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

Implementing Organization

Indian Institute of Science
Principal Investigator
Dr Deepak N Subramani
Assistant Professor
|
Indian Institute of Science

About

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.

Patents

NA

Achievements

NA

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.

Publications

NA

Related Research

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Funding Organization
Funding Organization
Department of Science and Technology (DST)
Quick Information
Focus Area
Mathematical modelling for COVID-19
Start Year
2020
Sanction Amount
₹ NA
Status
Ongoing
Contact
deepakns@iisc.ac.in
Output
No. of Research Paper
00
Technologies (If Any)
00
No. of PhD Produced
NA
No. of Patents
Filed : 00
Grant : 00
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