COVID-19 Modeling for India and a Roadmap for the Future

A NUMBER OF models have been developed in India to forecast the spread of the coronavirus disease or COVID-19 in the country. While these have largely been variants of the classical susceptible-exposed-infectious-recovered (SEIR) compartmental model, other approaches using time-series analysis, machine-learning, network models, and agent-based simulations have also helped to provide specific insights into questions of policy. Model building has had to incorporate our evolving knowledge of the disease, including the appearance of new variants, immune escape leading to reinfections, time-varying non-pharmaceutical interventions, the pace of the vaccination program, and breakthrough infections. The predictive power of these models has been hampered by the lack of availability of quality data

Automated summary

Several models have been developed in India to predict the spread of COVID-19. These models include variants of the classical SEIR model as well as approaches like time-series analysis, machine-learning, network models, and agent-based simulations. However, the lack of quality data has hindered the predictive power of these models.