COVID-19 will be under control in India by end of May, says study
The prediction by the Singapore University of Technology and Design (SUTD) also says that globally, the number of infections is falling and 97% of the pandemic will be over by the end of May.
The COVID-19 pandemic will come to an almost complete halt in India by the end of May, predicts a new epidemic model by the Singapore University of Technology and Design (SUTD).
According to the model, the country is now facing the peak of infections, and the number of cases will fall in the coming days. About 97% of the pandemic will be over by May 21, and India will be completely free of COVID-19 by July 25.
The prediction, based on a SIR (susceptible-infected-recovered) model, also says that globally, the number of infections is falling and 97% of the pandemic will be over by the end of May.
Currently, over 2.89 million people have been infected across the world, leading to over 202,000 deaths. India has reported over 26,000 cases and 824 deaths.
Countries like the US, UK, Canada, Italy, Saudi Arabia and Spain will also see a 97% completion in the epidemic in May itself, the model predicts.
The model uses data collected by Our World in Data to “estimate the pandemic life cycle curve and predict when the pandemic might end in different countries and in the world,” says the STUD website.
How accurate are these predictions?
“Mathematical models of how infections spread are simplified versions of reality. They are designed to mimic the main features of real-world disease spread well enough to make predictions which can, at least partly, be trusted enough to make decisions,” says Lester Caudill, Professor of Mathematics, University of Richmond.
But in the case of COVID-19, there is so much we are yet to know about the real world feature of the disease spread, as this is the first time we are faced with the disease.
“What are all the different ways it can be transferred between people? How long does it live on door knobs or Amazon boxes? How much time passes from the moment the virus enters a person’s body until that person is able to transmit it to someone else? These, and many other questions, are important to incorporate into a reliable model of COVID-19 infections. Yet people simply do not know the answers yet,” says Professor Caudill.
The SIR model – a simple model in which S, I and R represent the number of susceptible, infected, and recovered individuals – has been in use since 1927 to predict the course of a variety of diseases, especially airborne childhood diseases with lifelong immunity upon recovery, such as measles, mumps, rubella, and pertussis.
Asiaville spoke to Professor N.M. Anoop Krishnan of IIT Delhi, who works on computational modelling and who was part of a team that has created a national level COVID-19 prediction model for India. He says the STUD model predictions may not be very accurate.
“They have taken the total country level data, computed the average R0 and predicted the total curve using a traditional SIR model,” he says. (R0 is the number that indicated how many people an infected person would further infect. A R0 of 2 means a COVID-19 patient will infect 2 people on an average in that a region.)
He says that the model only gives a crude estimate of when the pandemic will be over if there’s no intervention. “It’ll not help in any planning of the intervention strategy,” he says.
The creators of STUD’s model themselves say that the forecasts are only for educational and research purposes and they are only as good as the data behind the prediction.
“The model is data-driven and is a simple picture of the epidemic. It works well for countries where the epidemic is controlled by quarantine i.e. where assumptions of the model are fulfilled,” says Milan Batista who created codes for the modelling.
“The model may fail in the initial phase and in when additional epidemic stages or outbreaks (not described by SIR model) are encountered. Use it at your own discretion,” he says.