climaid 0.1.1

Climate Change impact using AI on Diseases (ClimAID) Toolkit for Climate-Driven Disease Modelling

ClimAID - Climate Change impact using AI on Diseases

ClimAID is an integrated toolkit for modeling, forecasting, and projecting climate-sensitive diseases such as dengue and malaria using machine learning and climate model ensembles.

• ClimAID has inbuilt climate data for South Asian countries, namely India, Nepal, Bhutan, Sri Lanka, Myanmar, Afghanistan, Pakistan and Bangladesh.

• ClimAID support data from other countries through the global mode on the browser interface.

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What you can do

• Analyze historical disease patterns
• Integrate climate variables (temperature, rainfall, humidity)
• Automatically detect optimal lag effects using AutoML
• Train hybrid ML models
• Generate CMIP6-based future projections
• Identify outbreak risk under climate change scenarios
• Generate automated policy reports using the integrated C-DSI or local LLM models.

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Quick Example using Codes

from climaid.climaid_model import DiseaseModel

dm = DiseaseModel(
    district='IND_Mumbai_MAHARASHTRA',
    disease_file="dengue_data.xlsx",
    disease_name="Dengue"
)

dm.optimize_lags()
dm.train_final_model()

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Workflow

ClimAID has two interfaces,

• ClimAID Browser Interface (For both South Asian + Global countries)

  For initialisation through terminal, use

      climaid browse
  

• ClimAID Wizard Interface (For South Asian countries)

  For initialisation through terminal, use

      climaid wizard
  

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Documentation

The full documentation is available here: https://sam-as.github.io/ClimAID/

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Designed for

• Epidemiologists
• Climate scientists
• Public health analysts
• Data scientists

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Dependencies & License

Dependencies

• Core Requirements

  pandas  
  numpy  
  geopandas  
  matplotlib  
  scikit-learn  
  xarray  
  regionmask  
  plotly  
  xgboost  
  optuna
  

• Additional Utilities

  These packages support extended functionality and will be auto-installed.

  requests
  joblib
  fastapi
  uvicorn
  typer
  markdown
  fastparquet
  python-multipart
  seaborn
  openpyxl
  

• Optional (LLM Support)

  To enable local LLM-based report generation:

  pip install climaid[full]
  

  Includes:

  ollama 
  

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License

Designed by Avik Kumar Sam & Harish C. Phuleria as an open-access software.

• MIT License Summary

  • Free to use, modify, and distribute
  • Suitable for research and commercial use
  • No warranty is provided
  • Attribution is required

• Full License Text

  • See the complete license here: https://github.com/sam-as/ClimAID/blob/main/LICENSE

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