scholarly journals Collaborative critical care prediction and resource planning during the COVID-19 pandemic using computer simulation modelling: future urgent planning lessons

2021 ◽  
Vol 8 (2) ◽  
pp. e317-e321
Author(s):  
Nicola Irvine ◽  
Gillian Anderson ◽  
Chandrava Sinha ◽  
Holly McCabe ◽  
Robert Van der Meer
Keyword(s):  
Computer Simulation ◽  
Critical Care ◽  
Resource Planning ◽  
Simulation Modelling

scholarly journals Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Joe Viana ◽  
Tone Breines Simonsen ◽  
Hildegunn E. Faraas ◽  
Nina Schmidt ◽  
Fredrik A. Dahl ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Patient Flow ◽  
Simulation Modelling ◽  
Outpatient Clinics ◽  
Term Pregnancy ◽  
Post Term Pregnancy ◽  
Modelling Study

VORTEX: a computer simulation model for population viability analysis

1993 ◽  
Vol 20 (1) ◽  
pp. 45 ◽  
Author(s):  
RC Lacy
Keyword(s):  
Computer Simulation ◽  
Population Growth ◽  
Simulation Model ◽  
Density Dependence ◽  
Carrying Capacity ◽  
Population Viability Analysis ◽  
Simulation Modelling ◽  
Population Viability ◽  
Reproductive Age ◽  
Viability Analysis

Population Viability Analysis (PVA) is the estimation of extinction probabilities by analyses that incorporate identifiable threats to population survival into models of the extinction process. Extrinsic forces, such as habitat loss, over-harvesting, and competition or predation by introduced species, often lead to population decline. Although the traditional methods of wildlife ecology can reveal such deterministic trends, random fluctuations that increase as populations become smaller can lead to extinction even of populations that have, on average, positive population growth when below carrying capacity. Computer simulation modelling provides a tool for exploring the viability of populations subjected to many complex, interacting deterministic and random processes. One such simulation model, VORTEX, has been used extensively by the Captive Breeding Specialist Group (Species Survival Commission, IUCN), by wildlife agencies, and by university classes. The algorithms, structure, assumptions and applications of VORTEX are described in this paper. VORTEX models population processes as discrete, sequential events, with probabilistic outcomes. VORTEX simulates birth and death processes and the transmission of genes through the generations by generating random numbers to determine whether each animal lives or dies, to determine the number of progeny produced by each female each year, and to determine which of the two alleles at a genetic locus are transmitted from each parent to each offspring. Fecundity is assumed to be independent of age after an animal reaches reproductive age. Mortality rates are specified for each pre-reproductive age-sex class and for reproductive-age animals. Inbreeding depression is modelled as a decrease in viability in inbred animals. The user has the option of modelling density dependence in reproductive rates. As a simple model of density dependence in survival, a carrying capacity is imposed by a probabilistic truncation of each age class if the population size exceeds the specified carrying capacity. VORTEX can model linear trends in the carrying capacity. VORTEX models environmental variation by sampling birth rates, death rates, and the carrying capacity from binomial or normal distributions. Catastrophes are modelled as sporadic random events that reduce survival and reproduction for one year. VORTEX also allows the user to supplement or harvest the population, and multiple subpopulations can be tracked, with user-specified migration among the units. VORTEX outputs summary statistics on population growth rates, the probability of population extinction, the time to extinction, and the mean size and genetic variation in extant populations. VORTEX necessarily makes many assumptions. The model it incorporates is most applicable to species with low fecundity and long lifespans, such as mammals, birds and reptiles. It integrates the interacting effects of many of the deterministic and stochastic processes that have an impact on the viability of small populations, providing opportunity for more complete analysis than is possible by other techniques. PVA by simulation modelling is an important tool for identifying populations at risk of extinction, determining the urgency of action, and evaluating options for management.

Computer simulation modelling and visualization of 3D architecture of biological tissues

1995 ◽  
Vol 43 (4) ◽  
pp. 425-442 ◽  
Author(s):  
Carole J. Clem ◽  
Jean Paul Rigaut
Keyword(s):  
Computer Simulation ◽  
Simulation Modelling ◽  
Biological Tissues ◽  
3D Architecture

Reducing pharmacy patient waiting time

2018 ◽  
Vol 31 (7) ◽  
pp. 834-844
Author(s):  
Shoaib Alam ◽  
Muhammad Osama ◽  
Faheem Iqbal ◽  
Irfan Sawar
Keyword(s):  
Computer Simulation ◽  
Customer Satisfaction ◽  
Waiting Time ◽  
Six Sigma ◽  
Simulation Modelling ◽  
System Modelling ◽  
Evidence Based ◽  
Content Type ◽  
Patient Waiting Time ◽  
Patient Waiting

Purpose Pharmacy services start right from prescribing medicines and continue as the medication’s effect is monitored. Hospital and community pharmacy staff promote rational prescribing and medicine use. Consequentially, pharmacy is a complex and busy field. Often there are peak workload hours when patients must wait, which is associated with patient dissatisfaction that may negatively affect patient experience and the organisation’s reputation. The purpose of this paper is to enlist techniques, methods and technological advancements that have been successfully employed to reduce patient waiting time. Design/methodology/approach A database search was conducted in 2017 to locate articles addressing methods and technologies that reduce pharmacy waiting time. The literature revealed various techniques and technologies like queuing theory, tele-pharmacy, evidence-based pharmacy design, automated pharmacy systems (robotics), system modelling and simulation and the Six Sigma method for identifying potential problems associated with increased wait time. Findings The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation. Practical implications The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma methodology can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation. Originality/value The authors carried out a literature search and identified the techniques that have been successfully implemented to reduce pharmacy patient waiting time and methods that can identify potential process behind medication dispensation delays.

Computer Simulation Modelling for the Prevention of Diabetes Mellitus (Prameha)

2011 ◽  
Vol 11 (15) ◽  
pp. 2670-2679 ◽  
Author(s):  
Mehmood G. Sayyad ◽  
Arjan K. Shahani ◽  
Sanju Pal ◽  
Jyoti A. Shirodkar ◽  
G. Gopal
Keyword(s):  
Diabetes Mellitus ◽  
Computer Simulation ◽  
Simulation Modelling ◽  
Prevention Of Diabetes
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