Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents

    Mehdi Benhassine   Affiliation
    ; Ruben De Rouck Affiliation
    ; Michel Debacker Affiliation
    ; Ives Hubloue Affiliation
    ; Erwin Dhondt Affiliation
    ; Filip Van Utterbeeck Affiliation


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.

Keyword : disaster medicine, discrete-event simulation, victim health state model, mass-casualty incidents, combined injuries

How to Cite
Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2023). Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents. New Trends in Computer Sciences, 1(2), 113–125.
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Dec 28, 2023
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