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The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. We developed a Bayesian hierarchical model leveraging recent household surveys with probabilistic sampling designs and building footprints to produce up-to-date population estimates. We estimated population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibited a very good fit, with an R^2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. The results confirm the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses., Comment: 27 pages, 6 figures

As of October 12, 2018, 211 cases of Ebola virus disease (EVD) were reported in North Kivu Province, Democratic Republic of Congo. Since the beginning of October the outbreak has largely shifted into regions in which active armed conflict is occurring, and in which EVD cases and their contacts are difficult for health workers to reach. We modeled EVD transmission using a branching process with gradually quenching transmission estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used an autoregression for short-term projections, a regression model for final sizes, and a simple Gott's law rule as an ensemble of forecasts. Short-term model projections were validated against actual case counts. During validation of short-term projections, models consistently scored higher on shorter-term forecasts. Based on case counts as of October 13, the stochastic model projected a median case count of 226 by October 27 (95% prediction interval: 205-268) and 245 by November 10 (95% PI: 208-315), while the auto-regression model projected median case counts of 240 (95% PI: 215-307) and 259 (95% PI: 216-395) for those dates, respectively. Projected median final counts range from 274 to 421. Except for Gott's law, the projected probability of an outbreak surpassing 2013-2016 is exceedingly small. The stochastic model estimates that vaccine coverage in this outbreak is lower than reported in its trial. Based on our projections we believe that the epidemic had not yet peaked at the time of these estimates, though an outbreak like 2013-2016 is not likely. We estimate that transmission rates are higher than under target levels of vaccine coverage, and this model estimate may offer a surrogate indicator for the outbreak response challenges.