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

TLDR

Timely, reliable forecasting of emerging epidemics is difficult due to poor data, limited disease understanding, changing social contexts, and intervention uncertainty, but detailed computational models can integrate diverse data to improve understanding and decision‑making. The paper presents an agent‑based model framework for forecasting the 2014–2015 Ebola epidemic in Liberia and its use in the Ebola forecasting challenge. The framework comprises detailed agent‑based components, a calibration procedure, and performance evaluation across challenge scenarios. The study demonstrates that data‑driven agent‑based models can be refined and adapted for future epidemics, with lessons learned from the challenge.

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.