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

Identifieur interne : 000356 ( Main/Exploration ); précédent : 000355; suivant : 000357

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

Auteurs : Moritz U G. Kraemer [Royaume-Uni] ; Nuno R. Faria [Royaume-Uni] ; Robert C. Reiner [États-Unis] ; Nick Golding [Royaume-Uni, Australie] ; Birgit Nikolay [France] ; Stephanie Stasse ; Michael A. Johansson [États-Unis] ; Henrik Salje [France, États-Unis] ; Ousmane Faye [Sénégal] ; G R William Wint [Royaume-Uni] ; Matthias Niedrig [Allemagne] ; Freya M. Shearer [Royaume-Uni] ; Sarah C. Hill [Royaume-Uni] ; Robin N. Thompson [Royaume-Uni] ; Donal Bisanzio [Royaume-Uni] ; Nuno Taveira [Portugal] ; Heinrich H. Nax [Suisse] ; Bary S R. Pradelski [Suisse] ; Elaine O. Nsoesie [États-Unis] ; Nicholas R. Murphy [États-Unis] ; Isaac I. Bogoch [Canada] ; Kamran Khan [Canada] ; John S. Brownstein [États-Unis] ; Andrew J. Tatem [Royaume-Uni, Suède] ; Tulio De Oliveira [Afrique du Sud] ; David L. Smith [Royaume-Uni, États-Unis] ; Amadou A. Sall [Sénégal] ; Oliver G. Pybus [Royaume-Uni] ; Simon I. Hay [États-Unis, Royaume-Uni] ; Simon Cauchemez [France]

Source :

RBID : PMC:5332542

Descripteurs français

English descriptors

Abstract

SummaryBackground

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.


Url:
DOI: 10.1016/S1473-3099(16)30513-8
PubMed: 28017559
PubMed Central: 5332542


Affiliations:


