Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in Northeastern Democratic Republic of Congo

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

The stochastic model estimated likelihoods of the three scenarios of zero, low, and high 308 vaccination coverage, based on how often models using the different coverage 309 assumptions were able to pass the particle filtering step. In the estimate based on data 310 through February 25, vaccination coverage was estimated substantially more likely than 311 low or high coverage, as was also true in most earlier estimates. The lower vaccination 312 coverage scenario was estimated more likely than the higher one. Higher vaccination 313 coverage scenarios were estimated more likely in estimates made before October, at were concentrated in a range up to about 300 additional cases overall, and even the inaccurate. EVD has never before been introduced into a conflict zone with such present, the most reliable data source of EBOV transmission has been the weekly case 330 counts that can be found in the WHO situation reports. Despite such situations of data 331 scarcity and new outbreak circumstances, our models generated relatively accurate The mathematical models we adopted for this project near the beginning of the The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/461285 doi: bioRxiv preprint outbreak. This may be a useful pattern for short-term forecasting of ongoing disease 359 outbreaks in real time.