---
license: apache-2.0
language:
- en
- ha
- yo
- fr
tags:
- healthcare
- nigeria
- malaria
- environmental-health
- africa
- autoscientist
base_model: meta-llama/Llama-3.3-70B-Instruct
---

# Nigeria Malaria Health Model
### AutoScientist Challenge 2026 | Healthcare Category

**Author:** Hussein Adeiza (mabera)  
**Role:** Licensed Environmental Health Officer, Abuja Nigeria  
**Base Model:** Llama 3.3 70B  
**Fine-tuned with:** AutoScientist by Adaption Labs  

## Model Description
This is a LoRA adapter fine-tuned on Nigeria DHS malaria health data (2010–2021).
It predicts and explains malaria prevalence risk from ITN coverage, immunization
and child mortality indicators across Nigeria.

## Training Data
- Source: DHS Nigeria national surveys (2010–2021)
- Dataset: malaria parasitemia, ITN coverage, immunization, child mortality
- Quality improvement: 163.3% (Grade D → B)
- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-malaria-risk-model-prevalence-prediction

## Training Metrics
- Win rate: 65% adapted vs 35% base model
- Base model: meta-llama/Llama-3.3-70B-Instruct
- Method: LoRA (r=16, alpha=32, all-linear)
- Epochs: 1

## Key Finding
Malaria prevalence rose from 36.2% in 2018 back to 39.6% in 2021 despite
increased ITN coverage — signaling a sustained coverage gap.
Pregnant women ITN coverage is the strongest protective factor against malaria.

## Why This Matters
Nigeria carries the world's largest malaria burden. This model addresses the
gap in African epidemiological AI — built by a Licensed Environmental Health
Officer with real field experience in Abuja.

## Credits
Powered by Adaptive Data — Adaption Labs  
AutoScientist Challenge 2026