Model-based Simulation Research Articles

Machine Learning in Data Analysis and Visualization in Healthcare

Data visualization becomes an important tool for effective processing and analysis of large databases during epidemics, helping researchers, public health experts, and policymakers quickly identify trends, patterns, and anomalies by translating complex data into easy-to-understand graphs and charts. This study examines the application of Python and machine learning techniques to the analysis of outbreak data, specifically the methods used to process and analyze data during the COVID-19 outbreak. This paper concludes that data cleaning and feature extraction are important to ensure the accuracy and consistency of data. Furthermore, appropriate for the prediction of epidemic trends is the ARIMA model. Random Forest may therefore be used for case categorization as well as for SEIR model-based epidemic simulation. The huge possibilities of machine learning in enhancing the science and efficiency of public health decision-making are exposed in this work. At last, for the next pyhton applications, model interpretability, and data privacy protection become increasingly critical. This work offers a useful guide for further investigation and optimization of machine learning use in healthcare.

Exploration of the potential impact of batch-to-batch variability on the establishment of pharmacokinetic bioequivalence for inhalation powder drug products.

Batch-to-batch variability in inhalation powder has been identified as a potential challenge in the development of generic versions. This study explored the impact of batch-to-batch variability on the probability of establishing pharmacokinetic (PK) bioequivalence (BE) in a two-sequence, two-period (2 × 2) crossover study. A model-based parametric simulation approach was employed, incorporating batch-to-batch variability through the relative bioavailability (RBA) ratio. In the absence of batch variability, recruiting a total of 48 subjects in a 2 × 2 crossover study with the reference formulation resulted in a 95% probability of concluding BE. However, this probability decreased to 80% with a 5% batch difference in RBA and further declined to 30% with a 10% batch difference. With a 10% batch difference, the required number of subjects to achieve an 80% probability of concluding BE increased to 84. When considering product differences between the reference and the test formulations, an additional 10% batch difference reduced the study power from 97% to 30% for a T/R bioavailability ratio of 100% in a 2 × 2 crossover study with 48 subjects. As a result, the substantial impact of batch-to-batch variability on the study power and type I error of the PK BE study may pose significant challenges for the development of generic Advair Diskus due to its degree of PK batch-to-batch variability. Therefore, alternative PK BE study designs and guidelines are needed to adequately address the influence of batch-to-batch variability in products like Advair Diskus.

Population pharmacokinetic analyses for sulbactam-durlobactam using Phase 1, 2, and 3 data.

Sulbactam-durlobactam is a β-lactam/β-lactamase inhibitor combination approved in the United States for the treatment of hospital-acquired and ventilator-associated bacterial pneumonia caused by susceptible isolates of Acinetobacter baumannii-calcoaceticus in adults. A population pharmacokinetic (PK) model of sulbactam-durlobactam in plasma was developed using data from eight Phase 1-3 studies. A total of 432 subjects and 8,100 plasma concentrations were available for the population PK data set. The combined model was a four-compartment (two compartments per drug) model with linear kinetics. Both renal clearance and nonrenal clearance were estimated, and total clearance was calculated as the sum of renal and nonrenal clearance. Individual renal clearances were scaled by baseline creatinine clearance. The sampling-importance-resampling analysis indicated that the parameters were estimated reliably with adequate precision. Hemodialysis (HD) and epithelial lining fluid (ELF) sub-models were developed for each analyte separately. Intermittent HD resulted in an approximately 30% decrease in the daily area under the concentration-time curve (AUC0-24) when HD was started 1 hour after the end of the infusion. Assuming protein binding estimates of 10% and 38% for durlobactam and sulbactam, respectively, ELF penetration ratios were found to be 41.3% for durlobactam and 86.0% for sulbactam. Of the statistically significant covariates of PK identified, which included body weight, body mass index, infection type, and region of origin, renal function was the only clinically relevant covariate. Overall, a robust description of the plasma PK of sulbactam and durlobactam was achieved. The resultant population PK model was expected to be appropriate for model-based simulations and assessment of pharmacokinetic-pharmacodynamic relationships.

