Calculating the need for intensive care beds

2012 ◽  
Vol 97 (11) ◽  
pp. 943-946 ◽  
Author(s):  
Gale A Pearson ◽  
Fiona Reynolds ◽  
John Stickley
Keyword(s):  
Monte Carlo ◽  
Intensive Care ◽  
Monte Carlo Method ◽  
Average Length ◽  
Admission Rate ◽  
Final Analysis ◽  
Monte Carlo Analysis ◽  
Catchment Population ◽  
The Monte Carlo Method ◽  
Refused Admissions

AimPrompted by high refused admission rates, we sought to model demand for our 20 bed paediatric intensive care unit.MethodsWe analysed activity (admissions) and demand (admissions plus refused admissions). The recommended method for calculating the required number of intensive care beds assumes a Poisson distribution based upon the size of the local catchment population, the incidence of intensive care admission and the average length of stay. We compared it to the Monte Carlo method which would also include supra-regional referrals not otherwise accounted for but which, due to their complexity, tend to have a longer stay than average. For the new method we assigned data from randomly selected emergency admissions to the refused admissions. We then compared occupancy scenarios obtained by random sampling from the data with replacement.ResultsThere was an increase in demand for intensive care over time. Therefore, in order to provide an up-to-date model, we restricted the final analysis to data from the two most recent years (2327 admissions and 324 refused admissions). The conventional method suggested 27 beds covers 95% of the year. The Monte Carlo method showed 95% compliance with 34 beds, with seasonal variation quantified as 30 beds needed in the summer and 38 in the winter.ConclusionBoth approaches suggest that the high refused admission rate is due to insufficient capacity. The Monte Carlo analysis is based upon the total workload (including supra-regional referrals) and predicts a greater bed requirement than the current recommended approach.

Combined Cycle Performance Test Uncertainty Validation by Comparing Monte Carlo Analysis With Monovariate Perturbation Results

2006 ◽  
Author(s):  
Kerri L. Spencer ◽  
Jeffrey R. Friedman ◽  
Terry B. Sullivan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Test ◽  
Performance Testing ◽  
Monte Carlo Analysis ◽  
Combined Cycle ◽  
Plant Performance ◽  
The Monte Carlo Method ◽  
The Impact ◽  
Test Uncertainty

This paper focuses on the calculation of the test uncertainty of an ASME PTC 46 [1], overall plant performance test of a combined cycle by two separate methods. It compares the combined cycle corrected plant output and heat rate systematic uncertainty results that are generated using monovariate perturbation analysis with the Monte Carlo method. The Monte Carlo method has not been used widely in power plant performance testing applications. It offers insights into the results of the Monte Carlo analysis method, which is less intuitive than the conventional method. This study shows that utilizing two distinctly different methods of calculation of test uncertainty serves to corroborate assumptions, or to isolate flaws in one or both methods. In developing the method for calculation of test uncertainty, the authors conclude that it is prudent to validate the calculation method of choice of test uncertainty, and to consider the correlations in measurement uncertainties. Also discussed in detail are the impact of correlated uncertainty assumptions, and recommendations on their application. Correlated uncertainty has not been extensively discussed in the literature concerning specific applications in performance testing, although it should be a critical consideration in any uncertainty analysis. Details of determination of instrumentation uncertainty, measurement uncertainty of a parameter, and calculation of sensitivity factors are included in this paper.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

Application of the Monte Carlo Method for the Prediction of Behavior of Peptides

2019 ◽  
Vol 20 (12) ◽  
pp. 1151-1157 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Natural Sciences ◽  
Monte Carlo Technique ◽  
Quantitative Structure ◽  
Structure Property ◽  
The Monte Carlo Method ◽  
Modern Natural

Prediction of physicochemical and biochemical behavior of peptides is an important and attractive task of the modern natural sciences, since these substances have a key role in life processes. The Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers); (ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii) development of databases on the biopolymers. Current ideas related to application of the Monte Carlo technique for studying peptides and biopolymers have been discussed in this review.

Modeling grain growth by the monte carlo method on isotropic grids

1999 ◽  
Vol 72 (1) ◽  
pp. 68-72
Author(s):  
M. Yu. Al’es ◽  
A. I. Varnavskii ◽  
S. P. Kopysov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Grain Growth ◽  
The Monte Carlo Method
Sign in / Sign up

Export Citation Format

Share Document