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

We varied the tolerance as the data set became more complete, to maintain a roughly fixed rate of generation of filtered trajectories. As larger tolerances became necessary, in data sets from after October, we introduced one further distinction: while it is possible for cumulative case counts to decrease as inaccurately classified cases are removed from the counts, due to the precision of the labeling of cases probable and confirmed we expect this to happen rarely, so we limit the tolerance of matching to only at most 15 cases below the reported count regardless of the tolerance of counts above the reported count. This limit on underestimates was applied only to analysis of data sets from later than October 13, to preserve unaltered the projection methods we reported in a preprint of this paper [34] . Filtering tolerances were as follows: when using the August 20 data set, we allowed a tolerance of 4 cases more or less than each target count; for August 27 and September 5, 6 cases; for September 15, 10 cases; for October 7, 12 cases; for October 13, 17 cases; for November 1, 41 cases; for November 20, 55 cases; for January 6, 2019, 75 cases; and for February 25, 150 cases. This one-step particle filtering technique produced an ensemble of model outbreaks, filtered on agreement with the recorded trajectory of the outbreak to date. This filtered ensemble was then used to generate projections of the outbreak in the short term (one week, two weeks, four weeks, and eight weeks) and long term (final size) [35] .