This project focuses on predicting disease outbreaks with a particular emphasis on the novel coronavirus (COVID-19) as a case study. We utilized time-series analysis techniques such as ARIMA, Prophet, and LSTM, alongside classification methods including decision trees, random forests, and neural networks to forecast and categorize outbreak risks.
anikshar@iu.edujhiremat@iu.edusavinn@iu.eduWe developed predictive models to classify countries into low, moderate, and high-risk categories based on their healthcare infrastructure and socio-economic metrics, using machine learning algorithms. The methodologies are adaptable to other infectious diseases, making this a versatile tool in global health crisis management.
git clone https://github.com/aniket2468/Disease-Outbreaks-Prediction.gitpip install -r requirements.txtThe primary datasets include:
Run the Jupyter notebook to execute the analysis:
jupyter notebook Disease Prediction.ipynb
We employed a variety of forecasting and classification techniques:
The models were able to effectively predict and classify the risk levels of different countries, assisting in strategic planning for healthcare resource allocation.
The findings underscore the utility of predictive analytics in public health, particularly for pandemic preparedness. Future enhancements will focus on integrating more granular data and sophisticated algorithms.