Dataset Information

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.

SUBMITTER: C Costa AC

PROVIDER: S-EPMC4644323 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

C Costa Ana Carolina AC Codeço Cláudia T CT Honório Nildimar A NA Pereira Gláucio R GR N Pinheiro Carmen Fátima CF Nobre Aline A AA

BMC medical informatics and decision making 20151113

<h4>Background</h4>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 ...[more]

PMID: 26566610

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Project description:BackgroundRapidly increasing temperatures in the mountain region of Nepal and recent reports of dengue fever and lymphatic filariasis cases from mountainous areas of central Nepal prompted us to study the spatio-temporal distribution of the vectors of these two diseases along an altitudinal transect in central Nepal.Methodology/principal findingsWe conducted a longitudinal study in four distinct physiographical regions of central Nepal from September 2011 to February 2012. We used BG-Sentinel and CDC light traps to capture adult mosquitoes. We found the geographical distribution of the dengue virus vectors Aedes aegypti and Aedes albopictus along our study transect to extend up to 1,310 m altitude in the Middle Mountain region (Kathmandu). The distribution of the lymphatic filariasis vector Culex quinquefasciatus extended up to at least 2,100 m in the High Mountain region (Dhunche). Statistical analysis showed a significant effect of the physiographical region and month of collection on the abundance of A. aegypti and C. quinquefasciatus only. BG-Sentinel traps captured significantly higher numbers of A. aegypti than CDC light traps. The meteorological factors temperature, rainfall and relative humidity had significant effects on the mean number of A. aegypti per BG-Sentinel trap. Temperature and relative humidity were significant predictors of the number of C. quinquefasciatus per CDC light trap. Dengue fever and lymphatic filariasis cases had previously been reported from all vector positive areas except Dhunche which was free of known lymphatic filariasis cases.Conclusions/significanceWe conclude that dengue virus vectors have already established stable populations up to the Middle Mountains of Nepal, supporting previous studies, and report for the first time the distribution of lymphatic filariasis vectors up to the High Mountain region of this country. The findings of our study should contribute to a better planning and scaling-up of mosquito-borne disease control programmes in the mountainous areas of Nepal.

Project description:BackgroundSpatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques.Methodology/principal findingsWe obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River.Conclusion/significanceBayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.

Dataset Information

Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

Background

Methods

Results

Conclusions

Publications

Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

Similar Datasets

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