DOI: 10.5281/zenodo.17429832
title: LAI-PrEP Bridge Period Decision Support Tool v4.1.0: Code, Configuration, and Supplementary Materials

description: Zenodo v4.1.0 Update - Complete Documentation

Update Date: December 12, 2025Previous Version DOI: 10.5281/zenodo.17727117 (v2.1.0)NEW Version DOI: 10.5281/zenodo.17873201 (v4.1.0)Full URL: https://zenodo.org/record/17873201

What's New in v4.1.0

Manuscripts Uploaded (Viruses Journal Submission)





lai_tool_final.tex - LAI-PrEP Computational Validation Manuscript



Complete computational validation at UNAIDS global scale (21.2M patients)

Progressive validation across 4 scales (1K, 1M, 10M, 21.2M)

Comprehensive edge case testing (18/18 pass rate, 100%)

Full Discussion section addressing AI suitability in healthcare





Supplementary_File_S3_AI_Readiness_Healthcare.tex - Framework for Responsible AI Deployment



Critical examination of computational vs. clinical validity

Evidence quality assessment (Tier 1-3 classification)

Interpretability and algorithmic transparency analysis

Equity and health disparities considerations

Benefit-risk calculus and staged implementation framework




Supplementary Files



Supplementary Materials (S1-S4)



S1: Machine-readable configuration files (JSON)

S2: Complete 21-intervention library with evidence synthesis

S3: AI Readiness framework (as detailed above)

S4: Code repository documentation and reproducibility instructions




Documentation Updates



README with reproducibility instructions

Updated LICENSE files

Citation metadata (CITATION.cff)


Version History




Version
Date
DOI
Focus
Status




v2.1.0
Oct 24, 2025
10.5281/zenodo.17727117
Initial code release
Superseded


v4.1.0
Dec 12, 2025
10.5281/zenodo.17873201
Viruses manuscript submission
CURRENT




Key Metrics from v4.1.0 Manuscripts

Computational Validation Results



Sample scales: 1,000 → 1,000,000 → 10,000,000 → 21,200,000 patients

Algorithmic precision: ±0.018 percentage points (95% CI) at 21.2M scale

Precision improvement: 144-fold increase from 1K scale

Test pass rate: 18/18 edge cases (100%)

Convergence: Mean success rates stabilized by 1M patients


Primary Findings



Baseline bridge period success: 23.96% (95% CI: 23.94–23.98%)

With interventions: 43.50% (95% CI: 43.48–43.52%)

Relative improvement: 81.6%

Global impact: 4.1 million additional successful transitions


Population Disparities



PWID baseline: 10.36% (highest need)

MSM baseline: 33.11% (lowest need)

Disparity gap: 22.75 percentage points

PWID intervention benefit: +265% relative improvement

Adolescent benefit: +147% relative improvement


Regional Analysis (UNAIDS Global Scale)



Sub-Saharan Africa: 62% of global patients, 21.69% baseline success

Europe/Central Asia: 6% of patients, 29.33% baseline success

Regional equity gap: 7.64 percentage points

SSA relative improvement with interventions: +91.2%


Economic Projections



Annual HIV infections prevented: ~80,000–200,000 (midpoint: 100,000)

Lifetime treatment costs saved: $40 billion

Implementation cost: $19.1 billion

Annual ROI: 2.1:1

5-year cumulative ROI: 10.5:1


Complete Manuscript Contents

Main Manuscript Structure



Title: Computational Validation of a Clinical Decision Support Algorithm for LAI-PrEP Bridge Period Navigation at UNAIDS Global Target Scale

Journal: Viruses (MDPI)

Article Type: Original research manuscript

Word Count: ~52,000 (including supplementary materials)

Tables: 16 total (main + supplementary)

Figures: 8 total (including supplementary)

References: 87 citations


Section Breakdown

Main Manuscript (lai_tool_final.tex):





Introduction



LAI-PrEP promise and implementation challenges

Bridge period attrition crisis (47% failure rate)

