Gaidai Oleg, Yakimov Vladimir, van Loon Eric-Jan
Shanghai Ocean University, Shanghai, China.
Central Marine Research and Design Institute, Saint Petersburg, Russia.
Dialogues Health. 2023 Oct 27;3:100157. doi: 10.1016/j.dialog.2023.100157. eCollection 2023 Dec.
Global public health was recently hampered by reported widespread spread of new coronavirus illness, although morbidity and fatality rates were low. Future coronavirus infection rates may be accurately predicted over a long-time horizon, using novel bio-reliability approach, being especially well suitable for environmental multi-regional health and biological systems. The high regional dimensionality along with cross-correlations between various regional datasets being challenging for conventional statistical tools to manage.
To assess future risks of epidemiological outbreak in any province of interest, novel spatio-temporal technique has been proposed. In a multicenter, population-based environment, assess raw clinical data using state-of-the-art, cutting-edge statistical methodologies.
Authors have developed novel reliable long-term risk assessment methodology for future coronavirus infection outbreaks.
Based on national clinical patient monitoring raw dataset, it is concluded that although underlying data set data quality is questionable, the proposed method may be still applied.
尽管新型冠状病毒疾病的发病率和死亡率较低，但据报道其广泛传播最近阻碍了全球公共卫生。使用新颖的生物可靠性方法，可以在很长一段时间内准确预测未来的冠状病毒感染率，该方法特别适用于环境多区域健康和生物系统。高区域维度以及各种区域数据集之间的相互关联对传统统计工具来说具有挑战性。
为了评估任何感兴趣省份未来流行病学爆发的风险，提出了新颖的时空技术。在多中心、基于人群的环境中，使用最先进的前沿统计方法评估原始临床数据。
作者开发了用于未来冠状病毒感染爆发的新颖可靠的长期风险评估方法。
基于国家临床患者监测原始数据集，得出的结论是，尽管基础数据集的数据质量存在疑问，但所提出的方法仍可应用。
