TY - GEN T1 - Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study AU - Kraemer, Moritz U. G. AU - Faria, Nuno R. AU - Jr., Robert C. Reiner AU - Golding, Nick AU - Nikolay, Birgit AU - Stasse, Stephanie AU - Johansson, Michael A. AU - Salje, Henrik AU - Faye, Ousmane AU - Wint, G. R. William AU - Niedrig, Matthias AU - Shearer, Freya M. AU - Hill, Sarah C. AU - Thompson, Robin N. AU - Bisanzio, Donal AU - Taveira, Nuno AU - Nax, Heinrich H. AU - Pradelski, Bary S. R. AU - Nsoesie, Elaine O. AU - Murphy, Nicholas R AU - Bogoch, Isaac I. AU - Khan, Kamran AU - Brownstein, John S. AU - Tatem, Andrew J. AU - Oliveira, Tulio de AB - Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. KW - Vaccination KW - Animals KW - Humans KW - Immunization Schedule KW - Aedes/virology KW - Yellow fever virus/isolation & purification KW - Travel KW - Models Statistical KW - Disease Outbreaks/prevention & control KW - Angola KW - Democratic Republic of the Congo KW - Rural Population/statistics & numerical data KW - Urban Population/statistics & numerical data KW - Yellow Fever/epidemiology KW - Yellow Fever/mortality KW - Yellow Fever/transmission KW - 610 Medizin PY - 2016 LA - eng PB - Robert Koch-Institut VL - 17 IS - 3 DO - 10.1016/S1473-3099(16)30513-8 ER -