IIT Guwahati, Duke-NUS use data science to assess Covid-19 impact in India

The study is based on rise in active cases in recent times, along with the daily infection-rate (DIR) values for each state

Researchers from the Indian Institute of Technology (IIT) Guwahati, and the Duke-NUS Medical School, Singapore have carried out data-driven assessment of the Covid-19 situation in India.

The joint team has used data science models to analyse and predict the total number of infected people for different states in India in the next 30 days. 

Since a report solely based on any one model can be potentially misleading, the team has attempted to guard against this possibility by considering the exponential, the logistic, and the Susceptible Infectious Susceptible (SIS) models using open-source data. They have interpreted the results jointly from all models rather than individually.

The data-driven assessment has been carried out by Palash Ghosh, Assistant Professor, Department of Mathematics, IIT Guwahati, and his Ph D scholar Rik Ghosh, in collaboration with Bibhas Chakraborty, Associate Professor, Duke-NUS Medical School, Singapore.

The report is based on the rise in active cases in recent times, along with the daily infection-rate (DIR) values for each state. They label a state as severe if a non-decreasing trend in DIR values is observed over the last two weeks along with a near exponential growth in active infected cases and as moderate if an almost decreasing trend in DIR values is observed over the last two weeks along with neither increasing nor decreasing growth in active infected cases. 

A state is labeled as controlled if a decreasing trend in the last two weeks’ DIR values is observed along with a decreasing growth in active infected cases.

In their analysis, the logistic model under-predicts the next 30-day prediction, whereas the exponential model over-predicts the same, reflecting the worst-case scenario. Despite the nation-wide lockdown, the report has argued that people are still out of home for essential businesses, which can contribute to the spreading of the virus. 

The maximum value of DIR in the last two weeks can capture how severely the COVID-19 is spreading in recent times. In an attempt to capture these various subtleties in a realistic prediction, they propose a combination of the logistic and the exponential predictions using the maximum value of DIR over the last two weeks as a weighting factor. 

Meanwhile, given the situation in entire India, the team recommends this composite prediction to be used for assessment purposes for each state. States that are in severe category need to do much more in terms of the preventive measures immediately to combat the COVID-19 pandemic.