This model requires the use of two separate models in search of the best parameters for a big data-set. Uses:
- Progressive Partial Derivative Linear Regression model
- Nonlinear Global Pandemic Machine Learning model

PDR-NML

These models are necessary to measure the spread and outcome of the disease in a population. The following methods are have been used:
- Partial derivative regression and nonlinear machine learning

University of California, Santa Cruz

Machine Learning models can provide healthcare systems with a forecast of patient outcomes, risk of hospitalization, and probability of complications ranging from respiratory distress to organ failure. The model takes in blood samples and CT scan datasets to develop machine learning models of high risk patients. 

At high speed, these tools (logistic and FbProphet models) also observe trends in the accumulated epidemiological data with which they predict how the future epidemic curve will look like. It's determined that by end of October, approximately 14.1 million people will be infected, in a cumulative aspect. 


Pandemic Predicitions

Kavadi, M. D. P., Patan, R., Ramachandran, M., & Gandomi, A. H. (2020). Partial derivative nonlinear global pandemic machine learning prediction of covid 19. Chaos, Solitons & Fractals, 110056. https://doi.org/10.1016/j.chaos.2020.110056

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