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Article

Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

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Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘G.Caporale’, 64100 Teramo, Italy
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Progressive Systems Srl, Frascati, 00044 Rome, Italy
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AImageLab, Engineering Department “Enzo Ferrari”, University of Modena and Reggio Emilia, 41121 Modena, Italy
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 3064; https://doi.org/10.3390/rs12183064
Received: 7 August 2020 / Revised: 15 September 2020 / Accepted: 17 September 2020 / Published: 19 September 2020
West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV. View Full-Text
Keywords: Satellite Earth Observation data; West Nile Virus; surveillance; XGBoost; Italy; modelling; MODIS; Copernicus; soil moisture Satellite Earth Observation data; West Nile Virus; surveillance; XGBoost; Italy; modelling; MODIS; Copernicus; soil moisture
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MDPI and ACS Style

Candeloro, L.; Ippoliti, C.; Iapaolo, F.; Monaco, F.; Morelli, D.; Cuccu, R.; Fronte, P.; Calderara, S.; Vincenzi, S.; Porrello, A.; D’Alterio, N.; Calistri, P.; Conte, A. Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model. Remote Sens. 2020, 12, 3064. https://doi.org/10.3390/rs12183064

AMA Style

Candeloro L, Ippoliti C, Iapaolo F, Monaco F, Morelli D, Cuccu R, Fronte P, Calderara S, Vincenzi S, Porrello A, D’Alterio N, Calistri P, Conte A. Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model. Remote Sensing. 2020; 12(18):3064. https://doi.org/10.3390/rs12183064

Chicago/Turabian Style

Candeloro, Luca, Carla Ippoliti, Federica Iapaolo, Federica Monaco, Daniela Morelli, Roberto Cuccu, Pietro Fronte, Simone Calderara, Stefano Vincenzi, Angelo Porrello, Nicola D’Alterio, Paolo Calistri, and Annamaria Conte. 2020. "Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model" Remote Sensing 12, no. 18: 3064. https://doi.org/10.3390/rs12183064

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