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Spatial Scales, Big Data and Disease Transmission Models: Lessons from Pandemic Influenza and Ebola Outbreaks
Cécile Viboud, Division of International Epidemiology and Population Studies, Fogarty International Center, NIH
Abstract
Periodic emergence of novel infections provides a unique opportunity to study pathogen dissemination on naïve soils. New outbreaks are typically associated with strengthened data collection on host disease status, mobility, and pathogen genetic sequences, which are key ingredients to develop well-informed transmission models. Further, in the last decade, growing availability of medical claims and digital Big Data streams has provided a detailed lens on disease dissemination at different spatial scales (eg, state, county, city, zip code).
We will borrow from recent modeling work on the 2009 influenza pandemic and the 2014-2015 Ebola outbreak to review different model formulations used to study the spatial spread of rapidly transmitted agents at different spatial scales. We will discuss how the spatial granularity of epidemiological data affects reproduction number estimates and predictors of disease spread, particularly with respect to demographic and environmental factors. We will also provide examples of conflicting findings between spatial models applied to large pathogen sequencing datasets v. epidemiological data. Finally, we will discuss the way forward and introduce multi-scale models that integrate epidemiological and evolutionary datasets, and a recent boom in disease forecasting efforts.
Presentation: