Data-driven prediction and origin identification of epidemics in\n population networks Article Swipe
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· 2017
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1710.07880
· OA: W3033737875
Effective intervention strategies for epidemics rely on the identification of\ntheir origin and on the robustness of the predictions made by network disease\nmodels. We introduce a Bayesian uncertainty quantification framework to infer\nmodel parameters for a disease spreading on a network of communities from\nlimited, noisy observations; the state-of-the-art computational framework\ncompensates for the model complexity by exploiting massively parallel computing\narchitectures. Using noisy, synthetic data, we show the potential of the\napproach to perform robust model fitting and additionally demonstrate that we\ncan effectively identify the disease origin via Bayesian model selection. As\ndisease-related data are increasingly available, the proposed framework has\nbroad practical relevance for the prediction and management of epidemics.\n