Optimization of Traffic at Uncontrolled Intersections: Comparison of the Effectiveness of Roundabouts, Signal-Controlled Intersections, and Turbo-Roundabouts

This study focuses on optimizing traffic flow at uncontrolled intersections by comparing the effectiveness of different intersection types: roundabouts, signal-controlled intersections, and turbo-roundabouts. The purpose is to determine which type offers the best solution for enhancing traffic efficiency, reducing delays, and improving safety. The research employs simulation-based modeling to analyze traffic performance under varying traffic conditions. Critical parameters such as vehicle flow rate, average delay time, and capacity are used to assess the performance of each intersection type. The results indicate that turbo-roundabouts outperform conventional roundabouts and signal-controlled intersections in terms of both capacity and reduction in delays. The findings suggest that implementing turbo-roundabouts at high-traffic intersections can significantly improve traffic flow and reduce congestion. However, the effectiveness of each solution is context-dependent, with signal-controlled intersections still being advantageous under specific conditions, particularly in highly urbanized areas. This study provides valuable insights for transportation planners and engineers, highlighting the importance of intersection design in traffic optimization.

Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments

Addressing climate change issues is one of the most important tasks within the United Nations Sustainable Development Goals. Accurate and efficient simulation of wind fields within cities is essential for climate adaptation. Traditional simplified geometric model-based wind flow simulation can lead to significant errors, affecting the ability to develop effective urban climate strategies. This study addresses this limitation by introducing a novel workflow that leverages drone photogrammetry, deep learning, and geometric complexity quantification to create highly detailed 3D models of in-use building clusters within cities. These models are subsequently used for computational fluid dynamics simulations to accurately predict urban wind fields. The proposed method was validated on three real-world building clusters. Compared to traditional footprint extrusion models, the proposed method demonstrates an average error reduction of 29.2% in large eddy simulation cases and 17.6% in steady Reynolds-averaged Navier-Stokes equations cases. Meanwhile, the proposed model improved computational efficiency by an average of 33.7% in large eddy simulations compared to the flashy oblique photography model. The proposed method provides a balanced model of accuracy and efficiency for urban flow simulations. It has the potential to be incorporated into computational fluid dynamics best practice guidelines, thereby promoting the development of climate-resilient cities.

Population pharmacokinetics of rilpivirine following oral administration and long-acting intramuscular injection in real-world people with HIV.

The pharmacokinetics of long-acting rilpivirine has mostly been studied in clinical trials, which do not fully address the uncertainties that arise in routine clinical situations. Our population analysis aims to establish percentile curves for rilpivirine concentrations in people with HIV (PWH) followed-up in a routine clinical setting, while identifying patient-related factors that may influence rilpivirine exposure. A total of 238 PWH enrolled in our nationwide multicenter observational study contributed to 1038 concentrations (186 and 852 concentrations after oral and intramuscular injection, respectively). Rilpivirine pharmacokinetics were best described by a two-compartment model with an oral to intramuscular relative bioavailability factor. A simple zero-order absorption process was retained for oral administration while a parallel first-order absorption was used for intramuscular administration, with 27.6% of the dose released via a fast absorption pathway and the remaining fraction via a slow absorption pathway. Our model estimated that long-acting rilpivirine reaches steady-state after 2.5years and has an elimination half-life of 18weeks, consistent with published estimates. In females, a 45.6% reduction in the proportion of the dose absorbed via the rapid absorption pathway was observed. However, this resulted in no more than 15% difference in trough concentrations (Ctrough) compared to males, which was not considered to be clinically relevant. Overall, our model-based simulations showed that only approximately 50% of long-acting rilpivirine Ctrough would be above the 50ng/mL threshold associated with optimal therapeutic response, while approximately 85% of Ctrough would be above the first quartile of concentrations observed in Phase III trials (32ng/mL).

The distinctive pharmacokinetic profile of rezafungin, a long-acting echinocandin developed in the era of modern pharmacometrics.

