However, a deterministic model like InfluSim cannot reliably represent effects originating from stochasticity, from effects in small populations, or from heterogeneities. Examples are: (i) a geographically limited spread and fairly effective control measures can imply that the epidemic affects only a small population and thus, may be strongly influenced by stochastic events [15] [16] [17] ; (ii) transmission which predominantly occurs in households or hospitals, or which is driven by other substantial features of the contact network is not in agreement with the assumption of homogeneous mixing in the deterministic model cannot reliably predict the spread of infection [18] [19] [20] [21] [22] [23] . In particular, (iii) super-spreading events can substantially change the course of an epidemic compared to the deterministic prediction [24] [25] [26] [27] . Apart from such factors, the predictability of intervention success is generally subject to uncertainties in the choice of parameter values, Assumed scenarios and outcomes of pandemic preparedness plans. * Gross attack rate (i.e. clinically ill and moderately ill cases). A population of N = 100,000 inhabitants of Germany is subdivided according to age a and risk category r. We assume that all age groups are fully susceptible at begin of the outbreak. A fraction of F a = 6% of all children (age < 20 years) are regarded as being under high risk (r = r 1 ) after an influenza infection whereby the remaining 94% are under low risk (r = r 2 ). The high risk fractions of working adults (ages 20-59) and elderly (ages 60+) are F a = 14% and F a = 47%, respectively. Source: [9] demanding additional efforts like Bayesian approaches [28] to evaluate the reliability of predictions [29] .