mabera/global-vaccine-equity-interpreter

Global Vaccine Equity & Data Reliability Interpreter

Author: Hussein Adeiza (mabera) Role: Licensed Environmental Health Officer, Abuja Nigeria Base Model: Mixtral 8x7B Fine-tuned with: AutoScientist by Adaption Labs

Model Description

A LoRA adapter fine-tuned to interpret real global COVID-19 vaccination and mortality data, with careful attention to responsible epidemiological framing. Given the sensitivity of real pandemic mortality figures, every completion explicitly addresses testing-capacity and data-reliability limitations rather than taking raw numbers at face value.

Honest Disclosure: A Modest Result

This is the weakest win rate in this author's 20-submission AutoScientist portfolio, disclosed transparently rather than omitted:

• Win rate (on dataset): 55% adapted vs 45% base
• General Win Rate (Science domain): 50% adapted vs 50% base, a dead tie

Despite careful dataset construction and one of the most rigorously verified, responsibly-framed datasets in this portfolio, the fine-tuned model showed minimal differentiation from the base model on unseen Science tasks. A plausible explanation: Mixtral 8x7B may already handle careful epidemiological reasoning reasonably well out of the box, given extensive public discourse on COVID-19 data reliability during model pretraining, leaving less room for fine-tuning to demonstrate uplift. This is shared honestly as a genuine, useful data point, not every carefully-built dataset produces a strong training signal, and that itself is worth knowing.

Training Data

• Source: Our World in Data, owid/covid-19-data (GitHub), downloaded directly, 429,435 rows globally
• Dataset: 5 original prompt-completion pairs, every number computed via pandas directly from the raw source file, expanded via Adaptive Data (Hallucination Mitigation, full 20K+ datapoint expansion)
• Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/global-vaccine-equity-interpreter

Verification

All 5 rows independently, programmatically verified against the raw source file before training. 5/5 confirmed, demonstrated live in the accompanying Kaggle notebook.

Key Cited Findings (from the raw downloaded source only)

• Nigeria's vaccination coverage (37.2%) sits well below the WHO's 70% target and high-income comparators
• Nigeria's COVID-19 testing rate is roughly 300x lower than the UK's, meaning confirmed death rate comparisons alone would significantly understate true pandemic impact
• South Africa's excess mortality (5,115.79/million) exceeds both the US and UK, despite a lower confirmed death rate, demonstrating why excess mortality is the more reliable indicator where available
• Excess mortality data itself is unavailable for Nigeria, Kenya, Ghana, and the Africa aggregate, a genuine data gap that compounds the difficulty of assessing true pandemic impact in these countries

Credits

Powered by Adaptive Data — Adaption Labs AutoScientist Challenge 2026, Part 2 — Science Category (bonus submission)