Changes in climatic condition, air pollution and population growth have a measurable impact on the local conditions and the specific ecology and epidemiology of different contagious diseases (airborne, vector-borne and water-borne). This project aims at using AI-based machine learning and Machine Learning techniques for understanding the spread of Tuberculosis in India. The environmental factors such as Rainfall, Average Temperature, Relative Humidity and pollution parameters such as SO2, NO2 and RSPM were taken into consideration in order to perform analysis.
Objective
- Understand the impact through correlation of the factors such as climatic conditions (rainfall, humidity, temperature etc.), pollution parameter (Sulphur Dioxide, Nitrogen Dioxide, Respirable suspended particulate matter etc.) and population density on TB.
- Help understand the spread and propagation pattern for the prevention of new TB cases.
- Predict propagation based on the denominator factors for early detection.
Website URL: https://dmwebapplication.pythonanywhere.com/