Echinocandin drugs are the current first-line therapy for fungal infections caused by Candida spp. Most patients require once-daily intravenous (IV) administration in a hospital or outpatient setting for treatment, which may negatively impact their quality of life and stress healthcare resources. Similar to other echinocandins, the novel FDA-, EMA-, and Medical and Healthcare Products Regulatory Agency-approved echinocandin, rezafungin (CD101), exhibited strong antifungal activity against several fungal pathogens and a low drug-drug interaction liability, which are important for medically complex patients. A pharmacometric-based approach has been adopted throughout the development of rezafungin, which contrasts with older echinocandins where dosing regimens were largely derived empirically, and only recently based on pharmacometric guidance. This state-of-the-art approach used model-based simulations incorporating pre-clinical and clinical data as it became available to optimize the dosing regimen for rezafungin. The enhanced stability of the molecular structure and the safety profile of rezafungin allow for the administration of once-weekly IV doses, compared to the daily dosing requirement for other echinocandin drugs, with this distinctive pharmacokinetic profile of rezafungin resulting in a front-loaded dosing regimen with high exposures early in therapy for enhanced fungal killing. The long shelf-life of rezafungin makes this echinocandin more flexible in terms of storage and manufacturing. Demonstrated across clinical development, rezafungin may provide patients with next-generation first-line antifungal treatment for the treatment of candidaemia and invasive candidiasis.

Pharmacometric Analysis to Describe Pharmacokinetics and Exposure-Efficacy Response of Ivermectin in Adolescents Infected with Trichuris trichiura.

The efficacy of the combination therapy of albendazole and ivermectin against Trichuris trichiura infection is higher in Tanzania than in Côte d'Ivoire. This study therefore aimed to investigate the difference between the population pharmacokinetics (PK) at these study sites and to determine if an exposure-response analysis could explain the low efficacy of the combination therapy in Côte d'Ivoire. Twenty-four participants (aged 12-19 years) receiving single doses of ivermectin (200 µg/kg) and albendazole (400 mg) were included in the population PK modeling. A regression analysis was performed to investigate the relationship between the reduction of fecal whipworm eggs and different exposure metrics (peak concentration, area under the plasma drug concentration-time curve [AUC], and time above a certain threshold). The PK profile of ivermectin was best described by a one-compartment model, first-order absorption, and no delay in absorption, with the absorption rate constant estimated as 0.26 per h, an apparent volume of distribution of 162.43 L, and an apparent clearance of 7.82 L/h. In Tanzania, all patients showed a very high reduction in egg count independent of exposure. In Côte d'Ivoire, a relationship was found between higher ivermectin exposure and egg reduction, although not statistically significant. There was no significant difference between the PK profiles at both study sites, despite a difference in clinical outcome. Model-based simulations indicate that higher ivermectin doses such as 400 and 600 µg/kg may be associated with reduced egg count. Larger clinical studies are warranted to explore further the exposure-efficacy response relationship at 200 µg/kg and higher ivermectin doses in adults and children.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Longitudinal and time-to-event modeling for the survival of advanced pancreatic ductal adenocarcinoma patients.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers especially at advanced stage. In order to analyze the dynamics of potential prognostic biomarkers and further quantify their relationships with the overall survival (OS) of advanced PDAC patients, we herein developed a parametric time-to-event (TTE) model integrated with longitudinal submodels. Data from 104 patients receiving standard chemotherapies were retrospectively collected for model development, and other 54 patients were enrolled as external validation. The longitudinal submodels were developed with the time-course data of sum of longest diameters (SLD) of tumors, serum albumin (ALB) and body weight (BW) using nonlinear mixed effect models. The model-derived metrics including model parameters and individual predictions at different time points were further analyzed in the TTE model, together with other baseline information of patients. A linear growth-exponential shrinkage model was employed to describe the dynamics of SLD, while logistic models were used to fit the relationship of time prior to death with ALB and BW. The TTE model estimated the ALB and BW changes at the 9th week after chemotherapies as well as the baseline CA19-9 level that showed most significant impact on the OS, and the model-based simulations could provide individual survival rate predictions for patients with different prognostic factors. This study quantitatively demonstrates the importance of physical status and baseline disease for the OS of advanced PDAC patients, and highlights that timely nutrition support would be helpful to improve the prognosis.

Material-Sparing Approach to Predict Tablet Capping Under Processing Compression Conditions Based on Mechanical and Molecular Properties Derived from Compaction Simulation and Crystal Structural Analysis.

