Are ideas contagious? How the structure of human-interaction networks affects spread of both illness and information

October 9

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Overview of the problem. (a) A contagion spreads on a network for 𝑇 time steps, and we observe the resulting sequence of states 𝑿. The probability that a susceptible node (white) becomes infected (red) at the next time step is a function 𝑐⁡(𝜈) of the number of infected neighbors it has, e.g., 𝜈=4 for the square node highlighted in blue. (b) We compute a nonparametric Bayesian estimate of the contagion function 𝑐⁡(𝜈). Here, we show an estimate of 𝑐⁡(𝜈) obtained from a single short realization of the dynamics when the network is known. Error bars show the 50% highest-density posterior interval (HDPI) of 𝑐⁡(𝜈). (c) We estimate the network and the contagion function 𝑐⁡(𝜈) simultaneously using the marginals of the posterior distribution, Eq. (7). The reconstruction error goes to 0 as the amount of data 𝑇 goes to infinity. The shaded regions indicate the 50% HDPI, and lines show the median AUROC across 103 repetitions. (d) The reconstruction quality is determined by the shape of the contagion function, here demonstrated by varying its overall infectivity 𝛽 and the level of complexity 𝜔∈[0,1]. We use the parametrization 𝑐⁡(𝜈,𝛽,𝜔)=(1−𝜔)⁢𝑔+𝜔⁢ℎ, where 𝑔⁡(𝜈,𝛽)=1−(1−𝛽)𝜈 describes a simple contagion model, and ℎ⁡(𝜈,𝛽)=𝛽⁢1𝜈≥2 describes a complex threshold model. Credit: Physical Review E (2024). DOI: 10.1103/PhysRevE.110.L042301

The COVID-19 pandemic gave the global medical community the opportunity to take giant strides forward in understanding how to develop vaccines and implement public health measures designed to control the spread of disease, but the crisis also offered researchers the chance to learn more about another kind of contagion: ideas.

Mathematician and assistant professor of biology Nicholas Landry, an expert in the study of contagion, is exploring how the structure of human-interaction networks affects 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.

More information:
Nicholas W. Landry et al, Reconstructing networks from simple and complex contagions, Physical Review E (2024). DOI: 10.1103/PhysRevE.110.L042301. On arXiv: DOI: 10.48550/arxiv.2405.00129

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University of Virginia

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Are ideas contagious? How the structure of human-interaction networks affects spread of both illness and information (2024, October 9)
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