A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available. © The Author(s) 2023.

Using discrete-event simulation to address COVID-19 health and safety guidelines in outpatient laboratory clinic

Simulation has, over multiple decades, achieved a remarkable record of improving operational efficiency and effectiveness in many areas - manufacturing, supply chains (including commercial transportation and logistics), health care, public-sector transport, service industries, and military operations. About 2/3 through the twentieth century, simulation's earliest successes appeared in the manufacturing sector. These successes began with attention to value-added operations (e.g., at machines often entailing high capital investments) and rapidly spread to the non-value-added but very necessary material-handling requirements within factories. SARS-CoV-2, (COVID-19) has caused a rapid, widespread change in patient care across the globe. New health and safety guidelines have been established by the Centers for Disease Control and Prevention (CDC) (Health Care Guidelines, 2020). Still, it has been left to individual facilities to address and implement solutions to new standards for social distancing and cleanliness. Here we develop a discrete-event simulation model to simulate an outpatient laboratory clinic, including check-in and patient interaction, to determine if changes lead to increased efficiency and reduce patient wait times, without increasing staffing or additional resources. Under the aegis of the University of Michigan Medical Group (UMMG), this simulation is validated against real data of waiting time at the University of Michigan Canton Health Center (UMCHC) during the height of the pandemic. © 2021 The Authors.

Hospital Room Management for Covid-19 Patients Using Petri Nets

This paper treat the design of the sequence organization and then the optimization of a discrete event system (DES) modelled by Temporal Petri Net (T-PN) comprising a set of specifications corresponding to time intervals to activate or access another event. A Petri net is a well-known model that describes distributed systems. It is commonly used to describe various aspects of distributed systems, such as choice and synchronization. This paper focuses on the organizing problems in the hospitalization domain during the Covid-19 pandemic. We advocate the use of a real time approach based on TemporalPN and mathematical modeling to help drive the healthcare system in the face of occurrence of this type of giving many patients currently, which requires rethinking the predictive decision. The proposed solution permits to optimize the time to find all empty rooms using PN Temporal and the Dijkstra approach. © 2022, Springer Nature Switzerland AG.

Operator Resource Planning in a Giga Fab during Covid-19 Restrictions

With the world facing a public health emergency due to the Coronavirus disease (COVID-19) in a global pandemic, this paper provides insight about how a simulation model was used to determine the impact of headcount variability during lockdown on fab performance. To create a robust simulation model, operator loading time was introduced as one of the input parameters. An existing and well validated Discrete Event Fab simulation model was extended with operator modelling, and was used to conduct case studies, evaluating the impact of different operator availability scenarios including work disruptions for several shifts within a week. The studies provide implications for operation to derive mitigation strategies, weighing the trade-off between cost demand and speed loss due to operator resources. © 2021 IEEE.

Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.

OBJECTIVE: To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes. MATERIALS AND METHODS: Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs. RESULTS: The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy. CONCLUSIONS: The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures. LAY ABSTRACT: During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.