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Previous issue date: 2009Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz - FiocruzPrograma de Computação Científica - FiocruzSecretaria Municipal de Saúde do Rio de Janeiro - Fundação Nacional de SaúdeInstituto de Comunicação e Informação Científica e Tecnológica em Saúde - ICICT - Fiocruz,Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz - FiocruzDengue dynamics in Rio de Janeiro, Brazil, as in many dengue-endemic regions of the
world, is seasonal, with peaks during the wetÐhot months. This temporal pattern is generally attributed
to the dynamics of its mosquito vector Aedes aegypti (L.). The objectives of this study were to
characterize the temporal pattern of Ae. aegypti population dynamics in three neighborhoods of Rio
de Janeiro and its association with local meteorological variables; and to compare positivity and density
indices obtained with ovitraps and MosquiTraps. The three neighborhoods are distinct in vegetation
coverage, sanitation, water supply, and urbanization. Mosquito sampling was carried out weekly, from
September 2006 to March 2008, a period during which large dengue epidemics occurred in the city.
Our results show peaks of oviposition in early summer 2007 and late summer 2008, detected by both
traps. The ovitrap provided a more sensitive index than MosquiTrap. The MosquiTrap detection
threshold showed high variation among areas, corresponding to a mean egg density of 25Ð52 eggs
per ovitrap. Both temperature and rainfall were signiÞcantly related to Ae. aegypti indices at a short
(1 wk) time lag. Our results suggest that mean weekly temperature above 22Ð24 C is strongly
associated with high Ae. aegypti abundance and consequently with an increased risk of dengue
transmission. Understanding the effects of meteorological variables on Ae. aegypti population dynamics
will help to target control measures at the times when vector populations are greatest,
contributing to the development of climate-based control and surveillance measures for dengue fever
in a hyperendemic area
