Life Sciences & Biotechnology
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 : | deepakns@iisc.ac.in |
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
