Welcome
The Virtual Laboratory is an interactive computational platform designed to explore, simulate, and analyze real-world scenarios, including population dynamics, spatiotemporal biological processes, and pathogen transmission in healthcare settings. The platform enables researchers to study complex dynamical behavior, evaluate the impact of key parameters, visualize spatiotemporal patterns, and test intervention strategies through controlled numerical experiments.
User Manual Simulation Apps
Infectious Disease Simulation Apps
sir_app
Community SIR Model (Deterministic). Explore how transmission and recovery/isolation shape outbreak size and timing, and how physician recommendations that reduce contact or shorten infectious periods change population-level outcomes.
sir_delayed_intervention_app
Community SIR Model with Delayed Intervention. Examine how the timing of interventions affects peak burden and total infections, illustrating why early action can matter more than intervention strength.
sir_hospital_gillespie
Stochastic Hospital SIR Model (Event-Driven). Model infection spread in a small hospital population using a Gillespie approach to see how chance events produce variability even under identical policies.
sir_hospital_stochastic_tau
Stochastic Hospital SIR Model (Discrete-Time Approximation). Run repeated simulations to observe how modest changes in transmission or isolation affect the distribution of outcomes and tail risk.
SSI_stochastic_decision_lab
Procedure-Driven Surgical Site Infection Model. Treat SSI as probabilistic risk per procedure and explore how prophylaxis timing, sterile technique, operating room practices, and patient factors influence infection rates.
AUSTIN_core_VRE_gillespie
Austin ICU VRE Model with Explicit Healthcare Workers. Explore how hand hygiene, cohorting, antibiotic pressure, and colonized admissions interact when HCWs are modeled explicitly as transient vectors.
EXTENDED_sir_hospital_gillespie
Hospital SIR Model with Importation (Event-Driven). Add background importation representing admissions/transfers/staff exposures and explore persistence despite strong in-hospital controls.
EXTENDED_sir_hospital_stochastic_tau
Hospital SIR Model with Importation (Discrete-Time). Study how external pressure interacts with transmission controls and why elimination may be unrealistic even with high compliance.
HAI_generalized_Austin_gillespie
Generalized Healthcare-Associated Infection Model. Extend Austin-style reasoning to device-associated HAIs (e.g., CLABSI, CAUTI, VAP) and explore how colonization, device exposure, and care processes interact with physician decisions.
GitHub Modeling Resources
In addition to the simulation apps developed for this project, we have created a broader set of computational tools using MATLAB, R, Python, GAMA, and AnyLogic. These tools support modeling of biosecurity, antimicrobial resistance, healthcare-associated infections, patient behavior, and contact-driven transmission across multiple settings.
Modeling Biosecurity and Antibiotic Resistance in a One Health Context (MATLAB)
This repository implements a One Health modeling framework to examine how biosecurity compliance and farmworker behavior influence antimicrobial resistance dynamics across human, animal, and environmental systems.
Agent-Based Modeling of Respiratory Infections in Nursing Homes (GAMA)
This project uses the GAMA platform to simulate respiratory infection transmission in nursing homes, focusing on resident–staff interactions and facility structure. Instructions for running the simulations are provided in a separate document titled Instructions to Run Simulations.
HAI Modeling with Indoor GPS-Based Contact and Movement Data (Python)
This repository integrates indoor GPS-based contact and movement data with infection modeling to assess exposure risk and transmission pathways within healthcare environments.
Emergency Department HAI Model Using AnyLogic
This AnyLogic-based model simulates patient flow and healthcare-associated infection risk in emergency departments, emphasizing crowding, throughput, and contact structure.
Bayesian Estimation of MRSA Transmission Rates in Hospitals (MATLAB)
This repository applies Bayesian inference techniques to estimate transmission parameters in compartmental models of nosocomial methicillin-resistant Staphylococcus aureus (MRSA).
Comparative Modeling of HAI and CAI Urinary Tract Infections (Python)
This project uses machine learning methods to compare healthcare-associated and community-acquired urinary tract infection transmission patterns.
Indoor Respiratory Disease Transmission Modeling and Analysis (Python)
This repository focuses on modeling respiratory disease transmission in indoor environments, integrating spatial structure, contact patterns, and ventilation considerations.
Enhancing Agent-Based HAI Models Using RTLS UWB Data (Python)
This project enhances agent-based models of healthcare-associated infections by incorporating real-time location system (RTLS) ultra-wideband data to capture fine-scale contact dynamics.
Model-Based Hypothesis Testing
This repository develops computational approaches for hypothesis testing using mechanistic models, supporting inference and decision-making under uncertainty.
AnyLogic Simulation Models
The following AnyLogic models extend the scope of this work by focusing on agent-based epidemic dynamics, emergency care operations, trauma systems, and hospital admission workflows. These models emphasize system-level behavior, resource constraints, and operational decision-making in healthcare settings.
Patient Flow Modeling of Emergency Departments (AnyLogic Cloud)
This cloud-hosted AnyLogic model simulates patient flow and congestion in emergency departments, supporting analysis of throughput, delays, and system-level performance.
Agent-Based Epidemic Model (AnyLogic)
This agent-based epidemic model simulates disease spread through individual-level interactions, allowing exploration of heterogeneous behavior, contact structure, and intervention strategies. The model is designed to support scenario analysis and policy evaluation in dynamic population settings.
Trauma Center Operations Model (AnyLogic)
This model was developed to analyze operational improvement options in trauma centers, including staffing levels and schedules, Express Care hours of operation, and patient flow redesign. Experiments include evaluating new management functions and the addition of bedside registration processes to reduce delays and improve throughput.
Emergency Department Operations Model (AnyLogic)
This simulation focuses on emergency department dynamics, examining patient arrival patterns, service processes, congestion, and resource utilization. The model supports analysis of operational policies aimed at reducing wait times and improving patient care under variable demand.
Hospital Admission–Emergency–Elective Interaction Model (AnyLogic)
This model examines the interaction between emergency admissions, elective procedures, and inpatient capacity. It is designed to explore how scheduling decisions, admission policies, and capacity constraints influence hospital performance and patient flow across services.