Author: Chen, Shi; White, Brad J.; Sanderson, Michael W.; Amrine, David E.; Ilany, Amiyaal; Lanzas, Cristina
Title: Highly dynamic animal contact network and implications on disease transmission Document date: 2014_3_26
ID: 1pp7k1k6_12
Snippet: In this study, we have presented a high-resolution direct contact network of calves in a pen. We have found that resolution (or temporal/spatial scaling) substantially alters the observed pattern of contact structure. The degree distribution is less skewed at higher temporal resolution (in our study, 1 h) than at lower ones (1 d or longer period). Increasing to even higher temporal resolution, for example, at quarter-hour or even minute level, ma.....
Document: In this study, we have presented a high-resolution direct contact network of calves in a pen. We have found that resolution (or temporal/spatial scaling) substantially alters the observed pattern of contact structure. The degree distribution is less skewed at higher temporal resolution (in our study, 1 h) than at lower ones (1 d or longer period). Increasing to even higher temporal resolution, for example, at quarter-hour or even minute level, may further change network structure. However, as the resolution increases, the effect of system noise and stochasticity also increases, hence reducing the signal/noise ratio. Such scaling issues have been studied in landscape and conservation ecology 28, 29 but have rarely been addressed in epidemiology, especially for temporal scales. Homogeneous compartmental models assume that contact patterns within a population form a regular random network (3) . However, we show that contact network degree distribution varies with both time and individual, suggesting that non-regular dynamic networks characterize the animal-to-animal contact network at the pen level better than a regular static network. Previous studies have either considered the importance of individual heterogeneity [30] [31] [32] or temporal change in the contact network 33 , but lacked a unified framework to consider both factors simultaneously.
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