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Parasit Vectors. 2015 Feb 15;8:98. doi: 10.1186/s13071-015-0659-y.

Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities.

Author information

1
Fogarty International Center, National Institute of Health, Bethesda, Maryland, 20892, USA. Kim.M.Pepin@aphis.usda.gov.
2
United States Department of Agriculture, National Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service, 4101 Laporte Ave, Fort Collins, CO, 80521, USA. Kim.M.Pepin@aphis.usda.gov.
3
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. Kim.M.Pepin@aphis.usda.gov.
4
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. clint.leach@colostate.edu.
5
Ecovec S.A, Belo Horizonte, Minas Gerais, Brazil. cecilia@ecovec.com.
6
Departamento de Parasitologia, Universidade Federal de Minas Gerais, Av. Pres. Antonio Carlos, 6627, Pampulha, Belo Horizonte, MG, Brazil. karlalaass1@yahoo.com.br.
7
Departamento de Parasitologia, Universidade Federal de Minas Gerais, Av. Pres. Antonio Carlos, 6627, Pampulha, Belo Horizonte, MG, Brazil. kellypaixaoufmg@gmail.com.
8
Fogarty International Center, National Institute of Health, Bethesda, Maryland, 20892, USA. angela.d.luis@gmail.com.
9
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. angela.d.luis@gmail.com.
10
Current address: Department of Wildlife Biology, College of Forestry and Conservation, University of Montana, Missoula, Montana, 59812, USA. angela.d.luis@gmail.com.
11
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. davidtshayman@gmail.com.
12
Department of Biology, University of Florida, Gainesville, Florida, 32611, USA. davidtshayman@gmail.com.
13
Current address: EpiLab, Infectious Disease research Centre (IDReC), Hopkirk Research Institute, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, Manawatu, New Zealand. davidtshayman@gmail.com.
14
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. Nels.Johnson@rams.colostate.edu.
15
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. michael.buhnerkempe@gmail.com.
16
Current address: Department of Ecology and Evolutionary Biology, University of California - Los Angeles, Los Angeles, California, 90095, USA. michael.buhnerkempe@gmail.com.
17
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. scott.carver@utas.edu.au.
18
School of Biological Sciences, University of Tasmania, Hobart, 7000, Australia. scott.carver@utas.edu.au.
19
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. Daniel.A.Grear@aphis.usda.gov.
20
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. kimtsao@aya.yale.edu.
21
Departamento de Parasitologia, Universidade Federal de Minas Gerais, Av. Pres. Antonio Carlos, 6627, Pampulha, Belo Horizonte, MG, Brazil. alvaro@icb.ufmg.br.
22
Fogarty International Center, National Institute of Health, Bethesda, Maryland, 20892, USA. ctwebb@lamar.colostate.edu.
23
Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA. ctwebb@lamar.colostate.edu.

Abstract

BACKGROUND:

Vector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work.

METHODS:

We used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas.

RESULTS:

We found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects.

CONCLUSIONS:

For spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos.

PMID:
25889533
PMCID:
PMC4335543
DOI:
10.1186/s13071-015-0659-y
[Indexed for MEDLINE]
Free PMC Article

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