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The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany.
We present a universal, data-driven decomposition of chaos as an intermittently forced linear system.
Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources.
We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynam