Nonlinear bias toward complex contagion in uncertain transmission settings.

ABSTRACT: Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic or different moderation practices in social media communities. However, quantifying these heterogeneous levels of risk is difficult, and most models typically ignore them. Here, we include these features in an epidemic model on weighted hypergraphs to capture group-specific transmission rates. We study analytically the consequences of ignoring the heterogeneous transmissibility and find an induced superlinear infection rate during the e

SUBMITTER: St-Onge G 

PROVIDER: S-EPMC10769855 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature