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Uncertainty Quantification

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IDP - Infectious Dynamics of Pandemics: Mathematical and statistical challenges in understanding the dynamics of infectious disease pandemics

Peter Challenor Why do uncertainty quantification
Evan Baker (Exeter) Emulating Stochastic Models
Building emulators for complex models typically involves Gaussian processes. For stochastic models, the flexibility of a Gaussian process is a nice feature, but modifications are needed to account for the noisiness of simulations. In this talk I will summarise some key attributes of stochastic models and how these can change the emulation methodology. Additionally, I will briefly talk about the simulation design issues that arise for stochastic models.
Jeremy Oakley (Sheffield)  - Introduction to Probabilistic Sensitivity Analysis
Mathematical models of infectious diseases invariably have uncertainty about the correct values of some of their model inputs/parameters. This induces uncertainty in the model outputs. In some situations, it may be desirable to reduce this uncertainty, by collecting more data about uncertain model inputs, before using the model outputs to inform decisions. However, it is unlikely that all inputs are 'equally important': some will contribute to output uncertainty more than others. I will discuss how probabilistic sensitivity analysis can be used to identify which uncertain inputs are most influential, and describe simple computational tools that can be used for implementing the analysis, based on a random sample of model runs.

This talk is part of the Isaac Newton Institute Seminar Series series.

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