Maxent_Aedes_aegypti 🦟🌍

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.

Features ✨

• Species Distribution Modeling (SDM): Use Maxent to model Aedes aegypti distribution.
• Data Processing: 🛠️ Tools, scripts, and notebooks for processing environmental and occurrence data.
• Interactive Notebooks: 📓 Jupyter Notebook(s) containing the full modeling code for interactive data analysis.
• Visualization: 📊 Generate maps and plots to visualize predicted distributions.
• Reproducibility: 🔄 Clear documentation and reproducible computational workflows.

Sample Results 🎉

Getting Started 🚀

Prerequisites

• Python 3.8 or later 🐍
• Jupyter Notebook or JupyterLab installed 📓
• Required Python packages listed in requirements.txt 👩🏽‍💻
• QGIS software for spatial data mapping/visualization 🗺️

Reproducibility

1. Clone the repository:

   git clone https://github.com/kanjibrian/Maxent_Aedes_aegypti.git

2. Navigate to the repository:

   cd Maxent_Aedes_aegypti

3. Install the dependencies:

   pip install -r requirements.txt

Usage

• Launch the Jupyter Notebook:

  jupyter notebook

• Open and run the notebook(s) to perform data preprocessing, run the Maxent model, and visualize the results.

Project Structure 🗂️

Maxent_Aedes_aegypti/
├── data/                     # Environmental and mosquito occurrence data 📁
├── code/                     # Jupyter Notebook(s) containing the modeling code 📓
├── results/                  # Output results and maps 🗺️
├── requirements.txt          # List of Python dependencies 🐍
└── README.md                 # This README file 📖

Original Contributors 👷🏽‍♂️

• Dylan Komi Mensah - Conceptualization & Supervision
• Kanji Brian - Analysis & Modelling
• Quinto Juma - Geospatial mapping

Contributing 🤝

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!

Acknowledgments 🌟

The original project contributors gratefully acknowledge...