Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

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.

First responders including firefighters, paramedics, and police officers are among the first to respond to vehicle collisions on roads and highways. Police officers conduct regular roadside Please check if the country name is correct traffic controls and checks on urban and rural roads, and highways. Once first responders begin such operations, they are vulnerable to motor vehicle collisions by oncoming traffic, a circumstance that calls for a better understanding of contributing factors and the extent to which they affect tragic outcomes. In light of factors identified in the literature, this paper applies machine learning methods including decision tree and random forest to a subset of the National Collision Database (NCDB) of Canada that includes information on collisions between two vehicles (one in parked position) and the severity of these collisions as measured by having or not having injuries. Findings reveal that key measurable, predictable, and sensible factors such as time, location, and weather conditions, as well as the interconnections among them, can explain the severity of collisions that may happen between motor vehicles and first responders who are working alongside the roads. Analysis from longitudinal data is rich and the use of automated methods can be used to predict and assess the risk and vulnerability of first responders while responding to or operating on different roads and conditions.

The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facility’s population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes.

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.