scholarly journals Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259970
Author(s):  
Mohammadali Tofighi ◽  
Ali Asgary ◽  
Asad A. Merchant ◽  
Mohammad Ali Shafiee ◽  
Mahdi M. Najafabadi ◽  
...  
Keyword(s):  
Discrete Event ◽  
Intermittent Hemodialysis ◽  
Physical Interaction ◽  
Generation Process ◽  
Care Providers ◽  
Time Step ◽  
Dialysis Unit ◽  
Agent Based ◽  
Micro Scale ◽  
Contact Matrix

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

scholarly journals Modelling COVID - 19 Transmission in a Hemodialysis Centre Using Simulation Generated Contacts Matrices

2021 ◽  
Author(s):  
Mohammadali Tofighi ◽  
Ali Asgary ◽  
Asad A. Merchant ◽  
Mohammad Ali Shafiee ◽  
Mahdi M. Najafabadi ◽  
...  
Keyword(s):  
Discrete Event ◽  
Intermittent Hemodialysis ◽  
Physical Interaction ◽  
Generation Process ◽  
Care Providers ◽  
Time Step ◽  
Dialysis Unit ◽  
Agent Based ◽  
Micro Scale ◽  
Contact Matrix

AbstractThe COVID-19 pandemic has been particularly threatening to the patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

scholarly journals Principled simulation of agent-based models in epidemiology

2020 ◽  
Author(s):  
Sean L. Wu ◽  
Andrew J. Dolgert ◽  
Joseph A. Lewnard ◽  
John M. Marshall ◽  
David L. Smith
Keyword(s):  
Stochastic Simulation ◽  
Disease Transmission ◽  
Dynamic Models ◽  
Discrete Event ◽  
Model Complexity ◽  
Agent Based Models ◽  
Time Step ◽  
Hazard Functions ◽  
Population Mixing ◽  
Agent Based

AbstractAfter more than a century of sustained work by mathematicians, biologists, epidemiologists, probabilists, and other experts, dynamic models have become a vital tool for understanding and describing epidemics and disease transmission systems. Such models fulfill a variety of crucial roles including data integration, estimation of disease burden, forecasting trends, counterfactual evaluation, and parameter estimation. These models often incorporate myriad details, from age and social structure to inform population mixing patterns, commuting and migration, and immunological dynamics, among others. This complexity can be daunting, so many researchers have turned to stochastic simulation using agent-based models. Developing agent-based models, however, can present formidable technical challenges. In particular, depending on how the model updates state, unwanted or even unnoticed approximations can be introduced into a simulation model. In this article, we present computational methods for approximating continuous time discrete event stochastic processes based on a discrete time step to speed up complicated simulations which also converges to the true process as the time step goes to zero. Our stochastic models is constructed via hazard functions, and only those hazards which are dependent on the state of other agents (such as infection) are approximated, whereas hazards governing dynamics internal to an agent (such as immune response) are simulated exactly. By partitioning hazards as being either dependent or internal, a generic algorithm can be presented which is applicable to many models of contagion processes, with natural areas of extension and optimization.Author summaryStochastic simulation of epidemics is crucial to a variety of tasks in public health, encompassing intervention evaluation, trend forecasting, and estimation of epidemic parameters, among others. In many situations, due to model complexity, time constraints, unavailability or unfamiliarity with existing software, or other reasons, agent-based models are used to simulate epidemic processes. However, many simulation algorithms are ad hoc, which may introduce unwanted or unnoticed approximations. We present a method to build approximate, agent-based models from mathematical descriptions of stochastic epidemic processes which will improve simulation speed and converge to exact simulation techniques in limiting cases. The simplicity and generality of our method should be widely applicable to various problems in mathematical epidemiology and its connection to other methods developed in chemical physics should inspire future work and elaboration.

BIASED RANDOM WALKS, PARTIAL DIFFERENTIAL EQUATIONS AND UPDATE SCHEMES

2013 ◽  
Vol 55 (2) ◽  
pp. 93-108 ◽  
Author(s):  
JACK D. HYWOOD ◽  
KERRY A. LANDMAN
Keyword(s):  
Statistical Physics ◽  
Planck Equation ◽  
Step Length ◽  
Physical Object ◽  
Cell Diameter ◽  
Agent Based Models ◽  
Time Step ◽  
Partial Differential ◽  
Agent Based ◽  
Random Walkers

AbstractThere is much interest within the mathematical biology and statistical physics community in converting stochastic agent-based models for random walkers into a partial differential equation description for the average agent density. Here a collection of noninteracting biased random walkers on a one-dimensional lattice is considered. The usual master equation approach requires that two continuum limits, involving three parameters, namely step length, time step and the random walk bias, approach zero in a specific way. We are interested in the case where the two limits are not consistent. New results are obtained using a Fokker–Planck equation and the results are highly dependent on the simulation update schemes. The theoretical results are confirmed with examples. These findings provide insight into the importance of updating schemes to an accurate macroscopic description of stochastic local movement rules in agent-based models when the lattice spacing represents a physical object such as cell diameter.

