Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study

Thursday, 2nd of March 2017

Summary below; full text is at http://www.thelancet.com/journals/laninf/article/PIIS1473-3099(16)30513-8/fulltext?elsca1=etoc

The Lancet Infectious Diseases, Volume 17, No. 3, p330–338, March 2017

Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study

Moritz U G Kraemer, DPhilCorrespondence information about the author DPhil Moritz U G KraemerEmail the author DPhil Moritz U G Kraemer et al.

Published: 22 December 2016

Open Access

DOI: http://dx.doi.org/10.1016/S1473-3099(16)30513-8

Article Info

This article can be found in the following collections: Immunisation & vaccination; Infectious diseases-other

Summary

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 (Pearsons r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearsons 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.

Funding

Wellcome Trust.