New Simulation Helps To Understand the Spread of New Variants

Researchers at Tokyo Metropolitan University have made numerical simulations based on network theory, which shows how the number of infections changes when a new variant of COVID-19 emerges.

They found that there was a nonlinear dependence between how new the variant was and how contagious it was compared to the existing ones, which had not been done in previous work. Their model can be used to understand real pandemic situations like COVID-19 and to report control measures.

‘There was a nonlinear dependence between how new the variant was and how contagious it was compared to the existing ones.’

COVID-19 pandemic began its spreading in the year 2019 and had a devastating impact on people's lives. As the waves of new variants continue to wreak havoc around the world, scientists are looking for ways to understand how the disease spreads. In particular, there is the problem of how new variants appear, spread and replace existing ones. Understanding the dynamics of variants is essential in controlling their spread.

A classic framework for modeling epidemiological dynamics is the "compartmental" SIR model, which looks at the number of susceptible (S), infected (I) and recovered (R) members in a population. Numbers are related by equations and solved, providing several key features of how a disease spreads; as the number of susceptible patients decreases and more patients recover, the infection spreads faster before it subsides.

However, the model does not measure population diversity, meaning that a given infected person does not have the same probability of infecting everyone else, and the number of contacts that people have varies greatly from one person to another. Any model that attempts to capture pandemic dynamics and captures where and how it spreads should use a more sophisticated model.

Emeritus Professor Yutaka Okabe and Professor Akira Shudo from Tokyo Metropolitan University shows that why they have turned to network theory, a mathematical framework that is able to capture how different members of a population seemed to connect. Using different types of networks, they were able to develop a more realistic model of how an infection can spread.

Key features include dynamic suction levels, levels of network entanglement over time, for example, state without victims. With a few infections and low infectivity, the network will collapse back to a non-infectious state.

The team performed numerical simulation of the microscopic model on the group network; in the midst of a simulation of an infectious disease, they have added a variant that is more contagious than the original strain.

Looking at the numbers, the team found that a variant with the same contagion of the current strain actually failed to take off. This is a direct result of the non-linear nature of the simulation as the network returns to the suction state without infections, that is, without COVID-19 pandemic.

As the infectivity of the new variant increases, the population is more likely to be affected by the variant as opposed to the existing strain, which increases the rate for the new strain at the expense of the old variant. The nonlinear nature of how infection numbers increase with variant infectivity is a product of the microscopic nature of the network model, giving a more comprehensive, nuanced picture than ever before.

The team hopes that their model can be used to develop effective strategies for controlling infectious diseases, to look at significant connection points in the network and to understand how their isolation affects overall infections. As the COVID-19 pandemic continues to rage, fundamental studies of how diseases spread are an important part of informed decision making aimed at bringing normal life back to the community.

Source: Medindia