Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study.
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Authors
Kraemer, Moritz UG
Faria, Nuno R
Reiner, Robert C
Golding, Nick
Nikolay, Birgit
Stasse, Stephanie
Johansson, Michael A
Faye, Ousmane
Wint, GR William
Niedrig, Matthias
Shearer, Freya M
Hill, Sarah C
Thompson, Robin N
Bisanzio, Donal
Taveira, Nuno
Nax, Heinrich H
Pradelski, Bary SR
Nsoesie, Elaine O
Murphy, Nicholas R
Bogoch, Isaac I
Khan, Kamran
Brownstein, John S
Tatem, Andrew J
de Oliveira, Tulio
Smith, David L
Sall, Amadou A
Pybus, Oliver G
Hay, Simon I
Cauchemez, Simon
Publication Date
2017-03Journal Title
Lancet Infect Dis
ISSN
1473-3099
Publisher
Elsevier BV
Volume
17
Issue
3
Pages
330-338
Language
eng
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Kraemer, M. U., Faria, N. R., Reiner, R. C., Golding, N., Nikolay, B., Stasse, S., Johansson, M. A., et al. (2017). Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study.. Lancet Infect Dis, 17 (3), 330-338. https://doi.org/10.1016/S1473-3099(16)30513-8
Abstract
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. FUNDING: Wellcome Trust.
Keywords
Animals, Humans, Aedes, Yellow fever virus, Yellow Fever, Immunization Schedule, Vaccination, Models, Statistical, Disease Outbreaks, Travel, Rural Population, Urban Population, Democratic Republic of the Congo, Angola
Identifiers
External DOI: https://doi.org/10.1016/S1473-3099(16)30513-8
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331099
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