通过盖代多元风险评估方法预测 2019 年冠状病毒疾病的未来全球流行趋势

IF 3 Q2 CHEMISTRY, ANALYTICAL Analytical science advances Pub Date : 2024-08-27 DOI:10.1002/ansa.202400027
Oleg Gaidai, Yu Cao, Yan Zhu, Alia Ashraf, Zirui Liu, Hongchen Li
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摘要

准确估计美国各州在任何时间发生大流行病的可能性。冠状病毒病 2019(COVID-19)是一种极有可能在全球传播的传染病,死亡率和发病率都很低,给国家公共卫生系统带来了一定的压力。这项研究旨在根据现有的临床数据和动态观察到的患者人数,同时考虑到相关的地域和时间映射,对一种能够进行危害评估的新型技术进行基准测试。在处理原始/未经过滤的医疗调查数据时,采用了多中心、基于人口和生物统计的策略。将极值统计从单变量扩展到双变量会遇到许多挑战。首先,单变量极值类型定理无法直接扩展到双变量(2D)情况,更不用说系统维度高于 2D 的挑战了。评估任何感兴趣的国家/地区未来爆发疫情的风险。现有的生物统计方法并不总能有效处理大区域维度和不同区域观测值之间的交叉相关性。这些方法需要处理多区域现象的时间观测。将当代新型统计/可靠性技术直接应用于原始/未过滤的临床数据。目前的研究概述了一种新型生物系统危害评估技术,该技术特别适用于多区域环境、生物和公共卫生系统的代表性时期观测。利用 Gaidai 多变量危害评估方法,可对流行病爆发的时空风险进行适当评估。基于原始/未经过滤的临床调查数据,Gaidai 多变量危害评估方法可应用于各种公共卫生领域。该研究的主要发现是对流行病爆发风险的评估,以及与之相匹配的置信区间。本研究对未来全球 COVID-19 病毒/严重急性呼吸系统综合症冠状病毒 2(SARS-COV2)的流行风险进行了研究;但尚未讨论 COVID-19/SARS-COV2 的感染传播机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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