• Many scientists are blaming the government-backed model, called SUTRA (Susceptible, Undetected, Tested (positive), and Removed Approach), for having a larger role in creating the perception that a second wave of Covid was unlikely in India.
  • The second wave of Covid-19 has claimed thousands of lives since April 2021.

Important points:

  • Scientists from the IITs of Kanpur and Hyderabad have applied the SUTRA Model to predict the Covid graph in India.
  • It first came into public attention when one of its expert members announced in October 2020 that India was “past its peak”.

The model uses three main parameters to predict the course of the pandemic which are :

  1. Beta: Also called contact rate, which measures how many people an infected person infects per day. It is related to the R0 value, which is the number of people an infected person spreads the virus to over the course of their infection.
  2. Reach: It is a measure of the exposure level of the population to the pandemic.
  3. Epsilon: It is the ratio of detected and undetected cases.

Problems with SUTRA:

  • There have been many instances of the SUTRA forecasts being far out of bounds of the actual caseload and the predictions of the SUTRA model are too variable to guide government policy.
  • The SUTRA model was problematic as it relied on too many parameters, and recalibrated those parameters whenever its predictions “broke down”.
  • The more parameters you have, the more you are in danger of ‘overfitting’. You can fit any curve over a short time window with 3 or 4 parameters.
  • The SUTRA model’s omission of the importance of the behaviour of the virus; the fact that some people were bigger transmitters of the virus than others (say a barber or a receptionist more than someone who worked from home); a lack of accounting for social or geographic heterogeneity and not stratifying the population by age as it didn’t account for contacts between different age groups also undermined its validity.
  • New variants showed up in the SUTRA model as an increase in value of parameters called ‘beta’ (that estimated contact rate).
  • As far as the model is concerned, it is observing changes in parameter values. It does not care about what is the reason behind the change.


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