COVID-19 epidemic forecast in USA East coast by novel reliability approach

Gaidai, Oleg; Xing, Yihan; Xu, Xiaosen

doi:10.21203/rs.3.rs-1573862/v1

Cited by 19 publications

(31 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the MDOF response process was assumed ergodic, the probability for is independent of and entirely on the conditioning level that Eq. ( 5 ) is similar to the mean up-crossing rate formula for the probability of exceedance 4 . There is gradual convergence with respect to the conditioning parameter …”

Section: Methodsmentioning

confidence: 55%

“…The latter is usually due to a large number of degrees of system freedom and random variables governing dynamic systems. The reliability of complex structural systems can be straightforwardly estimated either by having enough measurements or by direct numerical Monte Carlo simulations [3][4][5][6][7][8][9][10][11] . However, experimental and computational often are unaffordable for many complex engineering dynamic systems.…”

Section: Methodsmentioning

confidence: 99%

“…The majority of current extrapolation techniques, which are frequently used in engineering practice, depending on the assumption of certain extrapolation functional classes, e.g. [3][4][5][6][7][8][9] .…”

Section: Novel Extrapolation Method Accurate Prediction Of Extreme Va...mentioning

confidence: 99%

See 2 more Smart Citations

Novel methods for wind speeds prediction across multiple locations

Gaidai

Yan

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

show abstract

Section: Methodsmentioning

confidence: 55%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Novel methods for wind speeds prediction across multiple locations

Gaidai

Yan

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the latter choice obviously introduces bias (accumulation point) at λ ¼ 0:5 if the number of countrys is large, in this study the number of regions is not that large (below 20 national regions, see Ref. 30 for similar USA study) and proper extrapolation technique may easily circumvent the above mentioned accumulation point bias.…”

Section: Use Casementioning

confidence: 90%

“…For example, Gumbel used EVT to estimate the demographic of various populations in. 30 Similarly, in Ref. 31, the author builds on this EVT theory to determine the fitness effect using a Beta-Burr distribution.…”

Section: Introductionmentioning

confidence: 99%

A novel statistical method for long-term coronavirus modelling

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Novel Reliability Method Validation for Floating Wind Turbines

Gaidai

Wang

Xing

et al. 2023

Adv Energy and Sustain Res

Self Cite

View full text Add to dashboard Cite

Wind turbines and associated parts are susceptible to cyclic stresses, including torque, bending, and longitudinal stress, and twisting moments. Therefore, research on the resilience of dynamic systems under such high loads is crucial for design and future risk‐free operations. The method described in this study is beneficial for multidimensional structural responses that have undergone sufficient numerical simulation or measurement. In contrast to established dependability methodologies, the unique technique does not need to restart the numerical simulation each time the system fails. Herein, it is demonstrated that it is also possible to accurately predict the probability of a system failure in the event of a measurable structural reaction. In contrast to well‐established bivariate statistical methods, which are known to predict extreme response levels for 2D systems accurately, this study validates a novel structural reliability method that is particularly suitable for multidimensional structural responses. In contrast to conventional methods, the novel reliability approach does not invoke a multidimensional reliability function in the Monte Carlo numerical simulation case. As demonstrated in this study, it is also possible to accurately anticipate the likelihood of a system failure in the case of a measurable structural reaction.

show abstract

COVID-19 epidemic forecast in USA East coast by novel reliability approach

Cited by 19 publications

References 19 publications

Novel methods for wind speeds prediction across multiple locations

Novel methods for wind speeds prediction across multiple locations

A novel statistical method for long-term coronavirus modelling

Novel Reliability Method Validation for Floating Wind Turbines

Contact Info

Product

Resources

About