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

The correlation between estimates of time-varying reproduction number estimated using ProMED, HealthMap and WHO data depended on the time window used for estimation and the country (Fig 3) . Restricting the analysis was robust to using reproduction number estimates with lower uncertainty (coefficient of variation less than 0.25, Fig 4) Similarly, for each data source, 6 . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Only estimates with a coefficient of variation less than 0.25 were included in this analysis. The reproduction numbers were estimated using R package EpiEstim over a 4 week sliding window. Median estimates from WHO data are on the x-axis and the median estimates using ProMED (blue) and HealthMap (green) data are on the y-axis. All correlation coefficients were statistically significant.