Prediction of death rates for cardiovascular diseases and cancers

Abstract

Background To estimate cardiovascular and cancer death rates by regions and time periods. Design Novel statistical methods were used to analyze clinical surveillance data. Methods A multicenter, population‐based medical survey was performed. Annual recorded deaths from cardiovascular diseases were analyzed for all 195 countries of the world. It is challenging to model such data; few mathematical models can be applied because cardiovascular disease and cancer data are generally not normally distributed. Results A novel approach to assessing the biosystem reliability is introduced and has been found to be particularly suitable for analyzing multiregion environmental and healthcare systems. While traditional methods for analyzing temporal observations of multiregion processes do not deal with dimensionality efficiently, our methodology has been shown to be able to cope with this challenge. Conclusions Our novel methodology can be applied to public health and clinical survey data.

Oil tanker under ice loadings

“…Conditioning level k was chosen according to the convergence of modified Weibull functions, see Sect. 5 7,[51][52][53][54][55][56][57][58][59] .…”

Cardio-Cancer Risks Cross-Validation by Gaidai-Yakimov Reliability Method

“…Classic contemporary health systems reliability approaches do not have advantage of easily dealing with observed clinical time-series, originating from complex bio-systems with highdimensionality and non-linear cross correlations between various bio-system's components. The key advantages of Gaidai-Yakimov method, proposed here, being its capacity to analyze reliability and risks of high-dimensional non-linear dynamic bio and public health systems [53][54][55][56][57][58][59][60]. Despite apparent simplicity, the current work presents essentially novel multi-dimensional bio-modelling methodology, along with methodological route to apply epidemic/chronical disease forecasting, while bio or health system has not yet reached its critical/hazard levels.…”

A novel statistical method for long-term coronavirus modelling

“…and other similar recent epidemics were receiving much attention in the modern research community. [1][2][3][4][5][6][7][8][9][10][11] Generally, it is quite challenging to calculate realistic biological system reliability factors and outbreak probabilities under actual epidemic conditions by using conventional theoretical statistical methods. [12][13][14][15][16][17][18][19] The latter is usually due to the fact that dynamic biological and environmental systems possess high number of degrees of freedom (in other words dimensional components), as well as system dependency on locationmaking bio-system of interest spatio-temporal.…”

“…. The mean up-crossing rate is given by the Rice's formula, given in Equation (7) 7) relies on the Poisson assumption that is up-crossing events of high λ levels (in this paper, it is λ ≥ 1) can be assumed to be independent. The latter may not be the case for narrowband bio-system components and higher-level dynamical systems that exhibit cascading failures in different dimensions, subsequent in time, caused by intrinsic inter-dependency between extreme events, manifesting itself in the appearance of highly correlated local maxima clusters within the assembled vector R…”