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

Authored by Bryan Lewis, Madhav Marathe, Jiangzhuo Chen, Srinivasan Venkatramanan, Dave Higdon, Anil Vullikanti

Date Published: 2018

DOI: 10.1016/j.epidem.2017.02.010

Sponsors: United States National Institutes of Health (NIH) United States National Science Foundation (NSF)

Platforms: Epifast

Model Documentation: Other Narrative

Model Code URLs: Model code not found

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. (c) 2017 The Author(s). Published by Elsevier B.V.

Tags

health Ebola Simulation optimization Emerging infectious diseases Agentmodels Bayesian calibration