  Background: In settings where proof-of-principle trials have succeeded but
the effectiveness of different forms of implementation remains uncertain,
trials that not only generate information about intervention effects but also
provide public health benefit would be useful. Cluster randomized trials (CRT)
capture both direct and indirect intervention effects; the latter depends
heavily on contact networks within and across clusters. We propose a novel
class of connectivity-informed trial designs that leverages information about
such networks in order to improve public health impact and preserve ability to
detect intervention effects.
  Methods: We consider CRTs in which the order of enrollment is based on the
total number of ties between individuals across clusters (based either on the
total number of inter-cluster connections or on connections only to untreated
clusters). We include options analogous both to traditional Parallel and
Stepped Wedge designs. We also allow for control clusters to be "held-back"
from re-randomization for some period. We investigate the performance epidemic
control and power to detect vaccine effect performance of these designs by
simulating vaccination trials during an SEIR-type epidemic using a
network-structured agent-based model.
  Results: In our simulations, connectivity-informed designs have lower peak
infectiousness than comparable traditional designs and reduce cumulative
incidence by 20%, but with little impact on time to end of epidemic and reduced
power to detect differences in incidence across clusters. However even a brief
"holdback" period restores most of the power lost compared to traditional
approaches.
  Conclusion: Incorporating information about cluster connectivity in design of
CRTs can increase their public health impact, especially in acute outbreak
settings, with modest cost in power to detect an effective intervention.
