Bayesian Surveillance for the Detection of Small Area Health Anomalies


The surveillance task when faced with small area health data is more complex than in the time domain alone. Both changes in time and space must be considered. Such questions as ‘where will the infection spread to next?’ and, ‘when will the infection arrive here’, or ‘when do we see the end of the epidemic?’ are all spatially specific questions that are commonly of concern for both the public and public health agencies.  Hence both spatial and temporal dimensions of the surveillance task must be considered.

In this webinar we plan to outline the questions to be addressed with geo-referenced small area health data. We also plan to outline Bayesian methods and how they can be applied to model disease variation in space and time. Finally we will consider a case study of respiratory outcomes monitored in small areas (counties) in the state of South Carolina, USA.


Professor Andrew B Lawson, Department of Public Health Sciences, Medical University of South Carolina
Research interests: I have extensive research experience in the study of spatial epidemiology/disease mapping and spatial health surveillance and I use Bayesian methods extensively in these applications. I have a PhD in spatial statistics from University of St Andrews, Scotland, I am an elected Fellow of the American Statistical Association and a MUSC Distinguished University Professor.

Professor Ana Corberán is assistant professor in the Department of Statistics and Operational Research, University of Valencia, Spain. She has a PhD from University of Valencia and was formally a post doctoral Fellow at MUSC working on small area health surveillance with Professor Lawson.

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Event/Publication Date: 
July, 2016

March 14, 2017

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