Effectiveness of normalized difference water index in modelling<i>Aedes aegypti</i>house index

Estallo, Elizabet Lilia; Ludueña-Almeida, Francisco; Visintin, Andrés Mario; Scavuzzo, Carlos Marcelo; Lamfri, Mario; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

doi:10.1080/01431161.2011.640962

Cited by 34 publications

(43 citation statements)

References 28 publications

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“…This study improved previous epidemiology studies, which consider statistical models with linear relationships [47,36,37]. Such improvement is obtained by the use of Machine Learning tools that impose the user no significant additional effort.…”

Section: Discussionmentioning

confidence: 68%

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Modeling Dengue vector population using remotely sensed data and machine learning

et al. 2018

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Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a support vector machine, an artificial neural networks, a K-nearest neighbors and a decision tree regressor. Considerations are made on parameter tuning and the validation and training approach. The results are compared to linear models used in previous works with similar data sets for generating temporal predictive models. These new tools perform better than linear approaches, in particular nearest neighbor regression (KNNR) performs the best. These results provide better alternatives to be implemented operatively on the Argentine geospatial risk system that is running since 2012.

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Section: Discussionmentioning

confidence: 68%

“…Finally this work presents several improvements regarding previous works [36,37,38,48], in terms of temporal data length, the use a more complete accessible operatively set of remotely sensed variables, and mostly with respect to the use of ML learning modeling.…”

Section: Discussionmentioning

confidence: 99%

Modeling Dengue vector population using remotely sensed data and machine learning

et al. 2018

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“…In several studies, the importance of variables has been demonstrated, being the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) both of the most used variables in Ae. aegypti models developed [6–8].…”

Section: Introductionmentioning

confidence: 99%

Temporal models using environmental variables to predict Aedes aegypti oviposition activity in a temperate region of Argentina

Benitez

Estallo

Grech

et al. 2019

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Environmental variables are some of the factors that more impact on Aedes aegypti, vector of Dengue, Chikungunya and Zika viruses. In this study, the Ae. aegypti oviposition activity was related to satellite and meteorological variables, in Córdoba City (Argentina). Eggs were collected using ovitraps placed throughout the city from 2009 to 2012, replaced them weekly. Generalized Linear Mixed Models were developed with negative binomial distributions of errors, modeling average number of eggs collected weekly as a function of satellite and meteorological variables with time lags. The best model included a vegetation index, vapor pressure of water, precipitation and photoperiod, lagged between 3 and 4 weeks. By each increment unit in vegetation index, vapor pressure (hPa) and light hour, the average number of eggs increases by 72%, 20% and 29%, respectively, in the following 3 or 4 weeks. In addition, the average number of eggs decreases a 50% in the following month by each millimeter of rain. The results evince the relevant importance of the vegetation, maybe due to the shade that provide to the containers as breeding habitat of this species and the shelter for adults. On the other hand, the negative effect of precipitation could be a consequence of abundant rainfalls that fulfill containers avoiding females to lay eggs in there. Although no significant effect of the photoperiod on the vector abundance has been detected in the north of the country, in Córdoba City an important effect is observed when it is presented together with other variables, producing different effects of these variables in different regions. Lastly, it is necessary to emphasize the importance of the minimum temperature in temperate zones, since it would be a limiting factor that prevents the Ae. aegypti oviposition activity.

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“…the study by McCann et al (2010) where the risk was estimated using post codes with a mean surface area of 2,000 km 2 . Mapping potential habitats at a finer spatial scale could substantially improve the temporal and spatial resolution of current risk maps and create novel possibilities for improved disease management based on better understanding of transmission dynamics at the local habitat scale (Lacaux et al, 2007;Simoonga et al, 2009;Charlier et al, 2011;Estallo et al, 2012). Flexible, automated and operational tools capable of characterising vector habitats at high resolutions are, however, currently lacking.…”

Section: Introductionmentioning

confidence: 99%

“…However, few initiatives have been undertaken to monitor SWBs using VHR satellite imagery (Dambach et al, 2009;Soti et al, 2009;Soti et al, 2010). Many studies use the freely available Landsat imagery (about 30-m resolution) and medium (20 m to 100 m) or coarse (>100 m) resolution imagery (Fuentes et al, 2001;Malone et al, 2001;Guo et al, 2005;Daniel et al, 2006;de Castro et al, 2006;Estallo et al, 2012). To study the typically small liver fluke vector habitats at the farm level, however, these relatively low resolutions are insufficient.…”

Section: Introductionmentioning

confidence: 99%

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

et al. 2014

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Abstract. The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m 2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

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Effectiveness of normalized difference water index in modellingAedes aegyptihouse index

Cited by 34 publications

References 28 publications

Modeling Dengue vector population using remotely sensed data and machine learning

Modeling Dengue vector population using remotely sensed data and machine learning

Temporal models using environmental variables to predict Aedes aegypti oviposition activity in a temperate region of Argentina

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

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