This repository contains the complete process of computational biogeographic modeling of the distribution/ecological niche of Aedes aegypti (yellow fever) mosquitoes using a correlative machine learning algorithm called Maximum Entropy (MaxEnt) in Python using the Elapid package. The project leverages spatial occurrence data, and bioclimatic & environmental parameters to predict global suitable habitats for this important disease vector. All the modeling code is provided through a Jupyter Notebook for interactive analysis and modeling experimentation.
- Species Distribution Modeling (SDM): Use Maxent to model Aedes aegypti distribution.
- Data Processing: π οΈ Tools, scripts, and notebooks for processing environmental and occurrence data.
- Interactive Notebooks: π Jupyter Notebook(s) containing the full modeling code for interactive data analysis.
- Visualization: π Generate maps and plots to visualize predicted distributions.
- Reproducibility: π Clear documentation and reproducible computational workflows.
- Python 3.8 or later π
- Jupyter Notebook or JupyterLab installed π
- Required Python packages listed in
requirements.txtπ©π½βπ» - QGIS software for spatial data mapping/visualization πΊοΈ
- Clone the repository:
git clone https://github.com/kanjibrian/Maxent_Aedes_aegypti.git
- Navigate to the repository:
cd Maxent_Aedes_aegypti - Install the dependencies:
pip install -r requirements.txt
- Launch the Jupyter Notebook:
jupyter notebook
- Open and run the notebook(s) to perform data preprocessing, run the Maxent model, and visualize the results.
Maxent_Aedes_aegypti/
βββ data/ # Environmental and mosquito occurrence data π
βββ code/ # Jupyter Notebook(s) containing the modeling code π
βββ results/ # Output results and maps πΊοΈ
βββ requirements.txt # List of Python dependencies π
βββ README.md # This README file π
- Dylan Komi Mensah - Conceptualization & Supervision
- Kanji Brian - Analysis & Modelling
- Quinto Juma - Geospatial mapping
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request with your enhancements, corrections, or bug fixes.
Happy geospatial modeling and mapping!
The original project contributors gratefully acknowledge...