Need for computational decision support

Study objectives and distinction between computational vs. clinical validity





Materials and Methods



Evidence synthesis from >15,000 clinical trial participants

Algorithm development with three-tier evidence classification

Population-specific baseline rates

21 structural barriers with quantified impacts

21 evidence-based interventions with effect sizes

Configuration-driven software architecture

Mechanism diversity scoring algorithm

Synthetic population generation procedures

Intervention combination models (two-stage approach)

Progressive validation study design (4 tiers)

Comprehensive edge case testing (18 scenarios)

Outcome measures and statistical analysis

Software availability and data sharing





Results



Progressive validation: Convergence and precision analysis

Unit test results across all validation tiers

Comprehensive edge case testing (100% pass rate)

Population-specific predictions vs. published trials

Population-specific intervention effects

Regional analysis at UNAIDS global scale

Barrier impact analysis with dose-response relationship

Risk stratification distribution

Global impact projections





Discussion



Principal findings and contributions

Computational precision vs. clinical uncertainty

Framework for prospective clinical validation

Contextualization of findings

Strengths and limitations

AI Suitability for Healthcare: 5 Critical Questions



External validity and false confidence

Evidence quality and extrapolated parameters

Interpretability and clinical oversight

Equity and population heterogeneity

Benefit-risk calculus and staged implementation



Limitations of computational validation vs. real-world performance

Future directions





Conclusions



Summary of computational validation achievements

Distinction between algorithmic and clinical readiness

Call for prospective validation and equity-focused implementation

Commitment to responsible AI deployment




Supplementary File S3: AI Readiness in Healthcare

Comprehensive 45-page framework addressing:





External Validity



Computational precision ≠ clinical certainty

Mathematical vs. external vs. prospective validity

Synthetic data limitations

Staged implementation approach





Evidence Quality



Tier 1 (direct LAI-PrEP): 8 interventions

Tier 2 (HIV prevention analogs): 9 interventions

Tier 3 (cross-field extrapolation): 4 interventions

Parameter uncertainty vs. computational precision

Dynamic evidence integration strategies





Interpretability



Algorithmic transparency: How calculations work

Mechanistic reasoning: Why recommendations are made

Uncertainty quantification: Confidence intervals

Population-specific baselines

Interpretability paradox and error detection

Supporting clinical judgment over algorithmic certainty





Equity and Heterogeneity



Aggregation bias in healthcare AI

Multi-dimensional stratification approach

Individual barrier assessment (13 barriers)

Algorithmic fairness considerations

Within-population heterogeneity recognition

Distributional impact assessment





Benefit-Risk Calculus



Projected benefits: 4.1M transitions, 100K infections prevented

Implementation risks: Resource misallocation, false confidence, equity harm

Staged implementation framework:



Phase 1: Pilot validation (2-3 sites, 50-100 patients)

Phase 2: Multi-site validation (10-15 sites, 500-1000 patients)

Phase 3: Scaled implementation with continuous monitoring







Limitations of Computational Validation



Simulation vs. reality differences

Parameter uncertainty vs. computational precision

Context-specificity of parameters

Path forward: Prospective validation and continuous refinement




🔗 Updated Citations for Manuscripts

For Data Availability Statements

APA Format:

Demidont, A. C. LAI-PrEP bridge period decision 
support tool: Computational validation at UNAIDS global scale [Computer 
software]. Zenodo. https://doi.org/10.5281/zenodo.17873201


BibTeX Format:

@software{demidont2025laiprep,
  author    = {Demidont, Adrian C},
  title     = {LAI-PrEP Bridge Period Decision Support Tool},
  subtitle  = {Computational Validation at UNAIDS Global Scale},
  version   = {4.1.0},
  year      = 2025,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.17873201},
  url       = {https://doi.org/10.5281/zenodo.17873201}
}


Chicago Manual of Style (Notes-Bibliography):

Demidont, Adrian C. "LAI-PrEP Bridge Period Decision 
Support Tool: Computational Validation at UNAIDS Global Scale." Zenodo. 
December 12, 2025. https://doi.org/10.5281/zenodo.17873201.


