Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series

V. Andreo, E. Izquierdo-Verdiguier, R. Zurita-Milla, R. Rosà, Annapaola Rizzoli, A. Papa

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

This study presents the first results of the use of co-clustering to identify potential spatial and temporal concurrences of favourable conditions for the emergence and maintenance of West Nile Virus (WNV) in Greece. We applied the Bregman block average co-clustering algorithm with I-divergence to various time series (from 2003 to 2016) of indices derived from Land Surface Temperature (LST) reconstructed from MODIS products. The results show that the combination of two temporal and three spatial groups performs best in identifying times and areas with and without WNV human cases, yielding smaller standard deviations in co-clusters. Among the indices that appeared to perform better we found: number of summer days, annual average of mean and maximum LST, potential number of mosquito and virus cycles (EIP) and mean LST of the WNV transmission season. These variables are consistent with known effects of temperature over mosquito development and reproduction as well as virus amplification. Further research will be carried out to identify groups of variables that cluster both in space and time.
Original languageEnglish
Title of host publication IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages4670-4673
Number of pages4
ISBN (Electronic)978-1-5386-7150-4
DOIs
Publication statusPublished - 5 Nov 2018
Event38th IEEE International Geoscience and Remote Sensing Symposium 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38
https://www.igarss2018.org/

Conference

Conference38th IEEE International Geoscience and Remote Sensing Symposium 2018
Abbreviated titleIGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18
Internet address

Fingerprint

West Nile virus
land surface
surface temperature
time series
mosquito
virus
MODIS
amplification
divergence
summer
temperature
index

Cite this

Andreo, V., Izquierdo-Verdiguier, E., Zurita-Milla, R., Rosà, R., Rizzoli, A., & Papa, A. (2018). Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 4670-4673). IEEE. https://doi.org/10.1109/IGARSS.2018.8519542
Andreo, V. ; Izquierdo-Verdiguier, E. ; Zurita-Milla, R. ; Rosà, R. ; Rizzoli, Annapaola ; Papa, A. / Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. pp. 4670-4673
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Andreo, V, Izquierdo-Verdiguier, E, Zurita-Milla, R, Rosà, R, Rizzoli, A & Papa, A 2018, Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series. in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 4670-4673, 38th IEEE International Geoscience and Remote Sensing Symposium 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8519542

Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series. / Andreo, V.; Izquierdo-Verdiguier, E.; Zurita-Milla, R.; Rosà, R.; Rizzoli, Annapaola; Papa, A.

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. p. 4670-4673.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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Andreo V, Izquierdo-Verdiguier E, Zurita-Milla R, Rosà R, Rizzoli A, Papa A. Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE. 2018. p. 4670-4673 https://doi.org/10.1109/IGARSS.2018.8519542