EVALUATING THE ETHICAL AND SOCIETAL IMPACTS OF MODELLING PANDEMIC CRISES: EXPERIENCES FROM A WORKSHOP

This paper describes a hybrid (virtual and online) workshop held as part of the EU STAMINA project that aimed to engage project partners to explore ethics and simulation modelling in the context of pandemic preparedness and response. The purpose of the workshop was to consider how the model's design and use in specific pandemic decision-making contexts could have broader implications for issues like transparency, explainability, representativeness, bias, trust, equality, and social injustices. Its outputs will be used as evidence to produce a series of measures that could help mitigate ethical harms and support the greater possible benefit from the use of the models. These include recommendations for policy, data-gathering, training, potential protocols to support end-user engagement, as well as guidelines for designing and using simulation models for pandemic decision-making. This paper presents the methodological approaches taken when designing the workshop, practical concerns raised, initial insights gained, and considers future steps. © SW 2023.All rights reserved

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

Evaluating a Covid-19 Vaccine Centre in the UK Using a DES Model

Efficient management of a Covid-19 vaccine centre (VC) is necessary for proper-functioning of a mass vaccination programme. This study reports on an evaluation of the operational performance of a VC. There are two key considerations: the VC capacity (patients per hour) and the patient flow-time (total time patients spent in the centre). In this paper, Witness Horizon a simulation model tool that can be used to enhance the effectiveness of vaccination facilities is introduced. The model is developed using discrete event simulation. The model utilises animation whilst dynamically displaying key performance indicators. The uniqueness of this approach is the ability to simulate and analyse VC scenarios stochastically by varying hourly arrivals, walk-ins to drive-in ratios, staffing levels, registration, immunization, and observation capacities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Simulation Teaching During the Pandemic: Report of an Experience in a Higher Education Private Institution

The COVID-19 pandemic has impacted virtually every sector of our society, including the educational arena. This article reports the experience of discrete-event simulation teaching as part of the industrial engineering curriculum of a higher education private institution in three time instances: the pre-pandemic period (2019), where teaching was in person;the main pandemic period (2020 and the first half of 2021), where teaching was 100% remote, and the hybrid pandemic period (2nd half of 2021). We conducted comparisons of the teaching process along these instances regarding several points, by performing both qualitative and quantitative analyses. This article concludes that, despite some pedagogical difficulties, it was possible to maintain high quality in the teaching-learning process, compatible with the pre-pandemic period. The article also makes a forecast of how the teaching process of this type of discipline will be in the near future, after having been influenced by the pandemic period. © 2022 IEEE.

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.

Regional Maximum Hospital Capacity Estimation for COVID-19 Pandemic Patient Care in Surge Through Simulation

Estimating the capacity of a region to serve pandemic patients in need of hospital services is crucial to regional preparedness for pandemic surge conditions. This paper explores the use of techniques of stochastic discrete event simulation for estimating the maximum number of pandemic patients with intensive care and/or in-patient, isolation requirements that can be served by a consortium of hospitals in a region before requesting external resources. Estimates from the model provide an upper bound on the number of patients that can be treated if all hospital resources are re-allocated for pandemic care. The modeling approach is demonstrated on a system of five hospitals each replicating basic elements (e.g. number of beds) of the five hospitals in the Johns Hopkins Hospital System in the Baltimore-Washington, D.C. Metropolitan area under settings relevant to the COVID-19 pandemic. © 2022 IEEE.

FACS-CHARM: A Hybrid Agent-Based and Discrete-Event Simulation Approach for Covid-19 Management at Regional Level

Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decision-makers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service. © 2022 IEEE.

