Global Sensitivity Analysis for a Mathematical Model of the General Escape Theory of Suicide

2025
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Authors: Sophie Engels; Valeria Epelbaum; Shirley B. Wang; Derek de Beurs; Valeria Krzhizhanovskaya
Published in: Computational Science – ICCS 2025 Workshops
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the intricate world of mathematical modeling and sensitivity analysis to unravel the complexities of suicidal ideation and behaviors. By applying Sobol and PAWN sensitivity analyses to the General Escape Theory of Suicide, the study identifies key parameters such as self-feedback loops and carrying capacities that significantly influence model outcomes. The analysis reveals the stable nature of parameter influences over a two-week simulation period, highlighting the critical role of interactions between aversive internal states and the urge to escape. The chapter also explores the impact of stressors on the model outputs, underscoring the importance of understanding these dynamics for developing effective intervention strategies. Through detailed visualizations and rigorous statistical methods, the study provides a nuanced understanding of the factors contributing to suicidal thoughts and behaviors, offering valuable insights for researchers and clinicians in the field of suicide prevention.

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Title: Global Sensitivity Analysis for a Mathematical Model of the General Escape Theory of Suicide
Authors: Sophie Engels
Valeria Epelbaum
Shirley B. Wang
Derek de Beurs
Valeria Krzhizhanovskaya
Publisher: Springer Nature Switzerland
Book: Computational Science – ICCS 2025 Workshops
Print ISBN: 978-3-031-97572-1

Electronic ISBN: 978-3-031-97573-8

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-3-031-97573-8_13

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