Yes, I know—both words start with an F. But a new study shows that they are also contagious.

From the press release:

Mathematician and assistant professor of biology Nicholas Landry, an expert in the study of contagion, is exploring how the structure of human-interaction networks affect the spread of both illness and information with the aim of understanding the role social connections play in not only the transmission of disease but also the spread of ideas and ideology.

In a paper published this fall in Physical Review E with collaborators at the University of Vermont, Landry explores a hybrid approach to understanding social networks that involves inferring not just social contacts but also the rules that govern how contagion and information spread.

“With the pandemic, we have more data than we’ve ever had on diseases,” Landry said.

“The question is, What can we do with that data and how much data do you need to figure out how people are connected?”

The key to making use of the data, Landry explained, is to understand their limitations and understand how much confidence we can have when using epidemic models to make predictions.

Landry’s findings suggest that reconstructing underlying social networks and their impacts on contagion is much more feasible for diseases like SARS-CoV-2, Mpox or rhinovirus but may be less effective in understanding how more highly infectious diseases like measles or chickenpox spread.

However, for extremely viral trends or information, Landry suggests it may be possible to track how they spread with more precision than we can achieve for diseases, a discovery that will better inform future efforts to understand the pathways of both contagion and misinformation.

Abstract of the study:

Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise.