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Researchers of IIT-G, Singapore institute use data science models

By Staff Reporter - 15 Sep 2010 12:00 AM GMT

GUWAHATI, May 12 - Researchers at the Indian Institute of Technology (IIT) Guwahati and the Duke-NUS Medical School of Singapore have used data science models to analyse and carry out a state-wise assessment of the COVID-19 situation in India.

The data-driven assessment was carried out by Dr Palash Ghosh, Assistant Professor in the Department of Mathematics of IIT Guwahati (IIT-G) and his PhD scholar Rik Ghosh, in collaboration with Dr Bibhas Chakraborty, Associate Professor at the Duke-NUS Medical School, Singapore.

The assessment can predict the total number of infected people for different states in India in the next 30 days. Their report is based on the growth of active cases in recent times, along with the daily infection-rate (DIR) values for each state.

�India is a vast country with a total population of about 1.3 billion. Most of the Indian states are quite large in geographic area and population. While analysing the novel coronavirus infection data, considering our entire country to be on the same page may not reveal the right picture. This is so because the first infection, new infection-rate, progression over time, and preventive measures taken by various state governments and the common public for each State are different. We need to address each state separately. It will enable the government/governments to utilise the limited available resources optimally,� said Dr Ghosh.

The researchers 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, 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, and as controlled if a decreasing trend in the last two weeks� DIR values is observed along with a decreasing growth in active infected cases.

They have released a 30-day prediction for some states with enough data for prediction. However, none of the north-eastern states is in the list.

�A report solely based on any one model can potentially mislead us. In an attempt to guard against this possibility, we have considered the exponential, the logistic, and the Susceptible Infectious Susceptible (SIS) models, along with the model-free DIR using open-source data. We have interpreted the results jointly from all models rather than individually,� said Dr Ghosh.