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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
Published on: October 20, 2022
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Nonlinear bias toward complex contagion in uncertain transmission settings
Guillaume St-Onge1, Laurent Hébert-Dufresne2,3,4, Antoine Allard2,4,5
1Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115.
Summary
Mathematical models of epidemics struggle with varied transmission risks. This study shows that ignoring group size and risk heterogeneity can falsely suggest complex contagion dynamics, even in simple linear contagion processes.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Standard epidemic models often oversimplify transmission dynamics.
- Real-world epidemics exhibit complex transmission patterns due to heterogeneous group sizes and varying infection risks (e.g., indoor vs. outdoor gatherings).
- Quantifying these heterogeneous risks is challenging, leading to their exclusion from many models.
Purpose of the Study:
- To develop an epidemic model that incorporates group-specific transmission rates using weighted hypergraphs.
- To analytically investigate the consequences of ignoring transmissibility heterogeneity in contagion models.
- To introduce a framework for quantifying contagion nonlinearity and assess classification biases.
Main Methods:
- Developed an epidemic model on weighted hypergraphs to capture group-specific transmission rates.
- Analytically studied the emergence of superlinear infection rates from linear contagion mechanisms when heterogeneity is ignored.
- Introduced a Bayesian inference framework to quantify contagion nonlinearity.
Main Results:
- Ignoring heterogeneous transmissibility can induce a superlinear infection rate during outbreak emergence, mimicking complex contagions.
- Simple contagions on real weighted hypergraphs are systematically biased towards the superlinear regime if weight heterogeneity is overlooked.
- This bias increases the risk of misclassifying simple contagions as complex ones.
Conclusions:
- Heterogeneity in transmission risk can blur the distinction between simple and complex contagion dynamics in realistic epidemic settings.
- Ignoring such heterogeneity in mathematical models can lead to erroneous conclusions about transmission mechanisms.
- The findings highlight the need for sophisticated models that account for complex epidemic features through nonlinear infection rates.
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