Modeling transmission chain heterogeneity from experimental data

Heterogeneity is inherent in biological processes. Many distributions of biological data are characterized by asymmetry/skew and long, sometimes bumpy tails, and it is difficult to generate models that fit the relevant features of these distributions. Here we show, using experimental data from small host models, how distributions of both transmission and sensitivity to colonization affect the shape of transmission chains, with distributions of secondary infections as the primary measure of interest. These results provide novel insights into the effects of individual heterogeneity on the outcomes of the stochastic process of transmission.