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dc.contributor.authorPepin, KMen_US
dc.contributor.authorLeach, CBen_US
dc.contributor.authorMarques-Toledo, Cen_US
dc.contributor.authorLaass, KHen_US
dc.contributor.authorPaixao, KSen_US
dc.contributor.authorLuis, ADen_US
dc.contributor.authorHayman, DTSen_US
dc.contributor.authorJohnson, NGen_US
dc.contributor.authorBuhnerkempe, MGen_US
dc.contributor.authorCarver, Sen_US
dc.contributor.authorGrear, DAen_US
dc.contributor.authorTsao, Ken_US
dc.contributor.authorEiras, AEen_US
dc.contributor.authorWebb, CTen_US
dc.coverage.spatialEnglanden_US
dc.date.available2015-02-15en_US
dc.date.available2015-01-12en_US
dc.date.issued2015-02-15en_US
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/25889533en_US
dc.identifier10.1186/s13071-015-0659-yen_US
dc.identifier.citationParasit Vectors, 2015, 8 pp. 98 - ?en_US
dc.description.abstractBACKGROUND: 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.en_US
dc.format.extent98 - ?en_US
dc.languageengen_US
dc.subjectAnimalsen_US
dc.subjectBrazilen_US
dc.subjectCitiesen_US
dc.subjectDengueen_US
dc.subjectDisease Transmission, Infectiousen_US
dc.subjectEpidemiological Monitoringen_US
dc.subjectHealth Care Rationingen_US
dc.subjectHumansen_US
dc.subjectModels, Statisticalen_US
dc.subjectMosquito Controlen_US
dc.subjectSpatio-Temporal Analysisen_US
dc.titleUtility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities.en_US
dc.typeJournal Article
dc.citation.volume8en_US
dc.identifier.doi10.1186/s13071-015-0659-yen_US
dc.identifier.elements-id232364
dc.relation.isPartOfParasit Vectorsen_US
dc.identifier.eissn1756-3305en_US
dc.description.publication-statusPublished onlineen_US
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/Institute of Vet, Animal & Biomed Science
pubs.notesNot knownen_US
dc.subject.anzsrc1108 Medical Microbiologyen_US
dc.subject.anzsrc1117 Public Health And Health Servicesen_US


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