Serveur d'exploration MERS

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Mapping Potential Amplification and Transmission Hotspots for MERS-CoV, Kenya

Identifieur interne : 000905 ( Main/Exploration ); précédent : 000904; suivant : 000906

Mapping Potential Amplification and Transmission Hotspots for MERS-CoV, Kenya

Auteurs : Stephen Gikonyo [Kenya] ; Tabitha Kimani [Kenya] ; Joseph Matere [Kenya] ; Joshua Kimutai [Kenya] ; Stella G. Kiambi [Kenya] ; Austine O. Bitek [Kenya] ; K. J. Z. Juma Ngeiywa [Kenya] ; Yilma J. Makonnen [Kenya] ; Astrid Tripodi [Italie] ; Subhash Morzaria [Italie] ; Juan Lubroth [Italie] ; Gabriel Rugalema [Kenya] ; Folorunso Oludayo Fasina [Kenya, Afrique du Sud]

Source :

RBID : PMC:7088189

Descripteurs français

English descriptors

Abstract

Dromedary camels have been implicated consistently as the source of Middle East respiratory syndrome coronavirus (MERS-CoV) human infections and attention to prevent and control it has focused on camels. To understanding the epidemiological role of camels in the transmission of MERS-CoV, we utilized an iterative empirical process in Geographic Information System (GIS) to identify and qualify potential hotspots for maintenance and circulation of MERS-CoV, and produced risk-based surveillance sites in Kenya. Data on camel population and distribution were used to develop camel density map, while camel farming system was defined using multi-factorial criteria including the agro-ecological zones (AEZs), production and marketing practices. Primary and secondary MERS-CoV seroprevalence data from specific sites were analyzed, and location-based prevalence matching with camel densities was conducted. High-risk convergence points (migration zones, trade routes, camel markets, slaughter slabs) were profiled and frequent cross-border camel movement mapped. Results showed that high camel-dense areas and interaction (markets and migration zones) were potential hotspot for transmission and spread. Cross-border contacts occurred with in-migrated herds at hotspot locations. AEZ differential did not influence risk distribution and plausible risk factors for spatial MERS-CoV hotspots were camel densities, previous cases of MERS-CoV, high seroprevalence and points of camel convergences. Although Kenyan camels are predisposed to MERS-CoV, no shedding is documented to date. These potential hotspots, determined using anthropogenic, system and trade characterizations should guide selection of sampling/surveillance sites, high-risk locations, critical areas for interventions and policy development in Kenya, as well as instigate further virological examination of camels.

Electronic supplementary material

The online version of this article (10.1007/s10393-018-1317-6) contains supplementary material, which is available to authorized users.


Url:
DOI: 10.1007/s10393-018-1317-6
PubMed: 29549589
PubMed Central: 7088189


Affiliations:


