title: Using data-driven agent-based models for forecasting emerging infectious diseases.
abstract: Producing 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 provide a 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 refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.
authors.affiliation: Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States. Electronic address: vsriniv@vbi.vt.edu.
authors.forename: Srinivasan
authors.initials: S
authors.lastname: Venkatramanan
authors.affiliation: Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States. Electronic address: blewis@vbi.vt.edu.
authors.forename: Bryan
authors.initials: B
authors.lastname: Lewis
authors.affiliation: Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States. Electronic address: chenj@vbi.vt.edu.
authors.forename: Jiangzhuo
authors.initials: J
authors.lastname: Chen
authors.affiliation: Social and Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Statistics, Virginia Tech, United States. Electronic address: dhigdon@vbi.vt.edu.
authors.forename: Dave
authors.initials: D
authors.lastname: Higdon
authors.affiliation: Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Computer Science, Virginia Tech, United States. Electronic address: akumar@vbi.vt.edu.
authors.forename: Anil
authors.initials: A
authors.lastname: Vullikanti
authors.affiliation: Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Computer Science, Virginia Tech, United States. Electronic address: mmarathe@vbi.vt.edu.
authors.forename: Madhav
authors.initials: M
authors.lastname: Marathe
mesh_terms: D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D005544:Forecasting; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology*; D006801:Humans; D007988:Liberia / Q000453:epidemiology; D015233:Models, Statistical
keywords: Agent-based models; Bayesian calibration; Ebola; Emerging infectious diseases; Simulation optimization