Gaidai Oleg, Cao Yu, Zhu Yan, Ashraf Alia, Liu Zirui, Li Hongchen
Department of Mechanics and Mathematics Ivan Franko Lviv State University Lviv Ukraine.
College of Engineering Science and Technology Shanghai Ocean University Shanghai China.
Anal Sci Adv. 2024 Aug 27;5(7-8):e2400027. doi: 10.1002/ansa.202400027. eCollection 2024 Aug.
Accurate 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.
准确估计美国每个相关州在任何时候的大流行可能性。2019冠状病毒病（COVID-19）是一种具有全球传播高潜力、低死亡率和发病率的传染病，给一些国家的公共卫生系统带来了压力。本研究旨在基于可用的临床数据和动态观察到的患者数量，同时考虑相关的地域和时间映射，对一种能够进行风险评估的新技术进行基准测试。多中心、基于人群和生物统计学策略已被用于处理原始/未过滤的医学调查数据。将极值统计从单变量扩展到双变量情况面临诸多挑战。首先，单变量极值类型定理不能直接扩展到双变量（二维）情况，更不用说高于二维的系统维度挑战了。评估任何感兴趣的国家/地区未来疫情爆发的风险。现有的生物统计学方法并不总是具有有效处理大区域维度和不同区域观测值之间交叉相关性的优势。这些方法处理多区域现象的时间观测值。将当代新颖的统计/可靠性技术直接应用于原始/未过滤的临床数据。当前研究概述了一种特别适用于在代表性时期内观察到的多区域环境、生物和公共卫生系统的新型生物系统风险评估技术。通过使用盖代多变量风险评估方法，可以正确评估疫情爆发的时空风险。基于原始/未过滤的临床调查数据，盖代多变量风险评估方法可应用于各种公共卫生应用。该研究的主要发现是对疫情爆发风险的评估以及相应的置信区间。本研究考察了未来全球COVID-19/严重急性呼吸综合征冠状病毒2（SARS-COV2）的疫情风险；然而，未讨论COVID-19/SARS-COV2的感染传播机制。
