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

doi:10.1109/IGARSS.2018.8519542

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 proceeding › Chapter › Academic › peer-review

8 Citations (Scopus)

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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 language	English
Title of host publication	IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE
Pages	4670-4673
Number of pages	4
ISBN (Electronic)	978-1-5386-7150-4
DOIs	https://doi.org/10.1109/IGARSS.2018.8519542
Publication status	Published - 5 Nov 2018
Event	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/

Conference

Conference	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title	2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18
Internet address	https://www.igarss2018.org/

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10.1109/IGARSS.2018.8519542

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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 (pp. 4670-4673). IEEE. https://doi.org/10.1109/IGARSS.2018.8519542

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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.",

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Identifying favorable spatio-temporal conditions for West Nile virus outbreaks by co-clustering of modis LST idences time series

AU - Andreo, V.

AU - Izquierdo-Verdiguier, E.

AU - Zurita-Milla, R.

AU - Rosà, R.

AU - Rizzoli, Annapaola

AU - Papa, A.

N1 - Conference code: 38

PY - 2018/11/5

Y1 - 2018/11/5

N2 - 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.

AB - 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.

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BT - IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

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

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