Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

Worden, Lee; Wannier, Rae; Hoff, Nicole A.; Musene, Kamy; Selo, Bernice; Mossoko, Mathias; Okitolonda-Wemakoy, Emile; Muyembe Tamfum, Jean Jacques; Rutherford, George W.; Lietman, Thomas M.; Rimoin, Anne W.; Porco, Travis C.; Kelly, J. Daniel

Epidemic curves reported for past Ebola outbreaks were used to estimate time series of effective reproduction number (R) by day, which were then fit to an exponentially declining ("quenched") curve. The quenching rate parameter τ estimates the relative change in R per day from R initial that results from outbreak control efforts, behavioral changes in response to the outbreak, and potential local depletion of susceptibles. Estimates of R by day are drawn as heavy curves, and exponentially quenched curves R = R initial e −τd fit to each series of R estimates are drawn as lighter curves. The R initial and τ parameters driving simulated outbreaks that were successful in passing the particle filtering step, which selects simulated outbreaks that match the reported case counts, tended to cluster in particular locations within the assumed distribution. In some cases, distinct ranges of R initial and/or τ were selected in conjunction with the different vaccine coverage scenarios. Shown here is the distribution of parameter combinations (R initial , τ) selected by the filtering process, colored by vaccine coverage scenario, for successive snapshots of available case count data. As in previous figure, black dots represent R initial , τ pairs estimated for past outbreaks (for comparison), and colors illustrate the density of R initial , τ pairs selected by filtering simulated outbreaks, by level of vaccine coverage. In the Feb. 25 dataset, no simulated outbreaks with vaccine coverage at the 62% (high) level were selected. (PDF) of the stochastic model, by vaccine coverage scenario, using successive snapshots of available case count data. The simulations passing the particle filtering step, representing a distribution of parameter values and vaccine scenarios, were continued beyond the particle filtering points to generate a spreading set of projections of case counts at later dates, shown here. This sample of projected case counts by day was smoothed to create probabilistic projections of projected case counts at the desired future dates. The vertical axis is cut off at the upper limit of the 95% prediction interval for outbreak sizes, for readability. The 62% (high) vaccine coverage scenario is not represented in the February 25 ensemble due to the action of the filtering step of the model.