  Understanding how person-to-person contagious processes spread through a
population requires accurate information on connections between population
members. However, such connectivity data, when collected via interview, is
often incomplete due to partial recall, respondent fatigue or study design,
e.g., fixed choice designs (FCD) truncate out-degree by limiting the number of
contacts each respondent can report. Past research has shown how FCD truncation
affects network properties, but its implications for predicted speed and size
of spreading processes remain largely unexplored. To study the impact of degree
truncation on spreading processes, we generated collections of synthetic
networks containing specific properties (degree distribution,
degree-assortativity, clustering), and also used empirical social network data
from 75 villages in Karnataka, India. We simulated FCD using various truncation
thresholds and ran a susceptible-infectious-recovered (SIR) process on each
network. We found that spreading processes propagated on truncated networks
resulted in slower and smaller epidemics, with a sudden decrease in prediction
accuracy at a level of truncation that varied by network type. Our results have
implications beyond FCD to truncation due to any limited sampling from a larger
network. We conclude that knowledge of network structure is important for
understanding the accuracy of predictions of process spread on degree truncated
networks.
