Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method

Author:

Gaidai Oleg1ORCID,Cao Yu2,Zhu Yan3,Ashraf Alia2,Liu Zirui2,Li Hongchen2

Affiliation:

1. Department of Mechanics and Mathematics Ivan Franko Lviv State University Lviv Ukraine

2. College of Engineering Science and Technology Shanghai Ocean University Shanghai China

3. School of Naval Architecture and Ocean Engineering Jiangsu University of Science and Technology Zhenjiang China

Abstract

AbstractAccurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID‐19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population‐based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,—not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio‐statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross‐correlation between various regional observations. These methods deal with temporal observations of multi‐regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio‐system hazard assessment technique that is particularly suited for multi‐regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID‐19/severe acute respiratory syndrome coronavirus 2 (SARS‐COV2) epidemic risks have been examined in the current study; however, COVID‐19/SARS‐COV2 infection transmission mechanisms have not been discussed.

Publisher

Wiley

Reference104 articles.

1. Accessed January 2024.https://ourworldindata.org/covid‐cases#daily‐confirmed‐cases‐per‐million‐people

2. Understanding epidemic data and statistics: A case study of COVID‐19

3. Coronavirus disease (COVID‐19)–Statistics and research;Roser M;Our World in Data,2020

4. Accurate Statistics on COVID-19 Are Essential for Policy Guidance and Decisions

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3