LAI-PrEP Bridge Period Decision Support Tool v2.1.0: Code, Configuration, and Supplementary Materials

Background: Long-acting injectable cabotegravir (LAI-CAB) and lenacapavir for HIV pre-exposure prophylaxis demonstrate >96% efficacy but face a critical implementation challenge: 47% of patients prescribed LAI-PrEP never receive their first injection during the "bridge period" between prescription and administration. This tool addresses this gap through systematic risk assessment and evidence-based intervention selection. Tool Components: Core Algorithm (lai_prep_decision_tool_v2_1.py, 850 lines): Population-specific risk stratification, barrier impact quantification, intervention recommendation with mechanism diversity scoring External Configuration (lai_prep_config.json, 558 lines): 7 populations, 13 barriers, 21 evidence-based interventions with literature-derived effect sizes Comprehensive Test Suite (test_edge_cases.py, 18 scenarios): 100% pass rate covering clinical edge cases, mathematical validation, mechanism diversity, data export, and error handling Validation Scripts: Progressive validation from 1K to 21.2M patients Documentation: Installation guides, API reference, integration instructions, contributing guidelines Example Data: Individual patient JSON template, batch CSV with 10 diverse scenarios Command-Line Interface (cli.py): Single patient assessment, batch processing, configuration validation Validation Results: Progressive Scales: Validated on 1K (functional), 1M (large-scale), 10M (ultra-large-scale), and 21.2M patients (UNAIDS global target) Precision: Achieved ±0.018 percentage point margin of error at 21.2M scale (policy-grade statistical precision) Convergence: Demonstrated algorithmic stability across all scales Unit Testing: 100% pass rate (18/18 edge cases) Population Alignment: All predictions within published clinical trial ranges Predicted Impact: 81.6% relative improvement (baseline 23.96% → 43.50% with interventions) Global Health Impact: 4.1 million additional successful LAI-PrEP transitions globally ~100,000 HIV infections prevented annually (assuming 2–5% HIV incidence, 96% LAI-PrEP efficacy) $40 billion in lifetime treatment costs saved Health equity focus: Greatest benefits to PWID (+265%) and adolescents (+147%) Technical Features: Configuration-driven architecture enabling parameter updates without code modification Streaming processing supporting millions of patients with <4GB RAM Mechanism diversity scoring preventing redundant intervention recommendations JSON export for reproducibility and machine learning integration Optional logit-space calculations for mathematical soundness Cross-platform compatibility (Python 3.7+, minimal dependencies) Evidence Base: Parameters derived from: Clinical trials: HPTN 083 (n=4,566), HPTN 084 (n=3,224), PURPOSE trials (n=10,761) Implementation studies: Real-world LAI-PrEP initiation data Systematic reviews: Patient navigation effectiveness, structural barrier impacts International guidelines: WHO, CDC, UNAIDS Supplementary Materials: S1: Clinician Quick-Reference Card (point-of-care decision support) S2: Patient Information Handout (accessible patient education guide) S3: Machine-Readable Data Files (JSON configuration documentation, patient input examples) S4: Implementation Guide (staged validation protocols, training curriculum) S5: Clinical Decision Flowchart (6-step systematic workflow) S6: Non-Technical Guide S7: Intervention Inventory S8: Code Repository Publications: Demidont, A.C. Bridging the Gap: The PrEP Cascade Paradigm Shift for Long-Acting Injectable HIV Prevention. Viruses 2025. (in review) Demidont, A.C. Computational Validation of Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale. Viruses 2025. (in review) Repository Contents: This Zenodo archive contains the exact version of the code, configuration, validation data, and supplementary materials used in the published manuscripts, ensuring complete reproducibility of all reported results. The database contains all data related to the Viruses Long-Acting Injectable Antiretroviral Special Edition. Intended Use: Clinical implementation: Risk stratification and intervention selection at LAI-PrEP prescription Research: Prospective validation studies, algorithm refinement Quality improvement: Bridge period navigation program development Policy: Resource allocation modeling, implementation planning Important Limitations: Computational validation only; prospective clinical validation required Some parameters extrapolated from oral PrEP cascade (PWID, adolescents) Population categories aggregate substantial heterogeneity Real-world performance may differ from predictions Recommended staged implementation with systematic outcome tracking License: Software (Python code): Pharma-Restricted Open Healthcare License v1.0 Configuration/Data: CC BY 4.0 (attribution required) Documentation: CC BY 4.0 (attribution required) Supplementary Materials (LaTeX): CC BY 4.0 (attribution required) Note: Healthcare providers, researchers, academics, non-profits, and government agencies may use freely with attribution. Pharmaceutical/biotechnology companies require written permission for commercial use (patient care exception applies with notification). Keywords: HIV prevention, pre-exposure prophylaxis, long-acting injectable, cabotegravir, lenacapavir, implementation science, clinical decision support, health equity, patient navigation, bridge period, algorithm validation, computational validation, UNAIDS targets, global health Funding: This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of Interest: The author was previously employed by Gilead Sciences, Inc. (January 2020–October 2024) and held company stock that was fully divested by December 2024. This research was conducted independently without industry funding after employment concluded. The decision support tool is provided as an open-source resource. No sponsors were involved in the design, execution, interpretation, or writing of this study. Contact: A.C. Demidont, DOEmail: [e-mail]: Nyx Dynamics, LLCLocation: Fairfield, CT, USA Version: 2.1.0 (manuscript submission version) Date: October 2024 – November 2025 Citation If you use this tool in your work, please cite: Software: @software{demidont2025laiprep, author = {Demidont, A.C.}, title = {LAI-PrEP Bridge Period Decision Support Tool}, version = {2.1.0}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.17727117}, url = {https://zenodo.org/records/17727117} } Manuscripts: @article{demidont2025bridging, author = {Demidont, A.C.}, title = {Bridging the Gap: The PrEP Cascade Paradigm Shift for Long-Acting Injectable HIV Prevention}, journal = {Viruses}, year = {2025}, note = {In review} } @article{demidont2025computational, author = {Demidont, A.C.}, title = {Computational Validation of Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale}, journal = {Viruses}, year = {2025}, note = {In review} } Related Identifiers Zenodo DOI: 10.5281/zenodo.17727117 GitHub Repository Zenodo Metadata Summary Field Value Title LAI-PrEP Bridge Period Decision Support Tool v2.1.0: Code, Configuration, and Supplementary Materials for Viruses 2025 Manuscripts Resource Type Software Version 2.1.0 Publication Date 2025-11-29 License Other (Open) — Pharma-Restricted Open Healthcare License v1.0 Author Demidont, A.C. Affiliation Nyx Dynamics, LLC ORCID 0000-0002-9216-8569