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Le document en format XML

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<term>Disease Reservoirs (virology)</term>
<term>Geographic Information Systems</term>
<term>Geographic Mapping</term>
<term>Kenya (epidemiology)</term>
<term>Middle East Respiratory Syndrome Coronavirus</term>
<term>Prevalence</term>
<term>Public Health Surveillance</term>
<term>Seroepidemiologic Studies</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Animaux</term>
<term>Cartographie géographique</term>
<term>Chameaux (virologie)</term>
<term>Coronavirus du syndrome respiratoire du Moyen-Orient</term>
<term>Infections à coronavirus (médecine vétérinaire)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Kenya (épidémiologie)</term>
<term>Maladies de l'animal (transmission)</term>
<term>Maladies de l'animal (épidémiologie)</term>
<term>Prévalence</term>
<term>Réservoirs d'agents pathogènes (médecine vétérinaire)</term>
<term>Réservoirs d'agents pathogènes (virologie)</term>
<term>Surveillance de la santé publique</term>
<term>Systèmes d'information géographique</term>
<term>Études séroépidémiologiques</term>
</keywords>
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<term>Kenya</term>
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<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Animal Diseases</term>
<term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="médecine vétérinaire" xml:lang="fr">
<term>Infections à coronavirus</term>
<term>Réservoirs d'agents pathogènes</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en">
<term>Animal Diseases</term>
<term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="veterinary" xml:lang="en">
<term>Coronavirus Infections</term>
<term>Disease Reservoirs</term>
</keywords>
<keywords scheme="MESH" qualifier="virologie" xml:lang="fr">
<term>Chameaux</term>
<term>Réservoirs d'agents pathogènes</term>
</keywords>
<keywords scheme="MESH" qualifier="virology" xml:lang="en">
<term>Camelus</term>
<term>Disease Reservoirs</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr">
<term>Infections à coronavirus</term>
<term>Kenya</term>
<term>Maladies de l'animal</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Animals</term>
<term>Geographic Information Systems</term>
<term>Geographic Mapping</term>
<term>Middle East Respiratory Syndrome Coronavirus</term>
<term>Prevalence</term>
<term>Public Health Surveillance</term>
<term>Seroepidemiologic Studies</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Animaux</term>
<term>Cartographie géographique</term>
<term>Coronavirus du syndrome respiratoire du Moyen-Orient</term>
<term>Prévalence</term>
<term>Surveillance de la santé publique</term>
<term>Systèmes d'information géographique</term>
<term>Études séroépidémiologiques</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr">
<term>Kenya</term>
</keywords>
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<front>
<div type="abstract" xml:lang="en">
<p id="Par1">Dromedary camels have been implicated consistently as the source of Middle East respiratory syndrome coronavirus (MERS-CoV) human infections and attention to prevent and control it has focused on camels. To understanding the epidemiological role of camels in the transmission of MERS-CoV, we utilized an iterative empirical process in Geographic Information System (GIS) to identify and qualify potential hotspots for maintenance and circulation of MERS-CoV, and produced risk-based surveillance sites in Kenya. Data on camel population and distribution were used to develop camel density map, while camel farming system was defined using multi-factorial criteria including the agro-ecological zones (AEZs), production and marketing practices. Primary and secondary MERS-CoV seroprevalence data from specific sites were analyzed, and location-based prevalence matching with camel densities was conducted. High-risk convergence points (migration zones, trade routes, camel markets, slaughter slabs) were profiled and frequent cross-border camel movement mapped. Results showed that high camel-dense areas and interaction (markets and migration zones) were potential hotspot for transmission and spread. Cross-border contacts occurred with in-migrated herds at hotspot locations. AEZ differential did not influence risk distribution and plausible risk factors for spatial MERS-CoV hotspots were camel densities, previous cases of MERS-CoV, high seroprevalence and points of camel convergences. Although Kenyan camels are predisposed to MERS-CoV, no shedding is documented to date. These potential hotspots, determined using anthropogenic, system and trade characterizations should guide selection of sampling/surveillance sites, high-risk locations, critical areas for interventions and policy development in Kenya, as well as instigate further virological examination of camels.</p>