Combining simulation and machine learning for the management of healthcare systems

The growing research trends in the field of artificial intelligence have largely impacted the healthcare sector. Thanks to the high predictive power of machine learning approaches, new tools to support the clinical decision-making can be designed. However, since the demand for healthcare services is complex and highly changing, as it is affected by external unpredictable factors such as the CoViD-19, the reliability and robustness of such predictive tools is highly dependent on their capability of varying and adapting the forecasting in accordance with variations in environmental factors and health needs. In this work, we propose a combined simulation and machine learning approach to study the robustness and adaptability of predictive tools for healthcare management. Discrete event simulation is employed to simulate a generic healthcare service. The patients' length of stay (LOS) is monitored as a performance indicator of the care process. Three machine learning algorithms have been tested to predict the LOS in different simulated scenarios obtained by varying the level of demand for the healthcare service. The predictability of the tested algorithms has been studied in terms of mean errors. Preliminary results suggest that abrupt changes in the healthcare demand have a negative impact on the performance of the machine learning algorithms, which are not prone to adapt decisions to the surrounding environment. The design of novel intelligent health system, which aim to integrate artificial intelligence tools in the clinical decision-making process, should take into account these limitations. In this sense the use of simulation can be beneficial in the assessment of the new generation of decision support systems in healthcare. © 2022 IEEE.

The Impact of the Constraints of Class Scheduling on Campus Dining: A Simulation-based Case Study

Waiting in queues in service systems is an inevitable part of the customer's everyday routine. Waiting time is an important indicator of a service system's performance. This paper studies the efficiency of service operations in a college campus dining setting. The authors implemented a discrete event simulation (DES) model in Simio to study how class scheduling may affect the overall customer waiting time and satisfaction at the college campus dining location. The results provide recommendations on how classes could be scheduled to optimize students' satisfaction with their lunchtimes and the quality of service. The results also provide valuable insights for operating during the COVID-19 pandemic, as campus dining locations have a decreased maximum capacity, which may lead to more bottlenecks than usual and increase waiting times. © 2021 The Authors.

Discrete event simulation of a drive-through COVID-19 mass vaccination model: senior population prioritization strategies

The COVID-19 pandemic has disrupted the normal operations of countries around the world, which applied different containment and mitigation policies, such as mask-wearing, social distancing, quarantine, and lockdowns, to limit the spread of the virus. More recent mitigation efforts include vaccination strategies, since various vaccines have been authorized for emergency use for the prevention of COVID-19. In fact, vaccination is one of the best proactive mitigation strategies against the virus spread. Mass vaccination strategies have been undertaken by multiple research and development teams in the past when the public needed to be vaccinated on a large scale due to a pandemic, such as the seasonal flu or H1N1. Drive through vaccination, in particular, is more convenient and safer than walk-in vaccinations in clinics due the nature of the contagious virus. In this paper, we present the implementation of a discrete event simulation model of a drive through clinic for mass vaccinations of patients, while prioritizing the senior population. The simulation output is examined in terms of average waiting time in the queue to get vaccinated, number of patients getting vaccinated per week, and utilization of the medical resources. The results are expected to provide insights into the allocation of medical resources across lanes and prioritization strategies for the senior population to achieve higher vaccination rates, while reducing the waiting time in queue. © 2021 The Authors.

The Expected Value of Perfect Information of COVID-19 Treatment Using Discrete Event Simulation

The Value of Perfect Information (EVPI) and also Sample Information (EVSI) are necessary for calculating the expected economic benefit of a research based on evidence about the cost and efficacy of novel therapies. The EVPI determines the maximum value resulting from soliciting data to decrease the uncertainties and the expected loss in case of providing ineffective treatment. In general, an inefficient decision will waste health resources that may be better spent elsewhere, thereby deteriorating health outcomes. In this article, the value of information resulting from reducing uncertainty will be applied in assessing two COVID-19 treatments, namely, the standard care and vaccines. A discrete event simulation model is introduced to expand the usage of EVPI calculations to medical applications with various sources of uncertainty as the case of COVID-19. Our simulation results show that further testing and vaccine validation will be of insignificant value if the response rate on vaccine is higher than 85%. The purpose of this study is to provide a step-by-step guide to the computation of the value pre-testing in the context of healthcare decision-making. Worked scenarios were presented for COVID-19 in UAE. The study can serve as a useful template for various decision-making problems in medical settings. © 2022 The Authors.

