Coronavirus: Brazilian Physicists Create Model That Predicts Mutations

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Coronavirus: Populations that are not being vaccinated and social groups that refuse to receive the coronavirus vaccine favor the emergence of variants. This is one of the main conclusions of an article by Brazilian physicists published in the scientific journal Plos One. They created a model capable of predicting coronavirus mutations based on the genetic evolution of the virus during the pandemic.

The study, carried out at the Gleb Wataghin Institute of Physics, of the State University of Campinas (IFGW-Unicamp), warns of the problem that, if not resolved urgently, could result in a new peak of cases on a global scale – which could prolong the current critical phase far beyond the expected time.

According to the study coordinator, physicist Marcus de Aguiar, a professor at the IFGW-Unicamp, during virus replication, copying errors are inevitable. If any of these errors give the virus an advantage, the mutation becomes important and may even predominate. “If the propagation occurs without restraint, due to non-vaccination, mutations tend to happen more and more and to spread across the globe”, he said in an interview to FAPESP Agency.

The danger of non-vaccination

The research leader explained that it is not the vaccination that favors the mutation, but the lack of it. In Brazil, 96% of deaths due to covid-19 are those who did not take the vaccine, according to the monitoring platform Info Tracker, developed by researchers at the University of São Paulo (USP) and the São Paulo State University Júlio de Mesquita Filho (Unesp).

Understand the model that predicts mutations in the coronavirus

The IFGW-Unicamp study model, in addition to focusing on the numbers of infected, susceptible and recovered people over time, includes a description of the RNA of the virus, which allows us to know how different the microorganisms in circulation are from the original viruses and estimate whether someone who has already been infected with the original virus could be reinfected by the variant. It also predicts whether or not the new pathogen will be able to escape the action of vaccines designed for the original virus.

The model developed is a simplified approximation of what happens in reality and was built based on the SEIR-type model, already established in epidemiology. “We adopted these simplifications in order to be able to focus on our objective, which was to study the accumulation of viral mutations during the pandemic and how different viruses can become”, explained the researcher.

“As long as an individual remains infected, the virus can mutate and be transmitted. We calculate the ‘distance’ between the original virus and the variant from the number of different nitrogenous bases they have. Our equations suggest that it is possible to predict, with epidemiological data [number of susceptible, infected and recovered], the variability of the viral population [‘average distance’ between RNA sequences], without having to have access to a huge amount of data genetic,” explained Aguiar.

To test the model, the researchers used data from the epidemic in China in early 2020. The evolution of the “average genetic distance” between the viruses that would have hypothetically emerged during that period was simulated. Comparing the result with the distances calculated from genetic data obtained locally in the same period, the forecast showed good agreement with the real data.

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