Vancouver Format:

Demidont AC. LAI-PrEP bridge period decision support tool: 
Computational validation at UNAIDS global scale [computer software]. 
Zenodo. 2025. https://doi.org/10.5281/zenodo.17873201


Nature Format:

Demidont, A. C LAI-PrEP bridge period decision support tool: 
Computational validation at UNAIDS global scale. Zenodo https://doi.org/10.5281/zenodo.17873201 (2025).


MLA Format (9th Edition):

Demidont, Adrian C. "LAI-PrEP Bridge Period Decision 
Support Tool: Computational Validation at UNAIDS Global Scale." Version 4.1.0, 
Zenodo, 12 Dec. 2025, doi.org/10.5281/zenodo.17873201.


📄 Manuscript LaTeX References Updated

In Data Availability Statement:

\section*{Data Availability Statement}
All code, configuration files, validation datasets, and supplementary materials 
are publicly available on Zenodo (DOI: \url{https://zenodo.org/record/17873201}) 
and GitHub Repository \url{https://github.com/Nyx-Dynamics/lai-prep-bridge-tool-pub}
(release v4.1.0, commit: [current-commit-hash]).


In GitHub Badge Section (if applicable):

\includegraphics[alt={DOI}]{https://zenodo.org/badge/DOI/10.5281/zenodo.17873201.svg}


 

Files in v4.1.0 Zenodo Record

Manuscripts



lai_tool_final.tex (52KB)

Supplementary_File_S3_AI_Readiness_Healthcare.tex (45KB)

[Additional supplementary files S1, S2, S4 as applicable]


Software



lai_prep_decision_tool_v2_1.py (850 lines)

lai_prep_config.json (configuration with 21 interventions)

test_edge_cases.py (18 test scenarios)

Validation result JSONs (1K, 1M, 10M, 21.2M scales)


Documentation



README.md (installation, usage, reproducibility)

LICENSE.md (MIT + CC-BY 4.0)

CITATION.cff (machine-readable citations)

CONTRIBUTING.md (if applicable)


 

Contact & Citation Information

For Questions About:



Algorithm: See Methods section in manuscript

Validation: See Results section with tables and figures

Implementation: See Discussion section, particularly AI Suitability subsection

Code reproduction: See Supplementary File S4 and GitHub README


Citation Template for Other Researchers:

When using this tool, please cite:

Demidont AC. Computational validation of a clinical decision 
support algorithm for LAI-PrEP bridge period navigation at UNAIDS global 
target scale. Viruses. 2025;[volume]:[article]. 
https://doi.org/10.5281/zenodo.17873201


Summary of v4.1.0

What's New:



Complete Viruses journal submission manuscripts

Comprehensive AI readiness framework (S3 supplement)

Full code repository with 100% test pass rate

Complete documentation for reproducibility

Updated evidence synthesis with 87 citations


Why It Matters:



Establishes algorithmic readiness for prospective clinical validation

Provides transparent framework for responsible AI deployment in healthcare

Demonstrates unprecedented computational rigor in HIV prevention tool validation

Bridges implementation science and AI with explicit equity focus

Ready for peer review and potential global implementation


Next Phase: Prospective clinical validation with 2-3 diverse sites (50-100 patients) to compare algorithmic predictions with real-world outcomes and refine parameters based on implementation data.

Status: ✅ COMPLETE AND SUBMITTED TO ZENODODOI: 10.5281/zenodo.17873201Version: 4.1.0Date: December 12, 2025Ready for Viruses Journal Submission
description: ** Zenodo on all historical updates of datasets requiring new Zenodo doi documented in Zenodo ChangeLog.

** all Zenodo dataset pushes and dataset updates documented for review in update for Transparency and Reproducibility.

 



 

** Licensing Excludes Pharamceutical/Manufacturer Use without express written permission from Author for use.