使我们能够有效地为未来战略和应对措施提供信息。本研究旨在提供菲律宾 COVID-19 大流行的描述性数据分析,通过可视化流行病学和流动性数据集、重温科学论文和新闻报道以及创建大流行期间所面临关键问题的时间表,总结菲律宾的抗击工作。通过利用这些多方面的分析,政策制定者和卫生当局可以做出明智的决策,以加强准备工作,扩大机构间合作,并有效应对未来的公共卫生危机。本研究旨在提供宝贵的资源,指导世界各国理解和应对 COVID-19 及其后带来的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
A descriptive analytics of the COVID-19 pandemic in a middle-income country with forward-looking insights

The outbreak of COVID-19 unleashed an unprecedented global pandemic, profoundly impacting lives and economies worldwide. Recognizing its severity, the World Health Organization (WHO) swiftly declared it a public health emergency of international concern. In response to this crisis, collaborative efforts have been underway to control the disease and minimize its health and socio-economic impacts worldwide. The COVID-19 epidemic curve holds vital insights into the history of exposure, transmission, testing, tracing, social distancing measures, community lockdowns, quarantine, isolation, and treatment, offering a comprehensive perspective on the nation’s response. One approach to gaining crucial insights is through meticulous analysis of available datasets, empowering us to effectively inform future strategies and responses. This study aims to provide descriptive data analytics of the COVID-19 pandemic in the Philippines, summarizing the country’s fight by visualizing epidemiological and mobility datasets, revisiting scientific papers and news articles, and creating a timeline of the critical issues faced during the pandemic. By leveraging these multifaceted analyses, policymakers and health authorities can make informed decisions to enhance preparedness, expand inter-agency cooperation, and effectively combat future public health crises. This study seeks to serve as a valuable resource, guiding nations worldwide in comprehending and responding to the challenges posed by COVID-19 and beyond.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Healthcare analytics (New York, N.Y.)
Healthcare analytics (New York, N.Y.) Applied Mathematics, Modelling and Simulation, Nursing and Health Professions (General)
CiteScore
4.40
自引率
0.00%
发文量
0
审稿时长
79 days
期刊最新文献
Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan An electrocardiogram signal classification using a hybrid machine learning and deep learning approach
×
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