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Multiple Data Sources, Missing and Biased Data

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If you have a question about this talk, please contact Mustapha Amrani.

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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