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1.
Cad. saúde pública ; 25(6): 1203-1214, June 2009. ilus, graf, tab
Article in English | LILACS | ID: lil-515774

ABSTRACT

Dengue fever has become the most important vector-borne viral disease in Brazil. Human facilitated transport of desiccation-resistant eggs has led to its two most important vectors, Aedes aegypti and Ae. albopictus, becoming widespread. In this paper, we report seasonal and spatial variation in larval abundances of Ae. aegypti and Ae. albopictus across a small-scale transition zone between an urban area and an urban wooded/forested area within Rio de Janeiro, Brazil. We installed 400 ovitraps across 10 sites with different human population densities and vegetation coverage. Eggs and larvae were collected for three weeks during the wet and dry seasons of 2002 and 2003. Ae. albopictus was predominantly found in the forested areas of the study site whereas in the urbanized area Ae. aegypti was more abundant. Both species peaked during the wet season. This distribution pattern, which may reflect adult flight range, may favor the co-occurrence of larvae of these species in a small-scale urban/urban forest transition zone.


Dengue tornou-se a mais importante doença viral transmitida por vetores no Brasil. O transporte humano de ovos resistentes à dessecação permitiu que seus dois mais importantes vetores, Aedes aegypti e Ae. albopictus, se tornassem cosmopolitas. Neste artigo, nós avaliamos a variação sazonal e espacial da abundância de larvas de Ae. aegyptie Ae. albopictus por meio de uma pequena escala espacial de zona de transição entre uma área urbana e uma área florestal do Rio de Janeiro, Brasil. Foram instaladas quarenta ovitrampas em cada área dos dez locais caracterizados por diferente densidade populacional humana e cobertura vegetal. Ovos e larvas foram coletados durante três semanas no verão e inverno de 2002 e 2003. Ae. albopictus foi predominantemente encontrado nas áreas com maior cobertura vegetal enquanto que nas áreas urbanizadas (comunidades), Ae. aegypti foi mais abundante. Ambas as espécies apresentaram pico durante a estação chuvosa. Esse padrão de distribuição das formas imaturas pode refletir no raio de vôo do adulto, favorecer a co-ocorrência de larvas dessas espécies nessa pequena escala espacial da zona de transição entre as áreas urbana e de floresta.


Subject(s)
Animals , Humans , Aedes/physiology , Dengue/transmission , Ecosystem , Insect Vectors/physiology , Analysis of Variance , Aedes/classification , Brazil , Disease Reservoirs , Insect Vectors/growth & development , Larva , Population Density , Seasons , Trees , Urban Population , Urbanization
2.
Mem. Inst. Oswaldo Cruz ; 102(3): 299-302, June 2007. mapas, graf, tab
Article in English | LILACS | ID: lil-452505

ABSTRACT

Malaria control has been directed towards regional actions where more detailed knowledge of local determinants of transmission is of primary importance. This is a short report on range distribution and biting indices for Anopheles darlingi and An. albitarsis during the dry and rainy season that follows river level variation in a savanna/alluvial forest malaria system area in the Northern Amazon Basin. Distribution range and adult biting indices were at their highest during the rainy season for both An. darlingi and An. albitarsis. During the rainy season the neighboring alluvial forest was extensively flooded. This coincided with highest rates in malaria transmission with case clustering near the river. As the river receded, anopheline distribution range and density decreased. This decrease in distribution and density corresponded to a malaria decrease in the near area. An exponential regression function was derived to permit estimations of An. darlingi distribution over specified distances. Anopheline spatio-temporal variations lead to uneven malaria case distribution and are of important implications for control strategies.


Subject(s)
Humans , Animals , Anopheles/physiology , Feeding Behavior , Insect Bites and Stings , Insect Vectors/physiology , Anopheles/classification , Brazil , Insect Vectors/classification , Malaria/transmission , Population Density , Population Dynamics , Seasons
3.
Mem. Inst. Oswaldo Cruz ; 102(3): 349-358, June 2007. mapas, tab
Article in English | LILACS | ID: lil-452513

ABSTRACT

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.


Subject(s)
Humans , Animals , Culicidae , Ecosystem , Insect Vectors , Malaria/prevention & control , Malaria/transmission , Algorithms , Brazil , Cluster Analysis , Population Density , Principal Component Analysis , Seasons , Tropical Climate
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