Shanghai Ocean University, Shanghai, China.
Central Marine Research and Design Institute, Saint Petersburg, Russia.
Biosystems. 2024 Jan;235:105073. doi: 10.1016/j.biosystems.2023.105073. Epub 2023 Nov 14.
This study presents novel methodology for pandemic risks assessment for a national health system of interest. The 2019 coronavirus disease (COVID-19) is a contagious disease with certain potential for worldwide spread and potentially significant effects on public health globally. Suggested methodology enables risks assessment of an epidemic, that may happen in the near future at any time, and in any national region of interest. Traditional spatio-temporal reliability methodologies do not have benefit of easily handling health system's high-dimensionality and complex cross-correlations between regional observations. Contrarily, advocated Gaidaireliability approach successfully addresses spatiotemporal clinical observations, as well as multi-regional epidemiological dynamics. This study aimed at benchmarking of a novel bio-statistical technique, enabling national health risk assessment, based on available clinical surveys with dynamically observed patient numbers, while accounting for relevant territorial mappings. The method developed in this study opens up the possibility of accurate epidemiological risk forecast for multi-regional biological and health systems. Suggested bioinformatical methodology may be used in a wide range of public health applications.
本研究提出了一种用于评估感兴趣的国家卫生系统的大流行风险的新方法。2019 年冠状病毒病（COVID-19）是一种具有一定全球传播潜力且可能对全球公共卫生产生重大影响的传染病。建议的方法能够评估可能在不久的将来随时在任何感兴趣的国家地区发生的流行病的风险。传统的时空可靠性方法没有优势可以轻松处理卫生系统的高维性和区域观测之间的复杂交叉相关性。相反，所提倡的 Gaidaire 可靠性方法成功地处理了时空临床观测以及多区域流行病学动态。本研究旨在对一种新的生物统计技术进行基准测试，该技术能够基于动态观察的患者数量的现有临床调查来评估国家卫生风险，同时考虑到相关的地域映射。本研究中开发的方法为多区域生物和卫生系统的精确流行病学风险预测开辟了可能性。建议的生物信息学方法可用于广泛的公共卫生应用。
