SUTRA is Susceptible, Undetected, Tested (positive) and Removed Approach. SUTRA is used to chart the trajectory of COVID-19. It is used to predict the future of COVID-19 infections, susceptibility, undetected and positive persons in the country.
This SUTRA Model is backed by GoI and was developed by professors from IIT Kanpur, IIT Hyderabad.
What is the issue?
The SUTRA model predicted in March 2021, that the second wave of COVID-19 is to peak in the third week of April. Also, it is likely to add one lakh cases per day. There are reports claiming that attention was not paid to this. According to Ministry of Science and Technology, these predictions were incorrect. The scientists who developed the model have come forward to explain that these claims are incorrect for the following reasons:
- The model can predict the future only if the virus dynamics and its transmissibility do not change. In case of COVID-19, the nature of the virus has been changing continuously.
Similarly, the model lags in several other things a well.
What are the flaws in the SUTRA Model?
- The model was incapable to predict the future under different scenarios. However, its predictions till February 2021 fitted in perfectly.
- Unlike other models that extrapolated (to predict by projecting the past experiences) cases based on behaviour of the virus, existing number of cases, and the manner of spread, SUTRA chose “Data Centric Approach”.
- The calibrations made were not correct. The calibrations of the model relied on the Serosurvey conducted by ICMR in May 2021. According to the survey, 0.73% of the Indian population were infected with Corona Virus, which is not correct.
- The equation that gave out the estimates of number of future infections and when a peak may occur needed constants. These constants relied on number of infections being reported at various intervals. The equations couldn’t say when the constants changed. Bottom line, the model failed to report rapid acceleration of cases in advance.
- The SUTRA Model was problematic as it relied on too many parameters. Too many parameters put the model into the danger of “overfitting”.
- It did not include the fact that some people were the bigger transmitters of the virus such as barbers or receptionists.