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Lancet Infect Dis. 2017 Mar;17(3):330-338. doi: 10.1016/S1473-3099(16)30513-8. Epub 2016 Dec 23.

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

Author information

1
Department of Zoology, University of Oxford, Oxford, UK. Electronic address: moritz.kraemer@zoo.ox.ac.uk.
2
Department of Zoology, University of Oxford, Oxford, UK.
3
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
4
Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; School of BioSciences, University of Melbourne, Parkville, VIC, Australia.
5
Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France.
6
Health Programme, European Commission, International Cooperation and Development, Delegation en RDC, Kinshasa, Democratic Republic of the Congo.
7
Centers for Disease Control and Prevention, San Juan, PR, USA; Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, MA, USA.
8
Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
9
Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar, Senegal.
10
Environmental Research Group Oxford, Department of Zoology, Oxford, UK.
11
Robert Koch Institut, Berlin, Germany.
12
Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.
13
Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, Portugal; Centro de Investigacao Interdisciplinar Egas Moniz, Instituto Superior de Ciencias da Saude Egas Moniz, Caparica, Portugal.
14
Computational Social Science, ETH Zurich, Zurich, Switzerland.
15
School of Medicine, University of California San Francisco, San Francisco, CA, USA.
16
Divisions of General Internal Medicine and Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
17
Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada.
18
Harvard University Medical School Boston, MA, USA.
19
WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK; Flowminder Foundation, Stockholm, Sweden.
20
School of Laboratory Medicine and Medical Sciences, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.
21
Department of Zoology, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD, USA.
22
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.

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.

PMID:
28017559
PMCID:
PMC5332542
DOI:
10.1016/S1473-3099(16)30513-8
[Indexed for MEDLINE]
Free PMC Article

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