Real-time forecasting of epidemic trajectories using computational dynamic ensembles

G Chowell; R Luo; K Sun; K Roosa; A Tariq; C Viboud

doi:10.1016/j.epidem.2019.100379

Real-time forecasting of epidemic trajectories using computational dynamic ensembles

Epidemics. 2019 Dec 21:30:100379. doi: 10.1016/j.epidem.2019.100379. Online ahead of print.

Authors

G Chowell¹, R Luo², K Sun³, K Roosa², A Tariq², C Viboud³

Affiliations

¹ Department of Population Heath Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. Electronic address: gchowell@gsu.edu.
² Department of Population Heath Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
³ Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.

PMID: 31887571
DOI: 10.1016/j.epidem.2019.100379

Abstract

Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approach to evaluate its uncertainty. We demonstrate the feasibility of our approach using simple dynamic differential-equation models and the trajectory of outbreak scenarios of the Ebola Forecasting Challenge. Specifically, we generate sequential short-term forecasts of epidemic outbreaks by combining phenomenological models that incorporate flexible epidemic growth scaling, namely the Generalized-Growth Model (GGM) and the Generalized Logistic Model (GLM). We rely on the root-mean-square error (RMSE) to quantify the quality of the models' fits during the calibration periods for weighting their contribution to the ensemble model while forecasting performance was evaluated using the RMSE of the forecasts. For a given forecasting horizon (1-4 weeks), we report the performance for each model as the percentage of the number of times each model outperforms the other models. The overall mean RMSE performance of the GLM and the GGM-GLM ensemble models outcompeted that of participant models of the Ebola Forecasting Challenge. We also found that the ensemble model provided more accurate forecasts with higher frequency than the GGM and GLM models, but its performance varied across forecasting horizons. For instance, across all of the Ebola Challenge Scenarios, the ensemble model outperformed the other models at horizons of 2 and 3 weeks while the GLM outperformed other models at horizons of 1 and 4 weeks.

Keywords: Ensemble forecast; Epidemic forecasting; Generalized-growth model; Generalized-logistic model; Model ensemble; Parameter estimation; RMSE; Reproduction number; Uncertainty propagation; Uncertainty quantification.