Using data-driven agent-based models for forecasting emerging infectious diseases

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dc.contributor.authorVenkatramanan, Srinivasanen
dc.contributor.authorLewis, Bryan L.en
dc.contributor.authorChen, Jiangzhuoen
dc.contributor.authorHigdon, Daveen
dc.contributor.authorVullikanti, Anil Kumar S.en
dc.contributor.authorMarathe, Madhav V.en
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentStatisticsen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.date.accessioned2017-11-14T17:49:23Zen
dc.date.available2017-11-14T17:49:23Zen
dc.date.issued2017-02-22en
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.en
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.en
dc.identifier.doihttps://doi.org/10.1016/j.epidem.2017.02.010en
dc.identifier.urihttp://hdl.handle.net/10919/80385en
dc.language.isoen_USen
dc.publisherElsevieren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEmerging infectious diseasesen
dc.subjectAgent-based modelsen
dc.subjectSimulation optimizationen
dc.subjectBayesian calibrationen
dc.subjectEbolaen
dc.titleUsing data-driven agent-based models for forecasting emerging infectious diseasesen
dc.title.serialEpidemicsen
dc.typeArticle - Refereeden

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Destination Area: Global Systems Science (GSS)
Scholarly Works, Computer Science
Scholarly Works, Fralin Life Sciences Institute
Scholarly Works, Statistics