Emmylytics/nigeria-diphtheria-outbreak-2022-2024

State-level epidemiological analysis and exploratory machine-learning classification of Nigeria's 2022–2024 diphtheria outbreak. # 🦠 State-Level Epidemiological Analysis and Exploratory Machine-Learning Classification of Nigeria's 2022–2024 Diphtheria Outbreak --- ## 📖 Overview This repository contains the complete analytical workflow accompanying the preprint: > **State-Level Epidemiological Analysis and Exploratory Machine-Learning Classification of Nigeria's 2022–2024 Diphtheria Outbreak** The study investigates the spatial distribution of diphtheria across Nigeria between 2022 and 2024 and examines the relationship between routine childhood immunisation coverage (DTP3) and outbreak burden using descriptive epidemiology, inferential statistics, geospatial analysis, and exploratory machine-learning methods. All analyses, figures, and statistical outputs presented in the manuscript can be reproduced from the notebook contained in this repository. --- ## 🎯 Objectives This study aimed to: - Describe the epidemiological distribution of diphtheria across Nigerian states. - Examine the association between DTP3 vaccination coverage and outbreak burden. - Identify regional and spatial disparities in disease distribution. - Evaluate the utility of exploratory machine-learning models for classifying high-burden states. - Produce reproducible analytical workflows for future epidemiological investigations. --- ## 📂 Repository Structure ``` . ├── data/ │ ├── Nigeria_Diphtheria_Dataset.xlsx │ └── gadm41_NGA_shp/ │ ├── gadm41_NGA_0.* │ ├── gadm41_NGA_1.* │ └── gadm41_NGA_2.* ├── notebooks/ │ └── diphtheria_outbreak_analysis.ipynb ├── paper_figures/ │ ├── Figure1_Epidemic_Curve.png │ ├── Figure2_Top10_Cases_Confirmed.png │ ├── Figure3_Top10_CFR.png │ ├── Figure4_DTP3_vs_Burden.png │ ├── Figure5_Distribution_Across_Zones.png │ ├── Figure6_Incidence_Rate_Map.png │ ├── Figure7_Vaccination_Gap_Map.png │ └── Figure8_RF_Feature_Importance.png ├── requirements.txt ├── README.md └── .gitignore ``` --- ## 📊 Data Sources The analytical dataset was compiled from …