Experts see flaws in SUTRA’s approach to forecast pandemic
Q. What is SUTRA Model?
Scientists from the IITs of Kanpur and Hyderabad have applied the SUTRA Model to predict the Covid graph in India.
SUTRA (Susceptible, Undetected, Tested (positive), and Removed Approach) first came into public attention when one of its expert members announced in October that India was “past its peak”.
Unlike many epidemiological models that extrapolated cases based on the existing number of cases, the behaviour of the virus and manner of spread, the SUTRA model chose a “data centric approach”.
However, the surge in the second wave was several times what any of the modellers had predicted.
The predictions of the SUTRA model were too variable to guide government policy.
Q. What is the parameter on which this model is based on? A.
The model uses three main parameters to predict the course of the pandemic which are :
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.
Reach: It is a measure of the exposure level of the population to the pandemic.
Epsilon: It is the ratio of detected and undetected cases.
Q. So, what went wrong in the model
A.
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.
One of the main reasons for the model not gauging an impending, exponential rise was that a constant indicating contact between people and populations went wrong.
Further the model was ‘calibrated’ incorrectly.
The model relied on a serosurvey conducted by the ICMR in May that said 0.73% of India’s population may have been infected at that time.
This calibration led our model to the conclusion that more than 50% population was immune by January.
The SUTRA model’s omission of the importance of the behaviour of the virus; the fact that some people were bigger transmitters; 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.
