  Background: Network-based interventions are most powerful against epidemics
when the full network structure is known. However, resource constraints often
require decisions based on partial network data. We investigated how the
effectiveness of network-based vaccination schemes varied based on the accuracy
of network data available. Methods: We simulated propagating an SIR process on
static empirical social networks from 75 rural Indian villages. First, we used
regression analysis to predict the percentage of individuals ever infected
(cumulative incidence) based on village-level networks. Second, we simulated
vaccinating 10% of each village at baseline, selecting vaccinees using 1 of 5
network-based approaches: at random (Random); random contacts of random
individuals (Nomination); random high-degree individuals (High Degree); highest
degree (Highest Degree); or most central (Central). The first three approaches
require only sample data; the latter two require full network data. We also
simulated imposing a limit on how many contacts an individual can nominate
(Fixed Choice Design, FCD), which reduces data collection burden but generates
only partially observed networks. Results: We found the mean and standard
deviation of the degree distribution to strongly predict cumulative incidence.
In simulations, Nomination reduced cumulative incidence by one-sixth compared
to Random vaccination; full network methods reduced infection by two-thirds.
High Degree had intermediate effectiveness. Somewhat surprisingly, FCD
truncating individuals' degrees at three was as effective as using complete
networks. Conclusions: Using even partial network information to prioritize
vaccines at either the village or individual level substantially improved
epidemic outcomes. Such approaches may be feasible and effective in outbreak
settings, and full ascertainment of network structure may not be required.
