Identifying Favorable Spatio-Temporal Conditions for West Nile Virus Outbreaks by Co-Clustering of Modis LST Indices Time Series

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.

[1]  G. Valiakos,et al.  Serological and molecular investigation into the role of wild birds in the epidemiology of West Nile virus in Greece , 2012, Virology Journal.

[2]  M. Athanasiou,et al.  Outbreak of West Nile Virus Infection in Greece, 2010 , 2011, Emerging infectious diseases.

[3]  Markus Metz,et al.  A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data , 2017, Remote. Sens..

[4]  Menno-Jan Kraak,et al.  A novel analysis of spring phenological patterns over Europe based on co‐clustering , 2016 .

[5]  M. Neteler,et al.  CAN RECONSTRUCTED LAND SURFACE TEMPERATURE DATA FROM SPACE PREDICT A WEST NILE VIRUS OUTBREAK , 2017 .

[6]  Emma Izquierdo-Verdiguier,et al.  Introducing co-clustering for hyperspectral image analysis , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  Menno-Jan Kraak,et al.  Triclustering Georeferenced Time Series for Analyzing Patterns of Intra-Annual Variability in Temperature , 2018 .

[8]  Mario Giacobini,et al.  Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy , 2014, Parasites & Vectors.

[9]  A. Tsakris,et al.  West Nile virus outbreak in humans, Greece, 2012: third consecutive year of local transmission. , 2014, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  T. Groen,et al.  Ecology of West Nile virus across four European countries: empirical modelling of the Culex pipiens abundance dynamics as a function of weather , 2017, Parasites & Vectors.