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Predicting the viral dynamics of an epidemic process requires the knowledgeof the underlying contact network. However, the network is not known for mostapplications and has to be inferred from observing the viral state evolutioninstead. We propose a polynomial-time network reconstruction algorithm for thediscrete-time NIMFA model based on a basis pursuit formulation. Given only fewinitial viral state observations, the network reconstruction method allows foran accurate prediction of the further viral state evolution of every nodeprovided that the network is sufficiently sparse.

Network Reconstruction and Prediction of Epidemic Outbreaks for NIMFA Processes