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Titlebook: Global COVID-19 Research and Modeling; A Historical Record Longbing Cao Book 2024 The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 16:11:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Global COVID-19 Research and Modeling
副標(biāo)題A Historical Record
編輯Longbing Cao
視頻videohttp://file.papertrans.cn/387/386062/386062.mp4
概述Answers tough questions: How do global researchers respond to COVID-19? How to quantify, model COVID-19 problems?.A historical record to collect, process, analyze the vasty COVID related literature ar
叢書(shū)名稱Data Analytics
圖書(shū)封面Titlebook: Global COVID-19 Research and Modeling; A Historical Record Longbing Cao Book 2024 The Editor(s) (if applicable) and The Author(s), under ex
描述.This book provides answers to fundamental and challenging questions regarding the global response to COVID-19. It creates a historical record of COVID-19 research conducted over the four years of the pandemic, with a focus on how researchers have responded, quantified, and modeled COVID-19 problems. Since mid-2021, we have diligently monitored and analyzed global scientific efforts in tackling COVID-19. Our comprehensive global endeavor involves collecting, processing, analyzing, and discovering COVID-19 related scientific literature in English since January 2020. This provides insights into how scientists across disciplines and almost every country and regions have fought against COVID-19. Additionally, we explore the quantification of COVID-19 problems and impacts through mathematics, AI, machine learning, data science, epidemiology, and domain knowledge. The book reports findings on publication quantities, impacts, collaborations, and correlations with the economy and infectionsglobally, regionally, and country-wide. These results represent the first and only holistic and systematic studies aimed at scientifically understanding, quantifying, and containing the pandemic. We hope
出版日期Book 2024
關(guān)鍵詞COVID-19 pandemic; Epidemic modeling; COVID-19 modeling; Machine learning; Statistical learning; Deep lea
版次1
doihttps://doi.org/10.1007/978-981-99-9915-6
isbn_softcover978-981-99-9917-0
isbn_ebook978-981-99-9915-6Series ISSN 2520-1859 Series E-ISSN 2520-1867
issn_series 2520-1859
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Book 2024D-19 research conducted over the four years of the pandemic, with a focus on how researchers have responded, quantified, and modeled COVID-19 problems. Since mid-2021, we have diligently monitored and analyzed global scientific efforts in tackling COVID-19. Our comprehensive global endeavor involves
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Deontic Modals are Hyperintensionalves and categorizations provide structural answers to how the modeling research addresses the aforementioned COVID-19 disease, problems, data and modeling complexities by quantifying COVID-19 problems.
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https://doi.org/10.1007/978-3-322-92761-3al inflation and recession, disasters related to misinformation, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, class
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AISDR: AI and Data Science for Crisis and Disaster Resilienceal inflation and recession, disasters related to misinformation, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, class
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