<sec>
<title>Electronic supplementary material</title>
<p>The online version of this article (10.1007/s10393-018-1317-6) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<name sortKey="Kock, R" uniqKey="Kock R">R Kock</name>
</author>
<author>
<name sortKey="Muturi, M" uniqKey="Muturi M">M Muturi</name>
</author>
<author>
<name sortKey="Ntoumi, F" uniqKey="Ntoumi F">F Ntoumi</name>
</author>
<author>
<name sortKey="Kaleebu, P" uniqKey="Kaleebu P">P Kaleebu</name>
</author>
<author>
<name sortKey="Eusebio, M" uniqKey="Eusebio M">M Eusebio</name>
</author>
<author>
<name sortKey="Mfinanga, S" uniqKey="Mfinanga S">S Mfinanga</name>
</author>
<author>
<name sortKey="Bates, M" uniqKey="Bates M">M Bates</name>
</author>
<author>
<name sortKey="Mwaba, P" uniqKey="Mwaba P">P Mwaba</name>
</author>
<author>
<name sortKey="Ansumana, R" uniqKey="Ansumana R">R Ansumana</name>
</author>
<author>
<name sortKey="Khan, M" uniqKey="Khan M">M Khan</name>
</author>
<author>
<name sortKey="Alagaili, An" uniqKey="Alagaili A">AN Alagaili</name>
</author>
<author>
<name sortKey="Cotten, M" uniqKey="Cotten M">M Cotten</name>
</author>
<author>
<name sortKey="Azhar, Ei" uniqKey="Azhar E">EI Azhar</name>
</author>
<author>
<name sortKey="Maeurer, M" uniqKey="Maeurer M">M Maeurer</name>
</author>
<author>
<name sortKey="Ippolito, G" uniqKey="Ippolito G">G Ippolito</name>
</author>
<author>
<name sortKey="Petersen, E" uniqKey="Petersen E">E Petersen</name>
</author>
</analytic>
</biblStruct>
</listBibl>
</div1>
</back>
</TEI>
<affiliations>
<list>
<country>
<li>Afrique du Sud</li>
<li>Italie</li>
<li>Kenya</li>
</country>
<region>
<li>Latium</li>
</region>
<settlement>
<li>Rome</li>
</settlement>
</list>
<tree>
<country name="Kenya">
<noRegion>
<name sortKey="Gikonyo, Stephen" sort="Gikonyo, Stephen" uniqKey="Gikonyo S" first="Stephen" last="Gikonyo">Stephen Gikonyo</name>
</noRegion>
<name sortKey="Bitek, Austine O" sort="Bitek, Austine O" uniqKey="Bitek A" first="Austine O." last="Bitek">Austine O. Bitek</name>
<name sortKey="Fasina, Folorunso Oludayo" sort="Fasina, Folorunso Oludayo" uniqKey="Fasina F" first="Folorunso Oludayo" last="Fasina">Folorunso Oludayo Fasina</name>
<name sortKey="Juma Ngeiywa, K J Z" sort="Juma Ngeiywa, K J Z" uniqKey="Juma Ngeiywa K" first="K. J. Z." last="Juma Ngeiywa">K. J. Z. Juma Ngeiywa</name>
<name sortKey="Kiambi, Stella G" sort="Kiambi, Stella G" uniqKey="Kiambi S" first="Stella G." last="Kiambi">Stella G. Kiambi</name>
<name sortKey="Kimani, Tabitha" sort="Kimani, Tabitha" uniqKey="Kimani T" first="Tabitha" last="Kimani">Tabitha Kimani</name>
<name sortKey="Kimutai, Joshua" sort="Kimutai, Joshua" uniqKey="Kimutai J" first="Joshua" last="Kimutai">Joshua Kimutai</name>
<name sortKey="Makonnen, Yilma J" sort="Makonnen, Yilma J" uniqKey="Makonnen Y" first="Yilma J." last="Makonnen">Yilma J. Makonnen</name>
<name sortKey="Matere, Joseph" sort="Matere, Joseph" uniqKey="Matere J" first="Joseph" last="Matere">Joseph Matere</name>
<name sortKey="Rugalema, Gabriel" sort="Rugalema, Gabriel" uniqKey="Rugalema G" first="Gabriel" last="Rugalema">Gabriel Rugalema</name>
</country>
<country name="Italie">
<region name="Latium">
<name sortKey="Tripodi, Astrid" sort="Tripodi, Astrid" uniqKey="Tripodi A" first="Astrid" last="Tripodi">Astrid Tripodi</name>
</region>
<name sortKey="Lubroth, Juan" sort="Lubroth, Juan" uniqKey="Lubroth J" first="Juan" last="Lubroth">Juan Lubroth</name>
<name sortKey="Morzaria, Subhash" sort="Morzaria, Subhash" uniqKey="Morzaria S" first="Subhash" last="Morzaria">Subhash Morzaria</name>
</country>
<country name="Afrique du Sud">
<noRegion>
<name sortKey="Fasina, Folorunso Oludayo" sort="Fasina, Folorunso Oludayo" uniqKey="Fasina F" first="Folorunso Oludayo" last="Fasina">Folorunso Oludayo Fasina</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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