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Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

C Costa AC, Codeço CT, Honório NA, Pereira GR, N Pinheiro CF, Nobre AA - BMC Med Inform Decis Mak (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus

Temporal Series. Time series of the average weekly egg density and the zero-egg collection week frequency in each of the eight SAs (a), accumulated rainfall (lag 2) and minimum temperature (lag 1) (b)
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Fig2: Temporal Series. Time series of the average weekly egg density and the zero-egg collection week frequency in each of the eight SAs (a), accumulated rainfall (lag 2) and minimum temperature (lag 1) (b)

Mentions: Figure 2a shows weekly average egg density in each of the eight sentinel areas, together with zero-egg frequency, that varied from 24 to 58 %. Ignoring zero-inflation leads to two possible consequences: 1) biased estimation of model parameters and standard errors, and 2) overdispersion. Due to the high frequency of zeros, a zero-inflated model was considered. The modeling approach was: first, use a Binomial (Bin) distribution to model the zero occurrence probability. Then, model the non- observations using a Zero-Altered Poisson (ZAP) distribution [10]. The underlying assumption is that two separate ecological processes are occurring: presence of eggs is driven by the mosquito choice of the ovitrap for ovipositing, and the abundance of eggs is driven by the number of females that chose the ovitrap. This can be formalized as following: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\begin{array}{@{}rcl@{}} \begin{array}{c} f_{ZAP}(y;\zeta,\mu)=\left\{ \begin{array}{l} 1-\zeta \;\qquad\qquad\qquad\quad\quad\quad\quad\;\; y=0\\ \zeta \times f_{ZAP}\left(y;\mu\right)\;\quad\quad\quad\quad\quad\quad\;\; y>0, \end{array} \right. \end{array} \end{array} $$ \end{document}fZAP(y;ζ,μ)=1−ζy=0ζ×fZAPy;μy>0,Fig. 2


Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

C Costa AC, Codeço CT, Honório NA, Pereira GR, N Pinheiro CF, Nobre AA - BMC Med Inform Decis Mak (2015)

Temporal Series. Time series of the average weekly egg density and the zero-egg collection week frequency in each of the eight SAs (a), accumulated rainfall (lag 2) and minimum temperature (lag 1) (b)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4644323&req=5

Fig2: Temporal Series. Time series of the average weekly egg density and the zero-egg collection week frequency in each of the eight SAs (a), accumulated rainfall (lag 2) and minimum temperature (lag 1) (b)
Mentions: Figure 2a shows weekly average egg density in each of the eight sentinel areas, together with zero-egg frequency, that varied from 24 to 58 %. Ignoring zero-inflation leads to two possible consequences: 1) biased estimation of model parameters and standard errors, and 2) overdispersion. Due to the high frequency of zeros, a zero-inflated model was considered. The modeling approach was: first, use a Binomial (Bin) distribution to model the zero occurrence probability. Then, model the non- observations using a Zero-Altered Poisson (ZAP) distribution [10]. The underlying assumption is that two separate ecological processes are occurring: presence of eggs is driven by the mosquito choice of the ovitrap for ovipositing, and the abundance of eggs is driven by the number of females that chose the ovitrap. This can be formalized as following: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\begin{array}{@{}rcl@{}} \begin{array}{c} f_{ZAP}(y;\zeta,\mu)=\left\{ \begin{array}{l} 1-\zeta \;\qquad\qquad\qquad\quad\quad\quad\quad\;\; y=0\\ \zeta \times f_{ZAP}\left(y;\mu\right)\;\quad\quad\quad\quad\quad\quad\;\; y>0, \end{array} \right. \end{array} \end{array} $$ \end{document}fZAP(y;ζ,μ)=1−ζy=0ζ×fZAPy;μy>0,Fig. 2

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus