PDE-based modelling of COVID-19 infections

Team Lead: Sashikumaar Ganesan and Deepak Subramani
Email: sashi@iisc.ac.in; deepakns@iisc.ac.in

Summary

The aim of this project is to develop and apply a predictive computational model for the Covid-19 epidemic based on a high-dimensional population balance modelling.

Key features

• A six-dimensional population balance predictive computational model for an epidemic.
• Unlike the existing (Compartment or Network) models, proposed model predicts the distribution of infected population across the region, the age of the infected people, the day since infection, and the severity of infection, over a period of time.
• Incorporates the immunity, pre-medical history, effective treatment, point-to-point movement of infected population (e.g., by air, train etc), interactivity (community spread), hygiene and the social distancing of the population.
• Finite element operator-splitting scheme for the high-dimensional PDE.
• Proposed model can be used to predict region-wise and age-wise COVID-19 spread accurately, and consequently it can be used to frame policies on periodic lockdown, staggered opening of educational institutions and public facilities.

This model is based on a high-dimensional population balance equation. Unlike the existing (Compartment or Network) pandemic models, the proposed model predicts the distribution of infected population across the region, the age of the infected people, the day since infection, and the severity of infection, over a period of time. Moreover, the newly developed model incorporates the immunity, pre-medical history, effective treatment, point-to-point movement of infected population (e.g., by air, train etc), interactivity (community spread), hygiene and the social distancing of the population.

A scenario analysis of COVID-19 pandemic in India using the proposed model can be found at https://cmg.cds.iisc.ac.in/covid/