Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study

Sangeeta Bhatia; Britta Lassmann; Emily Cohn; Malwina Carrion; Moritz U.G. Kraemer; Mark Herringer; John Brownstein; Larry Madoff; Anne Cori; Pierre Nouvellet

In this study, we propose a new statistical framework to estimate and visualize risks posed by outbreak events using digital surveillance data. Our approach relies on a relatively simple statistical framework that integrates multiple data streams to predict the future incidence trajectory, and quantifies spatial heterogeneity in the risk of disease spread. In this paper, we present the model implemented in this framework and as a case study, we report the analysis of ProMED and HealthMap data for the 2013-16 West African Ebola epidemic. To assess the robustness of the digital surveillance data, we also applied the framework to the data collated by WHO and made available at the end of the epidemic. We present a comparison of the near real-time ProMED/HealthMap data with the retrospective WHO data and the results of the spatio-temporal analysis carried out on these three data sources.