Simulation modeling assessment and improvement of a COVID-19 mass vaccination center operations

The development of safe and effective vaccines against COVID-19 has been a turning point in the international effort to control this disease. However, vaccine development is only the first phase of the COVID-19 vaccination process. Correct planning of mass vaccination is important for any policy to immunize the population. For this purpose, it is necessary to set up and properly manage mass vaccination centers. This paper presents a discrete event simulation model of a real COVID-19 mass vaccination center located in Sfax, Tunisia. This model was used to evaluate the management of this center through different performance measures. Three person’s arrival scenarios were considered and simulated to verify the response of this real vaccination center to arrival variability. A second model was proposed and simulated to improve the performances of the vaccination center. Like the first model, this one underwent the same evaluation process through the three arrivals scenarios. The simulation results show that both models respond well to the arrival’s variability. Indeed, most of the arriving persons are vaccinated on time for all the studied scenarios. In addition, both models present moderate average vaccination and waiting times. However, the average utilization rates of operators are modest and need to be improved. Furthermore, both simulation models show a high average number of persons present in the vaccination center, which goes against the respect of the social distancing condition. Comparison between the two simulation models shows that the proposed model is more efficient than the actual one. © The Author(s) 2022.

A Simulation of Patient Queuing System on MRI System at Tertiary Referral Hospital in Indonesia

Radiology department at a tertiary referral hospital faces service operation challenges such as huge and various patient arrival, which can increase the probability of patient queuing. During COVID-19 pandemic, it is mandatory to apply social distancing protocol in the radiology department. A strategy to prevent accumulation of patients at one spot would be required. The aim of this study is to identify an alternative solution which can reduce the patient's waiting time in MRI services. Discrete event simulation (DES) is used for this study by constructing several improvement scenarios with Arenao simulation software. Statistical analysis is used to test the validity of base case scenario model, and to investigate performance of the improvement scenarios. The result of this study shows that the selected scenario is able to reduce 83.6% of patient's length of stay, which lead into a more efficient MRI services in radiology department, be able to serve patients more effectively, and thus increase the patient satisfaction. The result of the simulation can be used by the hospital management to improve the operational performance of the radiology department. © 2021 IEEE.

Gate Assigment and Fleet Sizing of Apron Buses including social distancing metrics in an international airport using discrete event simulation

The COVID - 19 pandemic has impacted the aeronautical industry demanding new conditions in the transfer of passengers on air flights. These new conditions involve ensuring that passengers can maintain an adequate distance to minimize the chances of contagion. This implies that airports can resize their resources to meet these conditions. It is critical to be able to keep social distancing in the passenger boarding and unboarding processes, then the apron buses and gates used in these processes becomes critical. The bus fleet sizing needs to be calculated considering passengers needs to keep social distancing in their travel from and to the aircrafts. In a similar way gates need to be assigned considering a reduction in the use of neighboring doors to reduce the number of passengers in the same waiting room. The complexity of this problem lies in being able to incorporate the social distancing as an action parameter to obtain adequate solutions in the context of sizing the apron bus fleet and assigning doors. The contagion risk becomes an indicator related to social distancing in the position and movements made in the apron buses. So, it is proposed to incorporate these conditions in a simulation model that allows obtaining the estimation of the apron bus fleet and the allocation of gates as a result while showing the contagion risk as a decision parameter. © 2022 IEEE.

Application of Data Envelopment Analysis for pharmaceutical export decision-making during the COVID-19 pandemic

Decision-making in complex systems is undoubtedly quite difficult, mostly under exceptional circumstances. Indeed, in the context of international market selection, the COVID-19 pandemic has made pharmaceutical export decisions more complex. Several scientific approaches are used by researchers as well as practitioners to guide in this area. In particular, Operations Research techniques, including linear programming, discrete event simulation and queuing theory, are called by organizational leaders to make highquality decisions. This study presents a Benchmarking methodology to support the decision-making process for international market selection based on the Data Envelopment Analysis method. A computational numerical study was conducted to highlight the performance of the proposed approach. © 2022 IEEE.

