Analytical report of the 2016 dengue outbreak in Córdoba city, Argentina

Rotela, Camilo H.; López, Laura; Céspedes, Maria Frias; Barbás, Gabriela; Lighezzolo, Andres; Porcasi, Ximena; Lanfri, Mario; Scavuzzo, Carlos Marcelo; Gorla, David Eladio

doi:10.4081/gh.2017.564

Cited by 25 publications

(30 citation statements)

References 33 publications

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“…Major disease carrier vectors with spread and population depending on environmental conditions include mosquito species, ticks and rodents [15][16][17][18]. Mapping and predicting vector and diseases spread through EO data can provide reliable information in outbreak early warning, hotspots location, and public health budget optimization [19,20]. To this aim, there have been seminal works considering how to include major environmental variables obtainable from EO data such as surface temperature, vegetation condition, air quality, precipitation rate, humidity conditions, night-time lights, land cover types, degree of urbanization and velocity of the urban sprawl, among others [21][22][23][24].According to the data provided by the World Health Organization, over 50% of the world population is exposed to the risk of mosquito-borne diseases [18,25], the ones with the greatest clinical importance being Zika, Chikungunya, Dengue and Yellow fever virus diseases.…”

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confidence: 99%

“…These diseases are transmitted mainly by the female Ae. aegypti mosquito species [16,19]. While its male counterpart feeds only on fruit nectar, the female Ae.…”

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confidence: 99%

“…The Ae. aegypti species is known to be adaptable to urban environments due to its inclination to being bred in artificial containers [19,21,27,28]. Spread of the causal diseases by this vector is known to correlate with the local adult vector population [29].…”

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confidence: 99%

“…It is a collection of methods that provide multivariate, nonlinear and non-parametric regression or classification of data. ML has proven useful for a very large number of EO system applications such as crop disease detection [35], urban area classification [23], landscape epidemiology [19,21,27], and various other bio-geographical information extraction and regression analysis procedures [33,34].In landscape epidemiology, ML algorithms can be used to map relationships between proxy environmental conditions and ground collected vector population information for the purpose of prediction, classification and/or explanation of environmental condition effects on vector spread [21,27]. Previous attempts in modeling the population of female Ae.…”

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confidence: 99%

“…Previous attempts in modeling the population of female Ae. aegypti and other species of mosquitoes have employed ML algorithms including maximum entropy methods, support vector regression (SVR), decision trees regression (DTR), k-nearest neighbor regression (KNN), artificial neural networks (ANN), multilayer perceptron (MLP), among others [19][20][21]27].In spite of the works in this study domain, there are still some gaps. One of such is in the selecting the most informative environmental features subset to obtain the model with best fit and least redundancy.…”

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Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

Mudele¹,

Bayer²,

Zanandrez³

et al. 2020

Preprint

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Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most arXiv:1911.08979v2 [q-bio.PE] 26 Nov 2019 A PREPRINT -NOVEMBER 28, 2019 informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be empirically interpreted for use in vector control activities.Keywords Ae. aegypti · Machine learning · Random forest · Remote sensing IntroductionStudying urban ecosystems is a hot topic within various scientific clusters [1][2][3][4][5]. The compounding effects of human activities through urbanization, carbon emission and other biodiversity changes are modifying urban ecosystems, thus creating a need for continuous assessment of the environment to ensure health and quality of life suitability for dwellers [6]. With a repository covering over 40 years of data collected, Earth observation (EO) data from orbiting satellites present a gold mine for environmental change monitoring [7]. Due to recent advances resulting in higher resolution sensors (spectral, spatial and temporal), freely accessible data, and efficient processing algorithms, it is possible to extract static and dynamic phenomena happening on the Earth surface and use this information for various environmental characteriza...

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confidence: 99%

“…These diseases are transmitted mainly by the female Ae. aegypti mosquito species [16,19]. While its male counterpart feeds only on fruit nectar, the female Ae.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

Mudele¹,

Bayer²,

Zanandrez³

et al. 2020

Preprint

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show abstract

Modeling Dengue vector population using remotely sensed data and machine learning

et al. 2018

Self Cite

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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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Modeling dengue vector population with earth observation data and a generalized linear model

Mudele

Frery

Zanandrez³

et al. 2021

Acta Tropica

View full text Add to dashboard Cite

Mosquitoes are vectors of many human diseases. In particular, Aedes aegypti (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 aegypti (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.

show abstract

Analytical report of the 2016 dengue outbreak in Córdoba city, Argentina

Cited by 25 publications

References 33 publications

Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

Modeling Dengue vector population using remotely sensed data and machine learning

Modeling dengue vector population with earth observation data and a generalized linear model

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