Mapping Potential Amplification and Transmission Hotspots for MERS-CoV, Kenya
Identifieur interne : 000905 ( Main/Exploration ); précédent : 000904; suivant : 000906Mapping 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 :
- Ecohealth [ 1612-9202 ] ; 2018.
Descripteurs français
- KwdFr :
- Animaux, Cartographie géographique, Chameaux (virologie), Coronavirus du syndrome respiratoire du Moyen-Orient, Infections à coronavirus (médecine vétérinaire), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Kenya (épidémiologie), Maladies de l'animal (transmission), Maladies de l'animal (épidémiologie), Prévalence, Réservoirs d'agents pathogènes (médecine vétérinaire), Réservoirs d'agents pathogènes (virologie), Surveillance de la santé publique, Systèmes d'information géographique, Études séroépidémiologiques.
- MESH :
- médecine vétérinaire : Infections à coronavirus, Réservoirs d'agents pathogènes.
- virologie : Chameaux, Réservoirs d'agents pathogènes.
- épidémiologie : Infections à coronavirus, Kenya, Maladies de l'animal.
- Animaux, Cartographie géographique, Coronavirus du syndrome respiratoire du Moyen-Orient, Prévalence, Surveillance de la santé publique, Systèmes d'information géographique, Études séroépidémiologiques.
- Wicri :
- geographic : Kenya.
English descriptors
- KwdEn :
- Animal Diseases (epidemiology), Animal Diseases (transmission), Animals, Camelus (virology), Coronavirus Infections (epidemiology), Coronavirus Infections (transmission), Coronavirus Infections (veterinary), Disease Reservoirs (veterinary), Disease Reservoirs (virology), Geographic Information Systems, Geographic Mapping, Kenya (epidemiology), Middle East Respiratory Syndrome Coronavirus, Prevalence, Public Health Surveillance, Seroepidemiologic Studies.
- MESH :
- geographic , epidemiology : Kenya.
- epidemiology : Animal Diseases, Coronavirus Infections.
- transmission : Animal Diseases, Coronavirus Infections.
- veterinary : Coronavirus Infections, Disease Reservoirs.
- virology : Camelus, Disease Reservoirs.
- Animals, Geographic Information Systems, Geographic Mapping, Middle East Respiratory Syndrome Coronavirus, Prevalence, Public Health Surveillance, Seroepidemiologic Studies.
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.
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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<wicri:regionArea>Emergency Centre for Transboundary Animal Diseases – (ECTAD) Kenya, Food and Agriculture Organization of the United Nations (FAO), UN Office in Nairobi, Gigiri</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2107 2298</institution-id>
<institution-id institution-id-type="GRID">grid.49697.35</institution-id>
<institution>Department of Veterinary Tropical Diseases,</institution>
<institution>University of Pretoria,</institution>
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Pretoria, South Africa</nlm:aff>
<country xml:lang="fr">Afrique du Sud</country>
<wicri:regionArea>Pretoria</wicri:regionArea>
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</author>
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<series><title level="j">Ecohealth</title>
<idno type="ISSN">1612-9202</idno>
<idno type="eISSN">1612-9210</idno>
<imprint><date when="2018">2018</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animal Diseases (epidemiology)</term>
<term>Animal Diseases (transmission)</term>
<term>Animals</term>
<term>Camelus (virology)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Coronavirus Infections (veterinary)</term>
<term>Disease Reservoirs (veterinary)</term>
<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>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Kenya</term>
</keywords>
<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>
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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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<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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