Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology, discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a “hurdle model” approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer we create a multiplex, over which we simulated the spread of ‘fast’ (R0=3) and ‘slow’ (R0=1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralisation of movement data to construct representative networks.