Present study evaluates the usability of compaction simulation-based mechanical models as a material-sparing approach to predict tablet capping under processing compression conditions using Acetaminophen (APAP) and Ibuprofen (IBU). Measured mechanical properties were evaluated using principal component analysis (PCA) and principal component regression (PCR) models. PCR models were then utilized to predict the capping score (CS) from compression pressure (CP). APAP formulations displayed a quadratic correlation between CS and CP, with CS rank order following CP of 200MPa < 300MPa < 100MPa, indicating threshold compression pressure (TCP) limit between 200 and 300 MPa, resulting in higher CS at 300 than 200 MPa regardless of increased CP. IBU formulations displayed a linear correlation between CS and CP, with CS rank order following CP of 100MPa < 200MPa < 300MPa, indicating TCP limit between 100 and 200 MPa, resulting in higher CS at 200 and 300 than 100 MPa regardless of increased CP. Molecular models were developed as validation methods to predict capping from CP. Measured XRPD patterns of compressed tablets were linked with calculated Eatt and d-spacing of slip planes and analyzed using variable component least square methods to predict TCP triggering cleavage in slip planes and leading to capping. In APAP and IBU, TCP values were predicted at 245 and 175 MPa, meaning capped tablets above these TCP limits regardless of increased CP. A similar trend was observed in CS predictions from mechanical assessment, confirming that compaction simulation-based mechanical models can predict capping risk under desired compression conditions rapidly and accurately.

A model-informed clinical trial simulation tool with a graphical user interface for Duchenne muscular dystrophy.

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

Wettbewerbsfähigkeit der Industrie und Energiepreise

Abstract The rise in energy prices in 2021/22 put the competitiveness of Austrian manufacturing industry to the test. Model-based simulations and an enterprise survey were carried out to analyze the medium-term effects of high energy prices. The results show that the cost disadvantages in international competition induced by energy price increases will lead to a decline in industrial production and employment in the medium term and increase the risk of relocation of energy-intensive production.

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models.

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

Optimizing charge transport simulation for hybrid pixel detectors

To enhance the spatial resolution of the MÖNCH 25 µm pitch hybrid pixel detector, deep learning models have been trained using both simulation and measurement data. Challenges arise when comparing simulation-based deep learning models to measurement-based models for electrons, as the spatial resolution achieved through simulations is notably inferior to that from measurements. Discrepancies are also observed when directly comparing X-ray simulations with measurements, particularly in the spectral output of single pixels. These observations collectively suggest that current simulations require optimization. To address this, the dynamics of charge carriers within the silicon sensor have been studied using Monte Carlo simulations, aiming to refine the charge transport modeling. The simulation encompasses the initial generation of the charge cloud, charge cloud drift, charge diffusion and repulsion, and electronic noise. The simulation results were validated with measurements from the MÖNCH detector for X-rays, and the agreement between measurements and simulations was significantly improved by accounting for the charge repulsion.

Contact-implicit Model Predictive Control: Controlling diverse quadruped motions without pre-planned contact modes or trajectories

This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or foothold positions. This approach utilizes the contact-implicit differential dynamic programming (DDP) framework, merging the hard contact model with a linear complementarity constraint. We propose the analytical gradient of the contact impulse based on relaxed complementarity constraints to further the exploration of a variety of contact modes. By leveraging a hard contact model-based simulation and computation of search direction through a smooth gradient, our methodology identifies dynamically feasible state trajectories, control inputs, and contact forces while simultaneously unveiling new contact mode sequences. However, the broadened scope of contact modes does not always ensure real-world applicability. Recognizing this, we implemented differentiable cost terms to guide foot trajectories and make gait patterns. Furthermore, to address the challenge of unstable initial roll-outs in an MPC setting, we employ the multiple shooting variant of DDP. The efficacy of the proposed framework is validated through simulations and real-world demonstrations using a 45 kg HOUND quadruped robot, performing various tasks in simulation and showcasing actual experiments involving a forward trot and a front-leg rearing motion.

Crossing the Chasm: How to Approach Translational Pharmacokinetic-Pharmacodynamic Modeling of Phage Dosing.

