Our results show that the epidemiological consequences of uncertainty in natural history parameters can often be highly significant since these are important for interventions such as reducing transmission through quarantine or medication, as well as prediction of long-term disease behaviour and clinical outcomes.
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Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry.
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