SUTRA Model

Context:

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.

About:

• SUTRA standing for Susceptible, Undetected, Tested (positive) and Removed Approach earlier gained a public eye when one of its expert members informed in October 2020 that India had crossed the COVID 19 peak.
• National COVID 19 Supermodel Committee was formed by the Government of India to make projections about the spread of COVID 19 in India and help in making short and long term plans to defend the country from the dangerous disease caused due to the virus. The committee, however, accepted that it was unable to predict the exact nature of the second Covid wave in the country.
• The panel was working on the SUTRA model for calculating the trajectory of the disease in India. This committee comprises three scientists namely, Manindra Agrawal, Professor, IIT Kanpur, Madhuri Kanitkar, Deputy Chief, Integrated Defense Staff and M Vidyasagar, Professor, IIT Hyderabad who work on this model.

Problems with SUTRA Model:

• Variability:
  
  • 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.

• Too Many Parameters:
  
  • 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 ‘over fitting’. You can fit any curve over a short time window with 3 or 4 parameters.

• Ignores Behaviour of the Virus:
  
  • 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.

• Ignores the Reason For Change:
  
  
  • 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.

The game-changer

The SUTRA model has been undermined due to the following reasons

1. Omission of the importance of the changing behaviour of the virus 
2. Observations informing that some people were bigger transmitters of the virus than others Lack of accounting for social or geographic heterogeneity 
3. Non-stratification of the population by age ( contacts between different age groups were not accounted for)

Way Forward:

The new variants have been put up in the SUTRA model as an increase in value of parameters called beta.  As per the scientists now “the model is observing changes in parameter values. It does not care about what is the reason behind the change. And computing new beta value is good enough for the model to predict the new trajectory well.” Due to the combination of epidemiologists, data-centric modelling such as SUTRA and time-series models can work to their best. 

Source:  The Hindu