The use of remote sensing data for epidemic (socio-ecological) risk assessment in coastal regions (case study:cholera outbreak in Mariupol, 2011)

1Kostyuchenko, Yu.V, 2Yushchenko, MV, 3Kopachevsky, IM, 4Solovyov, DM, 3Bilous, Yu.H
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
2State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
3State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Science of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
4State institution «Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine», Kyiv; Marine Hydrophysical Institute Ukrainian National Academy of Sciences, Sevastopol
Kosm. nauka tehnol. 2013, 19 ;(1):57–67
Publication Language: Ukrainian

We consider the use of satellite observations and meteorological data for the analysis of epidemic and socio-ecological risks.We propose an approach to the assessment of the integrated risk which is based on the estimation of probabilities of the epidemic genesis and spread. Our procedure for the analysis of the risk of causative agent penetration to an ecosystem is based on the modified Pareto equation with taking into account of precipitation variations and the water absorption capability of local ecosystems.To increase the forecasting efficiency, a procedure for the calculation of most expected distributions of investigated values (instead of mean distributions) is proposed. Normalized Difference Vegetation Index and Normalized Difference Water Index (NDVI and NDWI) are used as the remote indicators. Some approaches to the analysis of development risk for the epidemic in the land and sea ecosystems are proposed. A problem oriented advanced method of spatial-temporal regularization of multispectral satellite observations (including spectral reflectance indexes analysis) is given. Corresponding values of scale factors, weight coefficients, and fitness functions are calculated for used satellite sensors and regional data. Using the algorithms proposed, the separate and integrated probabilities of the epidemic genesis and spread in the region under study are calculated for 2009—2012. Control parameter for comparing with disease statistics is proposed

Keywords: epidemic risks, Pareto equation, remote sensing data
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