Features of the simulated contagion process affect performance metrics such as bias and improvement in RMSE. To evaluate the sizes of these effects, we used simple linear regression treating the RMSE as the outcome and simulation features (i.e., mean degree, degree distribution, assortativity, block mixing structure, Infectivity mode, and infection prevalence at baseline) as covariates, coded as binary variables. The fitted coefficients represent the metric change when changing simulation features, holding all other simulation features constant. The percent improvement in RMSE is shown in Table 4. For example, holding all other simulation features constant, a contagion process exhibiting high baseline prevalence shows an additional RMSE reduction of 29-33 percentage points compared to a contagion process exhibiting low baseline prevalence. Therefore, using covariate X (9) (total number of affected neighbors at baseline) yields larger reductions in RMSE if the contagion begins with high compared to low prevalence at baseline. This suggests that a node’s local network contacts and whether they are contagious, i.e. can cause a process to spread on the network, can be quite predictive of the risk of the node becoming affected — a finding also reported by Ghani et al. $^{22}$. 14 Figure 2: The distribution of the proportion of occasions each covariate is included in the outcome model. The covariates along the x -axis are described in Table 1. Blue bars show results for the exposed group, and red bars show results for the unexposed group. The middle values represent medians, the bars represent the (25, 75)% quartiles across scenarios, and endpoints indicate minima and maxima. Sensitivity Analysis Results 0.0 0.2 0.4 0.6 0.8 1.0 Covariate Fraction of Times Included $_{X}$( 1 ) $_{X}$( 3 ) $_{X}$( 5 ) $_{X}$( 7 ) $_{X}$( 9 ) $_{X}$( 11 ) $_{X}$( 2 ) $_{X}$( 4 ) $_{X}$( 6 ) $_{X}$( 8 ) $_{X}$( 10 ) $_{X}$( 12 ) Exposed Unexposed