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Multiple Data Sources, Missing and Biased Data

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Infectious Disease Dynamics

Inferential methods based on multiple data sources are becoming increasingly common in infectious disease epidemiology, to combine heterogeneous, incomplete and biased evidence. We describe a Bayesian approach to evidence synthesis, highlight its ability to incorporate all available information in a single coherent probabilistic model and discuss current challenges in this area.

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

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