Effectively treating multidrug-resistant bacterial infections remains challenging due to the limited drug development pipeline and a scarcity of novel agents effective against these highly resistant pathogens. Bacteriophages (phages) are a potential addition to the antimicrobial treatment arsenal. Though, phages are currently being tested in clinical trials for antibiotic-resistant infections, phages lack a fundamental understanding of optimal dosing in humans. Rationally designed preclinical studies using in vitro and in vivo infection models, allow us to assess clinically relevant phage +/- antibiotic exposure (pharmacokinetics), the resulting treatment impact on the infecting pathogen (pharmacodynamics) and host immune response (immunodynamics). A mechanistic modeling framework allows us to integrate this knowledge gained from preclinical studies to develop predictive models. We reviewed recently published mathematical models based on in vitro and/or in vivo data that evaluate the effects of varying bacterial or phage densities, phage characteristics (burst size, adsorption rate), phage pharmacokinetics, phage-antibiotic combinations and host immune responses. In our review, we analyzed study designs and the data used to inform the development of these mechanistic models. Insights gained from model-based simulations were reviewed as they help identify crucial phage parameters for determining effective phage dosing. These efforts contribute to bridging the gap between phage therapy research and its clinical translation.

Synergistic cotreatment of cooling tower blowdown and produced waters: Techno-economic, sustainability, and optimization systems analyses

A blowdown and produced water cotreatment train design was preliminarily simulated to explore water-energy nexus synergy. This process model was able to provide results concerning water compositions, chemical demand, and energy intensity. The computational expense associated with this simulation made it intractable to use within optimization algorithms for further informative, comprehensive systems analyses. This work details the efforts of developing a framework suitable for the optimization of computationally demanding wastewater treatment train simulations, specifically demonstrated for the cotreatment train’s process, economic, and sustainability models. The unique challenges of the cotreatment train model motivated a modified surrogate optimization methodology to be developed. Using this framework, worst and best case economic scenarios were optimized to minimize the levelized cost of water (LCOW) employed in a reduced-order model-based simulation. Applying the optimal designs obtained from the surrogate optimization into the original comprehensive simulation resulted in an expected range of LCOW from $2.01 to $4.43m-3. Other established techno-economic and sustainability indicators further inform on process implications. The process was compared to current management practices of blowdown water treatment and produced water injection to discuss the advantages and disadvantages of the proposed cotreatment train designed for water reuse. The cotreatment process was estimated to have lower levelized treatment costs but require more chemicals and energy to manage a higher salinity wastewater than pure blowdown water alone. If sustainable management goals motivate produced water remediation, the cotreatment process can be beneficial for the broader scope of water management.

Population pharmacokinetic analysis of zastaprazan (JP-1366), a novel potassium-competitive acid blocker, in patients and healthy volunteers.

Zastaprazan (JP-1366) is a novel potassium-competitive acid blocker for the treatment of acid-related disorders. We aimed to establish a population pharmacokinetic (PK) model of zastaprazan, thereby characterizing the PK of zastaprazan in patients with gastroesophageal reflux disease (GERD) as well as evaluating the impact of various covariates, including CYP2C19 phenotypes, on zastaprazan PK. This population PK analysis included zastaprazan plasma concentration-time data from 92 patients with erosive GERD and 68 healthy volunteers without any gastrointestinal disorders and was performed using nonlinear mixed-effect modeling. Simulations were conducted to predict zastaprazan PK under various dosing regimens in patients with GERD. The plasma PK of zastaprazan was adequately described by a two-compartment model with Erlang-type absorption (six sequential compartments) and first-order elimination. CYP2C19 phenotypes had no significant effect on zastaprazan PK. The disease status was identified as a significant covariate on apparent clearance of zastaprazan, showing lower values in patients with GERD compared to healthy volunteers. However, the model-based simulation indicated that the impact of disease status on zastaprazan exposure was not clinically meaningful. Overall, the current population PK model successfully characterized the observed zastaprazan PK in both patients with GERD and healthy volunteers.

Improving efficiency in the emergency department Monte Carlo simulation for determining the optimal number of staff

This paper examines the application of Monte Carlo simulation to determine optimal staffing levels at reception desks in the Emergency Department. The study utilizes data collected from a hospital in Bogotá, with all data anonymized to maintain the confidentiality of both the institution and its patients. By leveraging programming tools, the study randomizes the data and models various scenarios to assess the staffing requirements accurately. The primary goal is to enhance the efficiency and quality of service by aligning staffing levels with patient demand. The use of historical data, combined with the simulation of hypothetical scenarios, provides a robust basis for predicting future needs and making informed staffing decisions. The study's findings offer valuable insights into human resources management, enabling the Emergency Department to strategically allocate personnel, minimize wait times, and improve overall patient care. This approach demonstrates the potential for simulation-based models to optimize resource allocation in critical healthcare environments.