The history of infections and epidemics holds famous examples where understanding, containing and ultimately treating an outbreak began with understanding its mode of spread. The key question then, is: which network of interactions is the main cause of the spread? Our current approaches to understand and predict epidemics rely on the scarce, but exact/reliable, expert diagnoses. In this talk we explore a different way forward: use more readily available but also more noisy and incomplete data to determine the causative network of an epidemic.
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