Sociology, Department of

 

ORCID IDs

http://orcid.org/0000-0002-9249-6423

http://orcid.org/0000-0002-7472-2597

http://orcid.org/0000-0002-0008-3673

Document Type

Article

Date of this Version

2020

Citation

Rosenblatt SF, Smith JA, Gauthier GR, He ́bert-Dufresne L (2020) Immunization strategies in networks with missing data. PLoS Comput Biol 16(7): e1007897. https://doi.org/10.1371/journal. pcbi.1007897

Comments

2020 Rosenblatt et al.

Abstract

Network-based intervention strategies can be effective and cost-efficient approaches to cur- tailing harmful contagions in myriad settings. As studied, these strategies are often impracti- cal 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 mini- mizing 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 prac- tice. 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.

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