Abstract
The main goal of emergency preparedness is the creation of processes and procedures focused on the preservation of human life. One of the leading contributors to loss of life during multi-casualty incidents (MCIs) is the lack of adequate planning, preparation, and simulation. According to a 2017 study, approximately 15 percent of human-caused mass casualty events, with over 10 fatalities, are mass shootings. These events can occur under various circumstances and take place in a wide range of venues, such as schools, offices, and outdoor events, presenting a wide range of unique challenges. To address these more effectively through procedures and policies, more research is need, including simulation and creation of digital twins, all of which have proven beneficial in gathering insights. This is especially true when conducting drills that are not practical or possible, as they do not allow for the multitude of responses to active shooter events. Current research models in use today treat the victims of these simulations either as "killed" or "unaffected." This binary approach is suitable for many simulations when the timeliness of interventions is of no concern, but it does not allow for higher-fidelity simulation, which may be beneficial when developing response and safety protocols for a specific event or specific facility. Simulating physiological decline is beneficial to improving realism and will lead to response protocol improvement. Increased fidelity can help assess the effects of active bystanders voluntarily and opportunistically providing medical first response. Furthermore, this allows us to assess the response of others who have different primary functions during those events, such as the School Resource Officer, or a tactical medic attached to Special Weapons And Tactics during the process of building clearing. Last but not least, this type of simulation can inform and improve how a lockdown is conducted. In mass shooting events, uncontrolled bleeding is often the proximate cause of death for victims. Several data sources were consulted to simulate exsanguination, which helped quantify and describe blood loss based on different types of injuries. This work summarizes our findings and provides a practical guide for the implementation of these findings. In additional papers, the authors cover the process of blood loss mitigation and provide a reference library for implementation in AnyLogic. This research focuses on the simulation of the initial injury and bleeding control mitigation, efforts which by historical context are limited to the first hour of treatment, the so-called golden hour of trauma management. The circulatory system is complex and has several compensatory mechanisms. The efficiency and timing of each are predictable but have some variability to every individual. This work mainly focuses on the primary effectors and simulates the overall process defined by statistical data from peer-reviewed studies. Ultimately, this work presents a quantitative model of blood loss as a function of time, injury placement, and individual victim variability such as age, weight, and gender, which are suitable for computer simulation. Data were validated using empirical datasets. While no model is perfect, the authors propose a common framework for future simulation work that different researchers can use on a known standard deterministic model. This allows for better control for the blood loss process variables while testing other hypotheses related to emergency response for similar events.