Comparison of AICs of logistic regression models with MFAI as predictor variable in varying time lags. (PDF 83 kb

Camila Teixeira (649715)

Carolin Degener (3744999)

Cecilia de Almeida Marques-Toledo (3745007)

Danielle da Cruz Ferreira (3744997)

Liane Fetzer (3745001)

Maria Bendati (3745003)

Ălvaro Eiras (3745005)

Parasites & Vectors

Comparison of discarded models. (PDF 118 kb

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Additional file 3: Table S2. of Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

Additional file 4: Table S3. of Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

DACF, CMD, and AEE received fellowships from CNPq (Conselho Nacional de
Desenvolvimento Científico e Tecnológico) and FAPEMIG (Fundação de
Amparo a Pesquisa do Estado de Minas Gerais). This project was funded by
CNPq/DECIT-MS (Grant # 404211/2012-7), and CAPES (Grant # 3048–2011).CNPq e FAPEMIGUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Parasitologia. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Parque Tecnológico de Belo Horizonte (BHTec). Ecovec Ltda. Belo Horizonte, Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. ICB. Departamento de Bioquímica e Imunologia. Belo Horizonte, MG, Brasil.CGVS/6SMS. Vigilância de Roedores e Vetores. Porto Alegre, RS, Brasil.CGVS/6SMS. Vigilância de Roedores e Vetores. Porto Alegre, RS, Brasil.UNIFEMM. Sete Lagoas, MG, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Parasitologia. Belo Horizonte, MG, Brasil.Background: Aedes aegypti is an important vector for arboviroses and widely distributed throughout the world. Climatic factors can influence vector population dynamics and, consequently, disease transmission. The aim of this study was to characterize the temporal dynamics of an Ae. aegypti population and dengue cases and to investigate
the relationship between meteorological variables and mosquito infestation.
Methods: We monitored and analyzed the adult female Ae. aegypti population, the dengue-fever vector, in Porto Alegre, a subtropical city in Brazil using the MI-Dengue system (intelligent dengue monitoring). This system uses sticky traps to monitor weekly infestation indices. We fitted generalized additive models (GAM) with climate variables including precipitation, temperature and humidity, and a GAM that additionally included mosquito abundance in the previous week as an explanatory variable. Logistic regression was used to evaluate the effect of adult mosquito infestation on the
probability of dengue occurrence.
Results: Adult mosquito abundance was strongly seasonal, with low infestation indices during the winters and high infestation during the summers. Weekly minimum temperatures above 18 °C were strongly associated with increased mosquito abundance, whereas humidity above 75% had a negative effect on abundance. The GAM model that included adult mosquito infestation in the previous week adjusted and predicted the observed data much better than the model which included only meteorological predictor variables. Dengue was also seasonal and 98% of all cases occurred at times of high adult Ae. aegypti infestation. The probability of dengue occurrence increased by 25%, when the mean number of adult mosquitos caught by monitoring traps increased by 0.1 mosquitoes per week.
Conclusions: The results suggest that continuous monitoring of dengue vector population allows for more reliable predictions of infestation indices. The adult mosquito infestation index was a good predictor of dengue occurrence. Weekly adult dengue vector monitoring is a helpful dengue control strategy in subtropical Brazilian cities

Ferreira, Danielle Andreza da Cruz

Degener, Carolin Marlen

Toledo, Cecilia de Almeida Marques

Bendati, Maria Mercedes

Fetzer, Liane Oliveira

Teixeira, Camila P.

Eiras, Álvaro Eduardo

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

https://figshare.com/articles/Additional_file_4_Table_S3_of_Meteorological_variables_and_mosquito_monitoring_are_good_predictors_for_infestation_trends_of_Aedes_aegypti_the_vector_of_dengue_chikungunya_and_Zika/4649495

Additional file 4: Table S3. of Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

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