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<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Department of Zoology, University of Oxford, Oxford</wicri:regionArea>
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<orgName type="university">Université d'Oxford</orgName>
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<wicri:regionArea>Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA</wicri:regionArea>
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<settlement type="city">Seattle</settlement>
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<orgName type="university">Université de Washington</orgName>
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<nlm:aff id="aff24">Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD, USA</nlm:aff>
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<wicri:regionArea>Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD</wicri:regionArea>
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<region type="state">Maryland</region>
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<author>
<name sortKey="Sall, Amadou A" sort="Sall, Amadou A" uniqKey="Sall A" first="Amadou A" last="Sall">Amadou A. Sall</name>
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<nlm:aff id="aff12">Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar, Senegal</nlm:aff>
<country xml:lang="fr">Sénégal</country>
<wicri:regionArea>Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar</wicri:regionArea>
<wicri:noRegion>Dakar</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Pybus, Oliver G" sort="Pybus, Oliver G" uniqKey="Pybus O" first="Oliver G" last="Pybus">Oliver G. Pybus</name>
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<nlm:aff id="aff1">Department of Zoology, University of Oxford, Oxford, UK</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
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</author>
<author>
<name sortKey="Hay, Simon I" sort="Hay, Simon I" uniqKey="Hay S" first="Simon I" last="Hay">Simon I. Hay</name>
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<nlm:aff id="aff2">Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
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<country xml:lang="fr">Royaume-Uni</country>
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</affiliation>
</author>
<author>
<name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name>
<affiliation wicri:level="3">
<nlm:aff id="aff5">Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris</wicri:regionArea>
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<region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Paris</settlement>
</placeName>
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<affiliation wicri:level="3">
<nlm:aff id="aff6">Centre National de la Recherche Scientifique, URA 3012, Paris, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Centre National de la Recherche Scientifique, URA 3012, Paris</wicri:regionArea>
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<region type="region">Île-de-France</region>
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</analytic>
<series>
<title level="j">The Lancet. Infectious Diseases</title>
<idno type="ISSN">1473-3099</idno>
<idno type="eISSN">1474-4457</idno>
<imprint>
<date when="2017">2017</date>
</imprint>
</series>
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<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Aedes (virology)</term>
<term>Angola</term>
<term>Animals</term>
<term>Democratic Republic of the Congo</term>
<term>Disease Outbreaks (prevention & control)</term>
<term>Humans</term>
<term>Immunization Schedule</term>
<term>Models, Statistical</term>
<term>Rural Population (statistics & numerical data)</term>
<term>Travel</term>
<term>Urban Population (statistics & numerical data)</term>
<term>Vaccination</term>
<term>Yellow Fever (epidemiology)</term>
<term>Yellow Fever (mortality)</term>
<term>Yellow Fever (transmission)</term>
<term>Yellow fever virus (isolation & purification)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Aedes (virologie)</term>
<term>Angola</term>
<term>Animaux</term>
<term>Calendrier vaccinal</term>
<term>Fièvre jaune (mortalité)</term>
<term>Fièvre jaune (transmission)</term>
<term>Fièvre jaune (épidémiologie)</term>
<term>Flambées de maladies ()</term>
<term>Humains</term>
<term>Modèles statistiques</term>
<term>Population rurale ()</term>
<term>Population urbaine ()</term>
<term>République démocratique du Congo</term>
<term>Vaccination</term>
<term>Virus de la fièvre jaune (isolement et purification)</term>
<term>Voyage</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Yellow Fever</term>
</keywords>
<keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en">
<term>Yellow fever virus</term>
</keywords>
<keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr">
<term>Virus de la fièvre jaune</term>
</keywords>
<keywords scheme="MESH" qualifier="mortality" xml:lang="en">
<term>Yellow Fever</term>
</keywords>
<keywords scheme="MESH" qualifier="mortalité" xml:lang="fr">
<term>Fièvre jaune</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en">
<term>Disease Outbreaks</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en">
<term>Rural Population</term>
<term>Urban Population</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en">
<term>Yellow Fever</term>
</keywords>
<keywords scheme="MESH" qualifier="virologie" xml:lang="fr">
<term>Aedes</term>
</keywords>
<keywords scheme="MESH" qualifier="virology" xml:lang="en">
<term>Aedes</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr">
<term>Fièvre jaune</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Angola</term>
<term>Animals</term>
<term>Democratic Republic of the Congo</term>
<term>Humans</term>
<term>Immunization Schedule</term>
<term>Models, Statistical</term>
<term>Travel</term>
<term>Vaccination</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Angola</term>
<term>Animaux</term>
<term>Calendrier vaccinal</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Modèles statistiques</term>
<term>Population rurale</term>
<term>Population urbaine</term>
<term>République démocratique du Congo</term>
<term>Vaccination</term>
<term>Voyage</term>
</keywords>
</textClass>
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<front>
<div type="abstract" xml:lang="en">
<title>Summary</title>
<sec>
<title>Background</title>
<p>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.</p>
</sec>
<sec>
<title>Methods</title>
<p>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.</p>
</sec>
<sec>
<title>Findings</title>
<p>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
<italic>r</italic>
0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's
<italic>r</italic>
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.</p>
</sec>
<sec>
<title>Interpretation</title>
<p>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.</p>
</sec>
<sec>
<title>Funding</title>
<p>Wellcome Trust.</p>
</sec>
</div>
</front>
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<li>Portugal</li>
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<name sortKey="Bisanzio, Donal" sort="Bisanzio, Donal" uniqKey="Bisanzio D" first="Donal" last="Bisanzio">Donal Bisanzio</name>
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<name sortKey="Golding, Nick" sort="Golding, Nick" uniqKey="Golding N" first="Nick" last="Golding">Nick Golding</name>
<name sortKey="Hay, Simon I" sort="Hay, Simon I" uniqKey="Hay S" first="Simon I" last="Hay">Simon I. Hay</name>
<name sortKey="Hill, Sarah C" sort="Hill, Sarah C" uniqKey="Hill S" first="Sarah C" last="Hill">Sarah C. Hill</name>
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<name sortKey="Shearer, Freya M" sort="Shearer, Freya M" uniqKey="Shearer F" first="Freya M" last="Shearer">Freya M. Shearer</name>
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<country name="États-Unis">
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<name sortKey="Johansson, Michael A" sort="Johansson, Michael A" uniqKey="Johansson M" first="Michael A" last="Johansson">Michael A. Johansson</name>
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<region name="Île-de-France">
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<name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name>
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<name sortKey="Salje, Henrik" sort="Salje, Henrik" uniqKey="Salje H" first="Henrik" last="Salje">Henrik Salje</name>
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<country name="Sénégal">
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<name sortKey="Faye, Ousmane" sort="Faye, Ousmane" uniqKey="Faye O" first="Ousmane" last="Faye">Ousmane Faye</name>
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<name sortKey="Niedrig, Matthias" sort="Niedrig, Matthias" uniqKey="Niedrig M" first="Matthias" last="Niedrig">Matthias Niedrig</name>
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<name sortKey="Taveira, Nuno" sort="Taveira, Nuno" uniqKey="Taveira N" first="Nuno" last="Taveira">Nuno Taveira</name>
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<name sortKey="Nax, Heinrich H" sort="Nax, Heinrich H" uniqKey="Nax H" first="Heinrich H" last="Nax">Heinrich H. Nax</name>
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<country name="Canada">
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<name sortKey="Bogoch, Isaac I" sort="Bogoch, Isaac I" uniqKey="Bogoch I" first="Isaac I" last="Bogoch">Isaac I. Bogoch</name>
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<name sortKey="Khan, Kamran" sort="Khan, Kamran" uniqKey="Khan K" first="Kamran" last="Khan">Kamran Khan</name>
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<country name="Suède">
<region name="Svealand">
<name sortKey="Tatem, Andrew J" sort="Tatem, Andrew J" uniqKey="Tatem A" first="Andrew J" last="Tatem">Andrew J. Tatem</name>
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<country name="Afrique du Sud">
<noRegion>
<name sortKey="De Oliveira, Tulio" sort="De Oliveira, Tulio" uniqKey="De Oliveira T" first="Tulio" last="De Oliveira">Tulio De Oliveira</name>
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</record>

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