Gaidai Oleg, Xing Yihan
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, China.
Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Stavanger, Norway.
Bioinform Biol Insights. 2023 Apr 11;17:11779322231161939. doi: 10.1177/11779322231161939. eCollection 2023.
This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.
本研究倡导一种新颖的时空方法，用于准确预测巴西任何感兴趣的州在任何时间的新冠疫情发生概率，并使用了原始临床观测数据。本文描述了一种新颖的生物系统可靠性方法，特别适用于在足够长的时间段内观测到的多区域环境和健康系统，从而对病毒爆发概率进行稳健的长期预测。研究考虑了巴西所有受影响州记录的新冠患者每日数量。这项工作旨在对新颖的先进方法进行基准测试，以便在考虑相关区域地图的同时动态分析观测到的患者数量。所倡导的方法可能有助于监测和预测各种多区域生物系统中未来可能的疫情爆发。建议的方法可用于各种现代公共卫生应用，有效利用其临床调查数据。
