A Unified Statistical Approach for Simulation, Modeling, Analysis and Mapping of Environmental Data

SIMULATION ◽  
2009 ◽  
Vol 86 (3) ◽  
pp. 139-153 ◽  
Author(s):  
Alessandro Fassò ◽  
Michela Cameletti
Keyword(s):  
Simulation Modeling ◽  
Statistical Approach ◽  
Environmental Data ◽  
Modeling Analysis

Simulation Modeling of Healthcare Delivery

2011 ◽  
pp. 69-89
Author(s):  
Ian W. Gibson
Keyword(s):  
Simulation Modeling ◽  
Healthcare Delivery ◽  
Healthcare Services ◽  
Healthcare Delivery System ◽  
Scarce Resources ◽  
Power Simulation ◽  
Modeling Analysis ◽  
Management Techniques ◽  
The Cost ◽  
Delivery Models

Healthcare has delivered incredible improvements in diagnosis and treatment of diseases but faces challenges to improve the delivery of services. Healthcare is a complex system using expensive and scarce resources. Benchmarking, experience, and lean management techniques currently provide the basis for developing service delivery models and facility planning. Simulation modeling can supplement these methods to enable a better understanding of the complex systems involved. This provides the basis for developing and evaluating options to provide improved healthcare delivery. Simulation modeling enables a better understanding of the processes and the resources used in delivering healthcare services and improving healthcare delivery systems. Options to improve the cost effectiveness can be evaluated without experimenting with patients. This chapter reviews the current challenges and methods including the use of simulation modeling. Analysis of emergency patient flows through a major hospital shows the capability of simulation modeling to enable improvement of the healthcare delivery system. This chapter enables healthcare managers to understand the power simulation modeling brings to the improvement of healthcare delivery.

scholarly journals Delineating flood prone areas using a statistical approach

2016 ◽  
Author(s):  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Paola Salvati ◽  
Marco Donnini ◽  
Simone Sterlacchini ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Learning Algorithm ◽  
Flood Hazard ◽  
Environmental Data ◽  
Training Data ◽  
Hazard Maps ◽  
Elevation Data ◽  
Terrain Elevation ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

scholarly journals Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs

2021 ◽  
Vol 11 ◽  
Author(s):  
Prathiba Natesan Batley ◽  
Ratna Nandakumar ◽  
Jayme M. Palka ◽  
Pragya Shrestha
Keyword(s):  
Bayesian Methods ◽  
Change Point ◽  
Simulation Modeling ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Point Model ◽  
Main Challenge ◽  
Modeling Analysis ◽  
Change Point Model

Recently, there has been an increased interest in developing statistical methodologies for analyzing single case experimental design (SCED) data to supplement visual analysis. Some of these are simulation-driven such as Bayesian methods because Bayesian methods can compensate for small sample sizes, which is a main challenge of SCEDs. Two simulation-driven approaches: Bayesian unknown change-point model (BUCP) and simulation modeling analysis (SMA) were compared in the present study for three real datasets that exhibit “clear” immediacy, “unclear” immediacy, and delayed effects. Although SMA estimates can be used to answer some aspects of functional relationship between the independent and the outcome variables, they cannot address immediacy or provide an effect size estimate that considers autocorrelation as required by the What Works Clearinghouse (WWC) Standards. BUCP overcomes these drawbacks of SMA. In final analysis, it is recommended that both visual and statistical analyses be conducted for a thorough analysis of SCEDs.

Simulation modeling analysis of sequential relations among therapeutic alliance, symptoms, and adherence to child-centered play therapy between a child with autism spectrum disorder and two therapists

2017 ◽  
Vol 22 (3) ◽  
pp. 455-466 ◽  
Author(s):  
Geoff Goodman ◽  
Hyewon Chung ◽  
Leah Fischel ◽  
Laura Athey-Lloyd
Keyword(s):  
Autism Spectrum Disorder ◽  
Therapeutic Alliance ◽  
Play Therapy ◽  
Simulation Modeling ◽  
Child Psychotherapy ◽  
Autism Spectrum ◽  
Autism Symptoms ◽  
Modeling Analysis ◽  
Child Centered ◽  
Child Centered Play Therapy

This study examined the sequential relations among three pertinent variables in child psychotherapy: therapeutic alliance (TA) (including ruptures and repairs), autism symptoms, and adherence to child-centered play therapy (CCPT) process. A 2-year CCPT of a 6-year-old Caucasian boy diagnosed with autism spectrum disorder was conducted weekly with two doctoral-student therapists, working consecutively for 1 year each, in a university-based community mental-health clinic. Sessions were video-recorded and coded using the Child Psychotherapy Process Q-Set (CPQ), a measure of the TA, and an autism symptom measure. Sequential relations among these variables were examined using simulation modeling analysis (SMA). In Therapist 1’s treatment, unexpectedly, autism symptoms decreased three sessions after a rupture occurred in the therapeutic dyad. In Therapist 2’s treatment, adherence to CCPT process increased 2 weeks after a repair occurred in the therapeutic dyad. The TA decreased 1 week after autism symptoms increased. Finally, adherence to CCPT process decreased 1 week after autism symptoms increased. The authors concluded that (1) sequential relations differ by therapist even though the child remains constant, (2) therapeutic ruptures can have an unexpected effect on autism symptoms, and (3) changes in autism symptoms can precede as well as follow changes in process variables.

scholarly journals Delineating flood prone areas using a statistical approach

2016 ◽  
Author(s):  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Paola Salvati ◽  
Marco Donnini ◽  
Simone Sterlacchini ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Learning Algorithm ◽  
Flood Hazard ◽  
Environmental Data ◽  
Training Data ◽  
Hazard Maps ◽  
Elevation Data ◽  
Terrain Elevation ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

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