SimInf: An R package for Data-driven Stochastic Disease Spread Simulations. Livestock movements are critical for the spread of many infectious diseases in animal populations. The use of real livestock data allows for disease spread modeling that incorporates the time-varying contact network and the population demographic. This paper introduces SimInf, an efficient and general framework for stochastic spatio-temporal disease-spread modelling over a temporal network of connected nodes. It integrates within-node infection dynamics as continuous-time Markov chains and livestock data as scheduled events. The core simulation solver is implemented in C and uses OpenMP to divide work over multiple processors. We provide a technical description of the framework, how to use a built-in model, demonstrate a case study and finally how to extend the framework with a user defined model

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