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dc.contributor.authorVenkatramanan, Srinivasanen_US
dc.contributor.authorLewis, Bryanen_US
dc.contributor.authorChen, Jiangzhuoen_US
dc.contributor.authorHigdon, Daveen_US
dc.contributor.authorVullikanti, Anilen_US
dc.contributor.authorMarathe, Madhaven_US
dc.date.accessioned2017-11-14T17:49:23Z
dc.date.available2017-11-14T17:49:23Z
dc.date.issued2017-02-22
dc.identifier.urihttp://hdl.handle.net/10919/80385
dc.description.abstractProducing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models providea comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper,we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refinedand adapted for future epidemics, and share the lessons learned over the course of the challenge.
dc.description.sponsorshipThe work has been partially supported by DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0001, NSFNetSE Grant CNS-1011769 and NIH MIDAS Grant 5U01GM070694.
dc.language.isoen_US
dc.publisherElsevier
dc.subjectEmerging infectious diseases
dc.subjectAgent-based models
dc.subjectSimulation optimization
dc.subjectBayesian calibration
dc.subjectEbola
dc.titleUsing data-driven agent-based models for forecasting emerging infectious diseases
dc.typeArticle - Refereed
dc.title.serialEpidemics
dc.identifier.doihttps://doi.org/10.1016/j.epidem.2017.02.010


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