Simulation-based cost optimisation of a risk mitigation strategy in an assembly job manufacturing process: a case study from the offshore wind industry

Despite holding very high expectations regarding installed capacity and planned investments, offshore wind energy is currently facing important challenges to align itself with the levelised cost of energy of other renewable energies such as solar power or onshore wind. In this context, we aim at putting DES optimisation at offshore wind’s disposal and leverage its advantages proved in other areas. To accomplish so, we have performed a DES-based optimisation of the routing strategy and the net income of an offshore wind foundations manufacturing project affected by very important delays due to covid-19 impact and material supply issues. The problem applies to a constraint-based multi-level assembly job shop where we use dispatching rules to model the routing decision. Overall, we have provided the company with an optimised schedule, due dates, expected penalties, expected net income and a detailed ongoing DES model to be used in further stages of the project. Copyright © 2021 Inderscience Enterprises Ltd.

A Tutorial on Participative Discrete Event Simulation in the Virtual Workshop Environment

Facilitated discrete event simulation offers an alternative mode of engagement with stakeholders (clients) in simulation projects. Pre-covid19 this was undertaken in face-to-face workshops but the new reality has meant that this is no longer possible for many of us around the globe. This tutorial explores PartiSim, short for Participative Simulation, as adapted to fit the new reality of holding virtual workshops with stakeholders. PartiSim is a participative and facilitated modelling approach developed to support simulation projects through a framework, stakeholder-oriented tools and manuals in facilitated workshops. We describe a typical PartiSim study consisting of six stages, four of which involve facilitated workshops and how it can be undertaken in a virtual workshop environment. We have developed games to provide those attending the tutorial with the experience of virtual facilitation. © 2021 IEEE.

Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic

Hospitals and health-care institutions need to plan the resources required for handling the increased load, i.e., beds and ventilators during the COVID-19 pandemic. BaBSim.Hospital, an open-source tool for capacity planning based on discrete event simulation, was developed over the last year to support doctors, administrations, health authorities, and crisis teams in Germany. To obtain reliable results, 29 simulation parameters such as durations and probabilities must be specified. While reasonable default values were obtained in detailed discussions with medical professionals, the parameters have to be regularly and automatically optimized based on current data. We investigate how a set of parameters that is tailored to the German health system can be transferred to other regions. Therefore, we use data from the UK. Our study demonstrates the flexibility of the discrete event simulation approach. However, transferring the optimal German parameter settings to the UK situation does not work-parameter ranges must be modified. The adaptation has been shown to reduce simulation error by nearly 70%. The simulation-via-optimization approach is not restricted to health-care institutions, it is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods. © 2021 European Union

Evaluation of simulation modeling of university dining hall COVID-19 reopening plans

The COVID-19 pandemic is reshaping and complicating the world. Nowhere has it been more controversial and complex than in reopening plans for schools. Observing student behavior indicates that the dining hall services are a major area of concern in reopening plans. Careful consideration and focus need to be taken into account for dealing with high demands in short timeframes experienced at dining halls. The removal of masks while eating increases the probability of spreading germs between individuals, forming a potential hotspot for spreading if a breakout were to occur. The dining halls are a complex system, in which modeling student behavior becomes critical conditions for determining results. Using Simio, a simulation and modeling software, three dining hall models were created and analyzed to determine the optimal number of people that should be allowed into the system where COVID-19 protocols could be followed but did not cause workstations to be idle. Parameters for the simulations were created from student conducted time studies. Simulation provided the ability to effectively compare alternative models with different conditions. Cycle time as well as queuing time and length were calculated for each model, which indicated the effectiveness of the system on meeting demand. The instructional models were compared to the baseline model to validate the dining hall COVID-19 reopening policies. The analysis proved guidelines for the dining halls would help limit the number of close contacts and get students through the system quickly;overall helping campus dining services serve students safely and quickly. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.