Network Inference from Population-Level Observation of Epidemics F. Di Lauro1 , J.-C. Croix1 , M. Dashti1 , L. Berthouze2 , I.Z. Kiss1 1 Department of Mathematics, University of Sussex, Falmer, Brighton BN1 9QH, UK 2 Centre for Computational Neuroscience...
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Network Inference from Population-Level Observation of Epidemics F. Di Lauro1 , J.-C. Croix1 , M. Dashti1 , L. Berthouze2 , I.Z. Kiss1 1 Department of Mathematics, University of Sussex, Falmer, Brighton BN1 9QH, UK 2 Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer BN1 arXiv:1906.10966v1 [q-bio.PE] 26 Jun 2019 9QH, UK June 27, 2019 Abstract The network paradigm is widely accepted as the gold standard in modelling complex systems such as epidemics or neuronal activity in the brain; however, in most cases, the exact nature of the network on which such dynamics unfold is unknown. This has motivated a significant amount of work on network inference. Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available. A key obstacle is the forbi
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