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 Science 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
https://doi.org/10.15407/knit2013.01.057
Section: Study of the Earth from Space
Publication Language: Ukrainian
Abstract: 

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
References: 
1. Beck L. R., Lobitz B. M., Wood B. L. Remote sensing and human health: new sensors and new opportunities, Emerg. Infectious Diseases, 6 (3), 217—227 (2000).
https://doi.org/10.3201/eid0603.000301   
2. Beguería S. Uncertainties in partial duration series modelling of extremes related to the choice of the threshold value.  J. Hydrology, 303, 215—230 (2005).
https://doi.org/10.1016/j.jhydrol.2004.07.015
3. Bernardi M. Global climate change — a feasibility perspective of its effect on human health at a local scale. Geospatial Health, 2, 137—150 (2008).
https://doi.org/10.4081/gh.2008.238
4. Butler J. S., Schachter B. Estimating value at risk with a precision measure by combining kernel estimation with historical simulation,  Rev. Derivatives Res.  1 (4), 371—390 (1998).
5. Carol A., Leigh C. T. On the covariance matrices used in value at risk,  Models. J. Derivatives, 4, 50—62 (1997).
https://doi.org/10.3905/jod.1997.407974
6. Ceccato P., Ghebremeskel T., Jaiteh M., et al. Malaria stratification, climate, and epidemic early warning in Eritrea,  Amer. J. Trop. Med. and Hyg. , 77 (6), 61—68 (2007).
7. Cholera2010, WHO weekly epidemiological record, 86 (31), 325—340 (2011).
8. Choudhury B. J., Ahmed N. U., Idso S. B., et al. Relations between evaporation coefficients and vegetation indices studied by model simulations,  Remote Sens. Environ., 50, 1—17 (1994).
https://doi.org/10.1016/0034-4257(94)90090-6
9. McMichael A. J., Campbell-Lendrum D. H., Corvalan C. F., et al. (Eds) Climatechange and humane health: risks and responses, 322 p. (WHO, 2003).
10. Davidson A. C., Smith R.L. Models for exceedances over high thresholds,  J. Roy. Statist. Soc. B,  52, 393—442 (1990).
11. Ermoliev Yu., Makowski M., Marti K. Managing Safety of Heterogeneous Systems,  Lect. Notes Econ. and Math. Syst.,  658, 378 p. (2012)
https://doi.org/10.1007/978-3-642-22884-1
12. Ermoliev Yu., Wets R. J.-B. Nonlinear programming techniques in stochastic programming.  Numerical Techniques for Stochastic Optimization Problems / Eds Yu. Ermoliev, R. J.-B.Wets, 10, 95—122 (Series in Computational Mathematics) (Springer-Verlag, Berlin, Heidelberg, 1988).
13. Ermoliev Yu., Winterfeldt D. Risk, security and robust solutions,  IIASA Interim Report, IR-10-013, IIASA, 41 p. (2010)
14. Ermoliev Yu., Winterfeldt D. Systemic risk and security management,  Lect. Notes Econ. and Math. Syst., 658, 19—49 (2012)
https://doi.org/10.1007/978-3-642-22884-1
15. Gao B. C. Normalized difference water index for remote sensing of vegetation liquid water from space,  Proc. SPIE, 2480, 225—236 (1995).
https://doi.org/10.1117/12.210877 
16. García-Ruiz J. M., Arnáez J., White S. M., et al. Uncertainty assessment in the prediction of extreme rainfall events: An example from the central Spanish Pyrenees,  Hydrological Processes, 14, 887—898 (2000).
https://doi.org/10.1002/(SICI)1099-1085(20000415)14:5<887::AID-HYP976>3.0.CO;2-0
17. Gething P. M., Noor A. M., Gikandi P. W., et al. Improving imperfect data from health management information systems in Africa using space-time geostatistics, PLoS Med., 3 (6): e271, 825—831 (2006)
https://doi.org/10.1371/journal.pmed.0030271
18. Glass G. E., Cheek J. E., Patz J. A., et al. Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome,  Emerg. Infectious Diseases, 6 (3), 238—247 (2000).
https://doi.org/10.3201/eid0603.000303 
19. Kindhauser M.K. (Ed.) Global defense against the infected disease threat, 242 p. (Geneva: WHO, 2003).
20. Haccou P., Jagers P., Vatutin V. A. Branching processes: Variation, growth, and extinction of populations, 316 p. (Univ. Press & IIASA, Cambridge, Edinburg, UK, 2005).
https://doi.org/10.1017/CBO9780511629136
21. Herbreteau V., Demoraes F., Khaungaew W., et al. Use of geographic information system and remote sensing for assessing environment influence on leptospirosis incidence, Phrae province, Thailand.  Int. J. Geoinformatics, 2 (4), 43—50 (2006).
22. Hosking J. R. M. L-moments: analysis and estimation of distributions using linear combinations of order statistics,  J. Roy. Statist. Soc. B, 52, 105—124 (1990).
23. Jackson R. D., Slater P. N., Pinter P. J. Discrimination of growth and water stress in wheat by various vegetation in dices through clear and turbid atmospheres,  Remote Sens. Environ.  15, 187—208 (1983).
https://doi.org/10.1016/0034-4257(83)90039-1
24. King A. A., Ionides E. L., Luckhurst J., Bouma M. J. Inapparent infections and cholera dynamics,  Nature, 454 (7206), 877—880 (2008).
https://doi.org/10.1038/nature07084 
25. Kogan F. N. Application of vegetation index and bright ness temperature for drought detection,  Adv. Space Res. 15, 91—100 (1995).
https://doi.org/10.1016/0273-1177(95)00079-T
26. Magny G. C. de, Long W., Brown C. W., et al. Predicting the distribution of Vibrio spp. in the Chesapeake Bay: A Vibrio cholerae case study,  ECOHEALTH, 6(3), 378—389 (2009)
https://doi.org/10.1007/s10393-009-0273-6
27. Pickands J. Statistical inference using extreme order statistics,  Ann. Statist., 3, 119—131 (1975).
https://doi.org/10.1214/aos/1176343003
28. Sack D. A., Sack R. B., Nair G. B., Siddique A. K. Cholera,  Lancet.  363 (9404), 223—233 (2004).
https://doi.org/10.1016/S0140-6736(03)15328-7
29. Singh R. P., Roy S., Kogan F. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India,  Int. J. Remote Sens.,  24 (22), 4393—4402 (2003). 
https://doi.org/10.1080/0143116031000084323
30. Werdell P. J., Franz B. A., Bailey S. W., et al. Approach for the long-term spatial and temporal evaluation of ocean color satellite data products in a coastal environment,  Proc. SPIE, 6680, 12 p. (2007)