Researchers from India's Indian Institute of Science (IISc) and Australia's Queensland Brain Institute (QBI) have devised a mathematical model to anticipate how antibodies produced by COVID-19 vaccinations protect against symptomatic infections.

This methodology attempts to make the best use of existing Covid-19 vaccines while also accelerating the development of new ones.

The need for such a model arose from the fact that different vaccines had varying degrees of efficacy.

The research was published in the journal Nature Computational Science. The researchers looked at approximately 80 different neutralising antibodies that were known to be produced following vaccination against SARS-CoV-2, the virus that causes Covid-19.

These antibodies can stay in the blood for months and block the spike protein, preventing viral entry.

According to the researchers, these 80 antibodies form a landscape, and each person creates a unique profile of antibodies that is a small, random subset of this landscape.

According to an IISc announcement, the team then created a mathematical model to simulate illnesses in a virtual patient population of roughly 3,500 people with various antibody profiles and forecast how many of them would be protected from symptomatic infection following vaccination.

"The reason why predicting vaccine efficacy has been hard is that the processes involved are complex and operate at many interconnected levels," reported The New Indian Express quoting Prof Narendra Dixit of the Department of Chemical Engineering, IISc, and senior author of the study,

"Vaccines trigger a number of antibodies, each affecting virus growth in the body differently. This, in turn, affects the dynamics of the infection and the severity of the associated symptoms. Further, different individuals generate different collections of antibodies and in different amounts," he added.