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 {em
causative network} of an epidemic.