A novel statistical method for long-term coronavirus modelling

Abstract

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Deconvolution approach for floating wind turbines

“…It should be emphasized that the majority of extrapolation techniques that are often used in offshore engineering practice do, in fact, rely on assuming certain extrapolation parametric/functional classes 8–33,39–41 . Among the widely used techniques, the techniques currently in use are peak over the threshold (POT), 31 Pareto, modified Weibull method, 42–47 bivariate modified Weibull, 48,49 traditional Weibull fit, and Gumbel fit; these are just a few examples of fitting techniques. In the simplest instance, PDFs pX1 and $${{p}}_{{X}_{2}}$$ may represent two identically distributed processes, $${X}_{1}(t)$$ and$$\,{X}_{2}(t)$$, with $${p}_{{X}_{1}}={p}_{{X}_{2}}$$.…”

“…The in situ metocean data were then used to quickly estimate the joint PDF $$p(U,{H}_{s}{T}_{p})$$, which produced a three‐dimensional (3D) dispersed diagram with $${{H}}_{s}$$ and $${{T}}_{p}$$ standing for significant wave height and peak‐spectral period, respectively. This strategy promotes the direct long‐term MC simulation method, 40,42–46,48,49 which has the benefit of not relying on any assumptions or simplifications. A semi‐submersible FOWT model with one main column and three outer offset columns is shown in Figure 2.…”

Carbon Storage Tanker Lifetime Assessment

“…Thus L being random process (variable), representing dynamic system lifetime. [ 15–22 ] In particular, when the number of system dimensions (failure modes) was high, modern engineering reliability methodologies did not provide a formula to analyze service level agreements of complex energy systems. In theory, it was eligible to assess target cumulative density function $$$$\begin{eqnarray}{\rm{LTD\ }}\left( L \right) = {\rm{Prob}}\left( {{\rm{Lifetime}} \le L} \right)\end{eqnarray}$$$$an easy manner for a complicated environmental system, employing either enough measurement data or direct Monte Carlo simulations.…”

On safe offshore energy exploration in the Gulf of Eilat

“…For the real measured copula data, which is non‐Gaussian and cross‐correlated by non‐Archimedean, it can be found that benefits of using Gaidai‐Xing method are much more obvious. Finally, for a given bivariate failure or hazard limit, computational effort of Gaidai‐Xing technique is considerably less than ACER2D, since when analysing large datasets, ACER2D performs 2D surface interpolation 27–31,56 …”