Second Covid wave to peak in April, 11 lakh cases in Karnataka by Sept: IISc

IISc study also predicts India could see total number of cases rise to over 63.5 lakh by October 9 this year 
A health worker sanitises a vaccination site in the city | Ashish Krishna HP 
A health worker sanitises a vaccination site in the city | Ashish Krishna HP 

BENGALURU: Covid-19 infections could touch 10.7 lakh in Karnataka by the end of April and rise to 11.2 lakh by September according a prediction model developed by researchers at the Indian Institute of Science. The findings from the model, developed by Prof Sashikumaar Ganesan and Prof Deepak Subramani and his team at the Department of Computational and Data Sciences at IISc, have been published in the journal Nature Sci.

The models predictions are based the assumptions that the pandemic trajectory in the second wave will follow the spread similar to that seen between March 23 and October 1 last year, sero-prevalence of 20 for each detected case (based on surveys), and factors in a daily vaccination of 30 lakh people with vaccine effectiveness at 70 percent.The model also predicts that the country could see the total number of cases rise to over 63.5 lakh by October 9 this year.

“We provide prediction of all India and state-wise confirmed, recovered, active and deceased Covid-19 cases based on our multi-dimensional PDE model. The prevalence estimates from sero-surveys have been included in the model,” explains Prof Ganesan.The prediction model is interactive and has earlier in 2020 accounted for the “best-case” scenario and the “worst-case” scenario. The present prediction, according to the researchers, will be updated soon with more categories of explanations and predictions.

Worst-case scenario

The worst-case scenario including a 20X sero-prevalence for Karnataka, according to the computational model, would be that cases rise to 12.2 lakh by April-end and 21 lakh by September.The researchers claim that the model predicts region-wise and age-wise Covid-19 spread accurately. It will soon include predictions of infected people by region, age 

of infected individuals, number of days since the start of the infection and severity over time.
The data fed into the model will include “immunity, pre-medical history, effective treatment, point-to-point movement of infected people (by air, train, etc), interactivity (spread in the community), hygiene and physical distancing”.

The researchers insist that there has to be an effective quarantine of active cases to prevent new infections and to contain the pandemic. According to them, an adaptive quarantine function in their model ensures that the infected population is quarantined based on their infection level (showing symptoms) and based on latest published literature on how the infection spreads from the infected population.

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