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

To broadly assess the extent to which such discrepancies in incidence would impact the quantification of transmissibility throughout the epidemic, we estimated the time-varying transmissibility, measured by the reproduction number R t (the average number of secondary cases at time t per infected individual [19] ), using the incidence from each of the three data sources (Fig 1) . R t was estimated independently for each country using the R package EpiEstim [20] over a sliding time window of 4 weeks. There were substantial differences in the estimates of R t according to the incidence data source used ( Fig 1B) . The correlation between the median R t estimates on sliding 4-week windows from ProMED or HealthMap data with the estimates from WHO data varied from weak (0.30, between reproduction number from WHO and ProMED in Guinea) to very strong (0.72, between reproduction number from WHO and ProMED in Sierra Leone, Fig 3) .