Chronic Stress: An Overview in the field of computational modelling and complexity

Overview This dataset contains the complete scoring methodology, evaluation data, and source materials supporting the systematic review "Chronic Stress: An Overview in the Field of Computational Modelling and Complexity" (Bumbuc et al., 2026). The repository provides a standardized framework for evaluating computational models of chronic stress and allostatic load across three critical dimensions: data requirements, computational demands, and mechanistic interpretability. Purpose Computational modeling of chronic stress lacks standardized frameworks for comparing diverse methodological approaches (machine learning, mechanistic ODE/PDE models, stochastic simulations, and hybrid methods). This dataset enables: Reproducible model evaluation using explicit scoring criteria (1-5 scale per dimension) Transparent comparison across heterogeneous modeling paradigms Practical model selection based on available resources and research objectives Clinical translation assessment through Technology Readiness Level (TRL) mapping Framework Rationale The complexity grading system (Grades 1-5) integrates principles from: NASA Technology Readiness Levels (TRLs): Assessing technology maturity from proof-of-concept to operational deployment ASME V&V 40 Standards: Verification and validation requirements for computational models in medical devices Field-specific adaptation: Recognizes current developmental stage of chronic stress research, which focuses on subsystem models (HPA axis, cardiovascular responses, immune function) rather than fully integrated multi-scale representations Complexity is not hierarchical – each grade serves complementary purposes: Grade 1 (ML/DL): Population screening from wearable data Grade 2 (Agent-based): Emergent system behaviors Grade 3 (Stochastic/System Dynamics): Policy interventions through transparent feedback loops Grade 4 (Mechanistic ODE/PDE): Therapeutic target identification through biological transparency Grade 5 (Hybrid Mechanistic-ML): Personalized medicine combining mechanistic foundations with individual variability Dataset Contents 1. Scoring Methodology (PDF) File: Simple-Complexity-Score-grading.pdf Detailed justification for the 5-grade classification system Scoring criteria for three dimensions: Data Constraints (Table 1a): Parameter counts and biological scale coverage Computational Constraints (Table 1b): Runtime requirements and infrastructure needs Mechanistic Interpretability (Table 1c): Transparency of biological representation Complete reference list with empirical benchmarks from cardiovascular modeling, HPA axis research, systems biology, and multi-scale physiological modeling TRL and V&V 40 alignment for each complexity grade 2. Model Scoring Data (Excel) File: Scoring.xlsx Quantitative scores for 15 computational models published 2020-2025 Evaluated dimensions: Data requirements (1-5), Computational demands (1-5), Interpretability (1-5) Overall complexity grade assignment (1-5) based on holistic assessment Model metadata: authors, year, disease context, computational approach 3. Model Categorization (Excel) File: Categorisation.xlsx Classification of models into paradigms: Machine Learning Models (MLM) Data-Driven Mechanistic Models (DD-MM) Continuous Nonlinear Mechanistic Models (CNL-MM) Stochastic Models (SM) Feature representation: feedback loops, long-term dynamics, allostatic load thresholds, wearable integration, circadian/immune modeling, cardiovascular metrics Cross-study similarity analysis 4. Source Publications (15 PDFs) Full-text PDFs of all reviewed studies: Bourdon et al. (2020): Time perspective and allostatic load Nixdorf et al. (2020): Burnout in elite athletes Mann et al. (2021): Cumulative stress factor structure Bozorgmehr et al. (2021): ML-based stress detection in healthcare workers Lei et al. (2021): Sex differences in decision-making under stress Sarkar et al. (2021): Prenatal stress detection via ECG deep learning Van Kraaij et al. (2020): Heart rate dynamics and chronic stress Karin et al. (2020): HPA axis dysregulation model Rao & Androulakis (2020): Circadian cortisol rhythms Monzer et al. (2022): Cardiac autonomic responses in Type 2 diabetes Benthem De Grave et al. (2022): Work stress to disease stochastic model Bumbuc et al. (2023): Stress-diabetes threshold model Yang et al. (2023): Type 2 diabetes progression with obesity Yildirim et al. (2023): Hybrid data-driven diabetes remission model Ribeiro et al. (2024): AI-based stress assessment in Type 2 diabetes Intended Use This dataset supports: Researchers: Match computational approaches to available resources and research questions Clinicians: Select appropriate models for intervention design Funding Agencies: Evaluate translational readiness and methodological rigor Journal Reviewers: Standardized criteria for assessing computational model complexity Methodologists: Benchmark for developing next-generation hybrid approaches When using this dataset, please cite: Dataset: Bumbuc, R. V., Baglamis, S., Yildirim, V., Nicolaou, M., & Sheraton, V. M. (2026). Complexity Classification Framework for Computational Models of Chronic Stress: Scoring Data and Methodology (2020-2025). Zenodo. https://doi.org/10.5281/zenodo.18442119 Review Paper: Bumbuc, R. V., et al. (2026). Chronic Stress: An Overview in the Field of Computational Modelling and Complexity. [in submission]. License CC BY 4.0 – You are free to share and adapt this material for any purpose, with appropriate credit. Keywords Chronic stress, Computational modeling, Complexity classification, Technology Readiness Levels, ASME V&V 40, Machine learning, Mechanistic models, Agent-based models, Stochastic models, Allostatic load, Type 2 diabetes, HPA axis, Systems biology, Clinical translation, Model validation Contact Corresponding Author: Vivek M. Sheraton (v.s.muniraj@uva.nl)Institution: Computational Science Lab, Informatics Institute, University of Amsterdam