  Network-based intervention strategies can be effective and cost-efficient
approaches to curtailing harmful contagions in myriad settings. As studied,
these strategies are often impractical to implement, as they typically assume
complete knowledge of the network structure, which is unusual in practice. In
this paper, we investigate how different immunization strategies perform under
realistic conditions where the strategies are informed by partially-observed
network data. Our results suggest that global immunization strategies, like
degree immunization, are optimal in most cases; the exception is at very high
levels of missing data, where stochastic strategies, like acquaintance
immunization, begin to outstrip them in minimizing outbreaks. Stochastic
strategies are more robust in some cases due to the different ways in which
they can be affected by missing data. In fact, one of our proposed variants of
acquaintance immunization leverages a logistically-realistic ongoing
survey-intervention process as a form of targeted data-recovery to improve with
increasing levels of missing data. These results support the effectiveness of
targeted immunization as a general practice. They also highlight the risks of
considering networks as idealized mathematical objects: overestimating the
accuracy of network data and foregoing the rewards of additional inquiry.
