Shanghai Ocean University, Shanghai, China.
University of Stavanger, Stavanger, Norway.
Sci Rep. 2023 Jan 6;13(1):303. doi: 10.1038/s41598-023-27547-x.
Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data.
癌症是一种全球性疾病，会导致严重的发病率和死亡率，并给全球公共卫生带来巨大负担。由于癌症波的非平稳性和复杂性，对这种现象进行建模非常复杂。直接将现代新颖的统计方法应用于原始临床数据。以估计任何地点任何时期的极端癌症死亡率的可能性。传统的统计方法处理多区域过程的时间观测，无法充分处理大量的区域维度和各种区域变量之间的交叉相关性。环境和健康系统。由于癌症的非平稳性和复杂性，对这种现象进行建模具有挑战性。本文提供了一种独特的适用于多区域环境和健康系统的生物系统可靠性技术。在长时间监测下，它可以可靠地预测异常癌症死亡率的长期可能性。传统的统计方法处理多区域过程的时间观测，无法有效处理大量的区域维度和多个区域数据之间的交叉相关性。提供的方法可以根据他们的临床调查数据应用于许多公共卫生应用。
