title: Surveillance of dengue vectors using spatio-temporal Bayesian modeling.
abstract: BACKGROUND
  At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics of A. aegypti infestation with oviposition traps, using efficient computational methods. These methods will allow for the implementation of the proposed model and methodology into surveillance and monitoring systems.
  
  
  METHODS
  The study area includes a region in the municipality of Rio de Janeiro, characterized by high population density, precarious domicile construction, and a general lack of infrastructure around it. Two hundred and forty traps were distributed in eight different sentinel areas, in order to continually monitor immature Aedes aegypti and Aedes albopictus mosquitoes. Collections were done weekly between November 2010 and August 2012. The relationship between egg number and climate and environmental variables was considered and evaluated through Bayesian zero-inflated spatio-temporal models. Parametric inference was performed using the Integrated Nested Laplace Approximation (INLA) method.
  
  
  RESULTS
  Infestation indexes indicated that ovipositing occurred during the entirety of the study period. The distance between each trap and the nearest boundary of the study area, minimum temperature and accumulated rainfall were all significantly related to the number of eggs present in the traps. Adjusting for the interaction between temperature and rainfall led to a more informative surveillance model, as such thresholds offer empirical information about the favorable climatic conditions for vector reproduction. Data were characterized by moderate time (0.29 - 0.43) and spatial (21.23 - 34.19 m) dependencies. The models also identified spatial patterns consistent with human population density in all sentinel areas. The results suggest the need for weekly surveillance in the study area, using traps allocated between 18 and 24 m, in order to understand the dengue vector dynamics.
  
  
  CONCLUSIONS
  Aedes aegypti, due to it short generation time and strong response to climate triggers, tend to show an eruptive dynamics that is difficult to predict and understand through just temporal or spatial models. The proposed methodology allowed for the rapid and efficient implementation of spatio-temporal models that considered zero-inflation and the interaction between climate variables and patterns in oviposition, in such a way that the final model parameters contribute to the identification of priority areas for entomological surveillance.
authors.affiliation: Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Rua Leopoldo Bulhões 1.480, Rio de Janeiro, Brazil. ana.costa@iff.fiocruz.br.|National Institute of Women, Children and Adolescents Health Fernandes Figueira, Department of Clinical Research Oswaldo Cruz Foundation, Avenida Rui Barbosa 716, Rio de Janeiro, Brazil. ana.costa@iff.fiocruz.br.
authors.forename: Ana Carolina
authors.initials: AC
authors.lastname: C Costa
authors.affiliation: Scientific Computing Program, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.
authors.forename: Cláudia T
authors.initials: CT
authors.lastname: Codeço
authors.affiliation: Laboratory of Transmitters of Hematozoa, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.|Sentinel Operational Unit of Mosquito Vectors, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.
authors.forename: Nildimar A
authors.initials: NA
authors.lastname: Honório
authors.affiliation: Sentinel Operational Unit of Mosquito Vectors, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.
authors.forename: Gláucio R
authors.initials: GR
authors.lastname: Pereira
authors.affiliation: Sentinel Operational Unit of Mosquito Vectors, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.
authors.forename: Carmen Fátima
authors.initials: CF
authors.lastname: N Pinheiro
authors.affiliation: Scientific Computing Program, Oswaldo Cruz Foundation, Avenida Brasil 4365, Rio de Janeiro, Brazil.
authors.forename: Aline A
authors.initials: AA
authors.lastname: Nobre
mesh_terms: D000330:Aedes; D000818:Animals; D001499:Bayes Theorem; D001938:Brazil; D003715:Dengue / Q000453:epidemiology / Q000635:transmission; D062665:Epidemiological Monitoring; D006801:Humans; D007303:Insect Vectors; D015233:Models, Statistical