SIMULATING VICTIM HEALTH STATE EVOLUTION FROM PHYSICAL AND CHEMICAL INJURIES IN MASS CASUALTY INCIDENTS

Author:

Benhassine Mehdi1ORCID,De Rouck Ruben2ORCID,Debacker Michel2ORCID,Hubloue Ives2,Dhondt Erwin3,Van Utterbeeck Filip1

Affiliation:

1. Department of Mathematics, Royal Military Academy, Brussels, Belgium

2. Research Group on Disaster and Emergency Medicine, Vrije Universiteit Brussel, Jette, Belgium

3. DO Consultancy, Brussels, Belgium

Abstract

The field of discrete-event simulation for medical disaster management is relatively new. In such simulations, human victims are generated using pre-determined transitions from one health state to the next, based on a set of triggers that correspond to treatment or the clinical progression of untreated injuries or diseases. However, this approach does not account for subtle differences in clinical progression. We propose a parameter-based model to characterize the evolution of symptoms at first for physical and nerve agent chemical injuries. We used a Gompertz function to predict the time of death in trauma based on forensic data. Then we separately considered the effects of the chemical warfare agent sarin (GB) being the origin of the chemical injuries for the purpose of modelling a GB attack in a metro station. We emphasize that our approach can be extended to other CBRN threats pending knowledge of clinical progressions available in the literature for the purpose of casualty estimations. The intent is to provide an estimate of time to death without any treatment and overlay this model with a treatment model, improving the evolution of the health state. A modification for non-life-threatening injuries is included without losing generality. Improvement functions modelling medical treatment are proposed. We argue that the availability of injury scores vs mortality can greatly enhance the validity of the model.

Publisher

Vilnius Gediminas Technical University

Reference25 articles.

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2. Bellamy, R. (1984). The causes of death in conventional land warfare: Implications for combat casualty care research. Military Medicine, 149(2), 55-62. https://doi.org/10.1093/milmed/149.2.55

3. Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2022a). Continuous Victim model for use in mass casualty incident simulations. In Proceedings of the 20th Industrial Simulation Conference (pp. 10-15). Eurosis-ETI, Ostend.

4. Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2022b). Simulating the evacuation of a subway station after a sarin release. In Proceedings of the 36th European Simulation Conference (pp. 271-277). Porto, Portugal, EUROSIS-ETI.

5. Casagrande, R., Wills, N., Kramer, E., Sumner, L., Mussante, M., Kurinsky, R., McGhee, P., Katz, L., Weinstock, D. M., & Coleman, C. N. (2011). Using the model of resource and time-based triage (MORTT) to guide scarce resource allocation in the aftermath of a nuclear detonation. Disaster Medicine and Public Health Preparedness, 5 (S1), S98-S110. https://doi.org/10.1001/dmp.2011.16

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