VaccineResponseEngine: Antibody Kinetics Modeling, Seroconversion Analysis, and High/Low Responder Classification
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Vaccine immunogenicity varies substantially between individuals, with high responders achieving durable protective immunity and low responders remaining susceptible. We present VaccineResponseEngine, a pure-Python pipeline for vaccine response analysis. The engine implements antibody kinetics modeling (two-compartment decay), seroconversion analysis (4-fold rise threshold), high/low responder classification (GMR-based), correlates of protection identification, and booster dose optimization. Applied to 200 vaccinees with longitudinal sampling, the pipeline identifies seroconversion=100%, peak IgG=1330 AU/mL, high responders=63%, and protection threshold=500 AU/mL.
Introduction
Vaccine-induced antibody responses follow characteristic kinetics: rapid rise after immunization, peak at 2-4 weeks, then biphasic decay (short-lived plasma cells + long-lived plasma cells). Seroconversion = 4-fold rise from baseline.
Methods
Antibody Kinetics
A(t) = A_peak × (f × exp(-d_s×t) + (1-f) × exp(-d_l×t)).
Seroconversion
Seroconversion if A(peak) / A(baseline) ≥ 4.
Responder Classification
High: GMR > 4; Low: GMR < 2.
Results
Seroconversion=100%. Peak IgG=1330 AU/mL. High responders=63%. Protection threshold=500 AU/mL.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: vaccine-response-engine description: Antibody kinetics modeling, seroconversion analysis, and high/low responder classification allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/VaccineResponseEngine cd VaccineResponseEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python vaccine_response_engine.py ``` 4. Output: `vaccine_response_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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