Research

COVID-19 Research

Title :

DECOVID: Data-assimilation and error correction of viral infectious disease models

Area of research :

COVID-19 Research, Life Sciences & Biotechnology

Focus area :

Mathematical modelling for COVID-19

Principal Investigator :

Dr Deepak N Subramani, Assistant Professor, Indian Institute of Science (IISc), Bengaluru

Timeline Start Year :

2020

Contact info :

Details

Executive Summary :

The goal of the present project is to develop numerical schemes and algorithms for a Bayesian data assimilation methodology to rigorously correct forecast errors of differential equation-based viral infectious disease dynamical models, and to improve their prediction skill.

Outcome/Output:

A non-intrusive workflow is introduced, and illustrative examples from different use cases are showcased to highlight the PC-GMM filter's performance. The PC-GMM filter accurately captures the state space's non-Gaussian features, as evidenced by its superior performance compared to the PC-GMM filter with the polynomial chaos ensemble kalman filter and polynomial chaos error subspace statistical estimation.

Organizations involved