Transovarial transmission of DENV in Aedes aegypti in the Amazon basin: a local model of xenomonitoring View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Cristiano Fernandes da Costa, Ricardo Augusto dos Passos, José Bento Pereira Lima, Rosemary Aparecida Roque, Vanderson de Souza Sampaio, Thais Bonifácio Campolina, Nágila Francinete Costa Secundino, Paulo Filemon Paolucci Pimenta

ABSTRACT

BACKGROUND: Transovarial transmission of dengue virus in Aedes spp. mosquitoes is considered an important mechanism for the maintenance of the virus in nature and may be implicated in the occurrence of outbreaks and epidemics of the disease. However, there are few studies involving transovarial transmission and viral vector monitoring as a surveillance tool and control strategy. The present study evaluated transovarial transmission of dengue virus in Aedes aegypti populations as a xenomonitoring strategy in municipalities of the Amazonas state. RESULTS: Aedes sp. eggs (13.164) were collected, with 30% viability of third- and fourth-instar larvae. Transovarial transmission of DENV was detected in all municipalities. The transovarial infection rate (TOR) in the municipalities was 46% of the DENV positive samples. The minimum infection rate (MIR) was 17.7 in the state, varying from 11.4 to 24.1 per 1,000 larvae tested in the respective municipalities. Four DENV serotypes were identified, with DENV I and IV being present in all municipalities investigated. The number of reported dengue fever cases varied during this period. CONCLUSIONS: Our results suggest that transovarial transmission may be an important mechanism for the maintenance and spreading of the disease in Amazonas municipalities. Using qRT-PCR, it was possible to identify the four DENV serotypes in larval samples. The methodology used in the present study proved suitable as a DENV xenomonitoring model in immature mosquitoes, contributing to the development of systems for early detection of viral circulation and predictive models for the occurrence of outbreaks and epidemics of this disease. TRIAL REGISTRATION: CAAE34025414200005015 . More... »

PAGES

249

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13071-017-2194-5

DOI

http://dx.doi.org/10.1186/s13071-017-2194-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085444048

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28526066


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