Maisa Carla Pereira Parra1, Eliane Aparecida Fávaro2, Margareth Regina Dibo3, Adriano Mondini4, Álvaro Eduardo Eiras5, Erna Geessien Kroon6, Mauro Martins Teixeira7, Mauricio Lacerda Nogueira8, Francisco Chiaravalloti-Neto9. 1. Laboratório de Pesquisa em Virologia, Faculdade de Medicina de São José do Rio Preto, Avenida Brigadeiro Faria Lima 5416, São José do Rio Preto, SP, Brazil. Electronic address: maisapparra@hotmail.com. 2. Laboratório de Pesquisa em Virologia, Faculdade de Medicina de São José do Rio Preto, Avenida Brigadeiro Faria Lima 5416, São José do Rio Preto, SP, Brazil. Electronic address: eliane_favaro@yahoo.com.br. 3. Laboratório de Entomologia, Superintendência de Controle de Endemias, Rua Cardeal Arcoverde 2878, 05408003, São Paulo, SP, Brazil. Electronic address: medibo@sucen.sp.gov.br. 4. Laboratório de Saúde Pública, Faculdade de Ciências Farmacêuticas, Campus Araraquara, Universidade Estadual Paulista (UNESP), Rodovia Araraquara-Jaú km 1, Araraquara, SP, Brazil. Electronic address: amondini@fcfar.unesp.br. 5. Departamento de Parasitologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos 6627, Belo Horizonte, MG, Brazil. Electronic address: alvaro@icb.ufmg.br. 6. Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos 6627, Belo Horizonte, MG, Brazil. Electronic address: kroone@icb.ufmg.br. 7. Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos 6627, Belo Horizonte, MG, Brazil. Electronic address: mmtex@icb.ufmg.br. 8. Laboratório de Pesquisa em Virologia, Faculdade de Medicina de São José do Rio Preto, Avenida Brigadeiro Faria Lima 5416, São José do Rio Preto, SP, Brazil. Electronic address: mnogueira@famerp.br. 9. Departamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo, Avenida Doutor Arnaldo 715, São Paulo, SP, Brazil. Electronic address: franciscochiara@usp.br.
Abstract
INTRODUCTION: Traditional indices for measuring dengue fever risk in a given area are based on the immature forms of the vector (larvae and pupae surveys). However, this is inefficient because only adult female mosquitoes actually transmit the virus. Based on these assumptions, our objective was to evaluate the association between an entomological index obtained from adult mosquito traps and the occurrence of dengue in a hyperendemic area. Additionally, we compared its cost to that of the Breteau Index (BI). MATERIAL AND METHODS: We performed this study in São José do Rio Preto, SP, Brazil, between the epidemiological weeks of 36/2012 and 19/2013. BG-Sentinel and BG-Mosquitito traps were installed to capture adult mosquitoes. Positive and negative cases of dengue fever were computed and geocoded. We generated biweekly thematic maps of the entomological index, generated by calculating the number of adult Aedes aegypti females (NAF) per 100 households during a week by kriging, and based on the number of mosquitoes captured. The relation between the occurrence of dengue fever and the NAF was tested using a spatial case-control design and a generalized additive model and was controlled by the coordinates of the positive and negative cases of dengue fever. RESULTS: Our analyses showed that increases in dengue fever cases occurred in parallel with increases in the number of Ae. aegypti females. The entomological index produced in our study correlates positively with the incidence of dengue, particularly during intervals when vector control measures were applied less intensively. The operational costs of our index were lower than those of the BI: NAF used 71.5% less human resources necessary to measure the BI. CONCLUSIONS: Spatial analysis techniques and the number of adult Ae. aegypti females were used to produce an indicator of dengue risk. The index can be applied at various levels of spatial aggregation for an entire study area, as well as for sub-areas, such as city blocks. Even though the index is adequate to predict dengue risk, it should be tested and validated in various scenarios before routine use.
INTRODUCTION: Traditional indices for measuring dengue fever risk in a given area are based on the immature forms of the vector (larvae and pupae surveys). However, this is inefficient because only adult female mosquitoes actually transmit the virus. Based on these assumptions, our objective was to evaluate the association between an entomological index obtained from adult mosquito traps and the occurrence of dengue in a hyperendemic area. Additionally, we compared its cost to that of the Breteau Index (BI). MATERIAL AND METHODS: We performed this study in São José do Rio Preto, SP, Brazil, between the epidemiological weeks of 36/2012 and 19/2013. BG-Sentinel and BG-Mosquitito traps were installed to capture adult mosquitoes. Positive and negative cases of dengue fever were computed and geocoded. We generated biweekly thematic maps of the entomological index, generated by calculating the number of adult Aedes aegypti females (NAF) per 100 households during a week by kriging, and based on the number of mosquitoes captured. The relation between the occurrence of dengue fever and the NAF was tested using a spatial case-control design and a generalized additive model and was controlled by the coordinates of the positive and negative cases of dengue fever. RESULTS: Our analyses showed that increases in dengue fever cases occurred in parallel with increases in the number of Ae. aegypti females. The entomological index produced in our study correlates positively with the incidence of dengue, particularly during intervals when vector control measures were applied less intensively. The operational costs of our index were lower than those of the BI: NAF used 71.5% less human resources necessary to measure the BI. CONCLUSIONS: Spatial analysis techniques and the number of adult Ae. aegypti females were used to produce an indicator of dengue risk. The index can be applied at various levels of spatial aggregation for an entire study area, as well as for sub-areas, such as city blocks. Even though the index is adequate to predict dengue risk, it should be tested and validated in various scenarios before routine use.
Authors: Oscar Alberto Newton-Sánchez; Miriam de la Cruz Ruiz; Yisel Torres-Rojo; Hector Ochoa-Diaz-López; Iván Delgado-Enciso; Carlos Moises Hernandez-Suarez; Francisco Espinoza-Gomez Journal: Int J Public Health Date: 2020-03-17 Impact factor: 3.380
Authors: Gabriel Ribeiro Dos Santos; Darunee Buddhari; Sopon Iamsirithaworn; Direk Khampaen; Alongkot Ponlawat; Thanyalak Fansiri; Aaron Farmer; Stefan Fernandez; Stephen Thomas; Isabel Rodriguez Barraquer; Anon Srikiatkhachorn; Angkana T Huang; Derek A T Cummings; Timothy Endy; Alan L Rothman; Henrik Salje; Kathryn B Anderson Journal: J Infect Dis Date: 2022-10-17 Impact factor: 7.759
Authors: Luis Fernando Chaves; José Angel Valerín Cordero; Gabriela Delgado; Carlos Aguilar-Avendaño; Ezequías Maynes; José Manuel Gutiérrez Alvarado; Melissa Ramírez Rojas; Luis Mario Romero; Rodrigo Marín Rodríguez Journal: Curr Res Parasitol Vector Borne Dis Date: 2021-02-09
Authors: Sophie A Lee; Christopher I Jarvis; W John Edmunds; Theodoros Economou; Rachel Lowe Journal: J R Soc Interface Date: 2021-05-26 Impact factor: 4.118