Projections for confirmed, recovered, active, and deceased Covid-19 cases in Karnataka are provided. The prevalence estimates from serosurveys have been included in the model. However, the scenario curves below show only the officially tested cases for Covid-19.
Model predictions updated/issued on Apr 27, 2021 .

Assumptions of COVID-19 Wave II Model

• 1.7L vaccinated every day, and the vaccine is 70% effective
• Two unreported cases for every reported case
• Lockdown-like restrictions: local lockdown, city lockdown, strict curfew, etc. "Social distance" parameter in the model is fitted similar to the previous year (2020) lockdown
• Un-lock: Social distance parameter is restored to post-lockdown values of 2020
• No inter-state transmission of COVID-19, including air travel

Scenario Analysis

• S0: No Lockdown - worst-case
• S1: Scenario I - 30-day lockdown from 27th April with 50% effectiveness
• S2: Scenario II - 30-day lockdown from 27th April

Computational Model

• Data from 23 March, 2021 is used partially to tune the parameters of the data-driven model. These results are current as of Apr 26, 2021.
• The severity of the infection is taken into consideration while modeling the infectious death rate function (see the rate functions in the model).
• What if the lockdown-like restrictions are not followed as in 2020?: All projections will follow the No-Lockdown scenario.
• What if the post-lockdown "social distance" parameter values follow the pre-lockdown values of 2021 instead of the post-lockdown values of 2020? See, KA Lockdown Scenario Analysis II
• What if the restrictions, inoculation, recovery, and herd immunity improve/worsen compare to the present situation? One can claim that the mathematical model has failed and proven wrong. In reality, we failed the mathematical model by feeding incorrect parameters.
• District-wise numbers are computed with the Karnataka parameters to compare the actual data of the respective state with the Karnataka's trend. Hence, Districts' curves are not projections but indicate how the individual District got affected (better/worse) than the Karnataka average.

Acknowledgements

• Prof. Siva Athreya, ISI, Bangalore
• Google Data Studio
• Special thanks to all well wishers and colleagues for the discussion and feedback on our model.
• Deepak wishes to acknowledge DST Inspire and Arcot Ramachandran Young Investigator Awards.
• Sashi wishes to acknowledge SERB, DST, DRDO, DAAD and AvH for the grants that supported for the development of ParMooN.