scholarly journals Physical parametrisation of fire-spotting for operational fire spread models: response analysis with a model based on the Level Set Method

2018 ◽  
Author(s):  
Inderpreet Kaur ◽  
Anton Butenko ◽  
Gianni Pagnini
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Discrete Event ◽  
Fire Spread ◽  
Response Analysis ◽  
System Specification ◽  
Time Step ◽  
Processing Scheme ◽  
Model Based ◽  
The Many

Abstract. Fire-spotting is often responsible for a dangerous flare up in the wildfire and causes secondary ignitions isolated from the primary fire zone leading to perilous situations. In this paper a complete physical parametrisation of fire-spotting is presented within a formulation aimed to include random processes into operational fire spread models. This formulation can be implemented into existing operational models as a post-processing scheme at each time step, without calling for any major changes in the original framework. In particular, the efficacy of this formulation has already been shown for wildfire simulators based on an Eulerian moving interface method, namely the Level Set Method (LSM) that forms the baseline of the operational software WRF-SFIRE, and for wildfire simulators based on a Lagrangian front tracking technique, namely the Discrete Event System Specification (DEVS) that forms the baseline of the operational software FOREFIRE. The simple and computationally less expensive parametrisation includes the important parameters necessary for describing the landing behavior of the firebrands. The results from different simulations with a simple model based on the LSM highlight the response of the parametrisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands towards increasing the fire perimeter varies according to different concurrent conditions and the simulation results prove to be in agreement with the physical processes. Among the many rigorous approaches available in literature to model the firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational fire spread models.

Reducing Handoffs and Improving Patient Flow in the ED

2021 ◽  
Author(s):  
Vishnunarayan Girishan Prabhu ◽  
Kevin Taaffe ◽  
Ronald Pirrallo ◽  
William Jackson ◽  
Michael Ramsay
Keyword(s):  
Performance Metrics ◽  
Patient Flow ◽  
Discrete Event ◽  
Common Source ◽  
Agent Based ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Healthcare Settings ◽  
Shift Schedules ◽  
Hybrid Agent

Abstract Over 145 million people visit US Emergency Departments annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated healthcare settings. In particular, handoffs, the transfer of patient care from one physician to another during shift transition are a common source of errors resulting from workflow interruptions and high cognitive workload. This research focuses on developing a hybrid agent-based discrete event simulation model to identify physician shifts that minimize handoffs without affecting other performance metrics. By providing overlapping shift schedules as well as implementing policies that restrict physicians from signing up a new patient during the last hour of the shift, we observed that handoffs and patient time in the emergency department could be reduced by as much as 42% and 17%, respectively.

Multi-Methodology Approach

2021 ◽  
pp. 98-128
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Multiple Methods ◽  
Soft Systems ◽  
Agent Based ◽  
Event Simulation ◽  
Pluralistic Approach ◽  
The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

scholarly journals A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
Ali Asgary ◽  
Mahdi M. Najafabadi ◽  
Richard Karsseboom ◽  
Jianhong Wu
Keyword(s):  
Waiting Times ◽  
Discrete Event ◽  
Mass Vaccination ◽  
Simulation Tool ◽  
Agent Based ◽  
Public Health Agencies ◽  
The Past ◽  
Development Teams ◽  
Recovery Times ◽  
Effectiveness Assessment

Several research and development teams around the world are working towards COVID-19 vaccines. As vaccines are expected to be developed and produced, preparedness and planning for mass vaccination and immunization will become an important aspect of the pandemic management. Mass vaccination has been used by public health agencies in the past and is being proposed as a viable option for COVID-19 immunization. To be able to rapidly and safely immunize a large number of people against SARS-CoV-2, different mass vaccination options are available. Drive-through facilities have been successfully used in the past for immunization against other diseases and for testing during COVID-19. In this paper we introduce a drive-through vaccination simulation tool that can be used to enhance the planning, design, operation, and feasibility and effectiveness assessment of such facilities. The simulation tool is a hybrid model that integrates discrete event and agent-based modeling techniques. The simulation outputs visually and numerically show the average processing and waiting times and the number of cars and people that can be served (throughput values) under different numbers of staff, service lanes, screening, registration, immunization, and recovery times.

Simulation for Business Engineering of Electronic Markets

2011 ◽  
pp. 163-169
Author(s):  
Marijn Janssen ◽  
Henk G. Sol
Keyword(s):  
Information Systems ◽  
Discrete Event ◽  
Electronic Markets ◽  
Simulation Approach ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Market Structures ◽  
Information And Communication ◽  
Business Engineering ◽  
Insight Into

Developments in Information and Communication Technology (ICT) enable information systems to intermediate between sellers and buyers in electronic markets (e-markets). A business engineering methodology can be of help to design and develop e-markets by providing insight into current market and potential e-market structures, matching mechanisms and processes, and by evaluating the implications of e-markets. In this chapter, a first concept of an interactive, discrete-event, agent-based simulation approach for the analyses and design of e-markets is presented and evaluated.

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