Climate and Satellite Indicators to Forecast Rift Valley Fever Epidemics in Kenya

American Association for the Advancement of Science (AAAS) - Tập 285 Số 5426 - Trang 397-400 - 1999
Kenneth J. Linthicum1, Assaf Anyamba2, Compton J. Tucker2, Patrick W. Kelley1, Monica F. Myers2, C. J. Peters3
1Department of Defense, Global Emerging Infections System, Division of Preventive Medicine, Walter Reed Army Institute of Research, Washington, DC 20307–5100, USA.
2Earth Sciences Directorate, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
3Special Pathogens Branch, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA

Tóm tắt

All known Rift Valley fever virus outbreaks in East Africa from 1950 to May 1998, and probably earlier, followed periods of abnormally high rainfall. Analysis of this record and Pacific and Indian Ocean sea surface temperature anomalies, coupled with satellite normalized difference vegetation index data, shows that prediction of Rift Valley fever outbreaks may be made up to 5 months in advance of outbreaks in East Africa. Concurrent near–real-time monitoring with satellite normalized difference vegetation data may identify actual affected areas.

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Tài liệu tham khảo

Daubney R., Hudson J. R., Garnham P. C., J. Pathol. Bacteriol. 34, 545 (1931).

Davies F. G., Linthicum K. J., James A. D., Bull. W.H.O. 63, 941 (1985).

Linthicum K. J., Davies F. G., Kairo A., Bailey C. L., J. Hyg. 95, 197 (1985).

Linthicum K. J., Bailey C. L., Davies F. G., Tucker C. J., Science 235, 1656 (1987).

Pope K. O., et al., Remote Sens. Environ. 40, 185 (1992);

Linthicum K. J., et al., Sist. Terra 3, 44 (1994).

Cane M. A., Annu. Rev. Earth Planet. Sci. 14, 43 (1986).

M. H. Glantz in Teleconnections Linking World Wide Climate Anomalies: Scientific Basis and Societal Impact M. H. Glantz R. W. Katz N. Nicholls Eds. (Cambridge Univ. Press New York 1991) pp. 1-12

N. Nicholls in Teleconnections Linking World Wide Climate Anomalies: Scientific Basis and Societal Impact M. H. Glantz R. W. Katz N. Nicholls Eds. (Cambridge Univ. Press New York 1991) pp. 493-510.

Ropelewski C., Halpert M., Mon. Weather Rev. 115, 2352 (1987).

C. Chagas and G. Puppi in Persistent Meteo-Oceanographic Anomalies and Teleconnections C. Chagas and G. Puppi Eds. (Pontificia Academia Scientiaruum Citte Del Vaticano 1986) pp. 1–15.

E. M. Rasmusson in Teleconnections Linking World Wide Climate Anomalies: Scientific Basis and Societal Impact M. H. Glantz R. W. Katz N. Nicholls Eds. (Cambridge Univ. Press New York 1991) pp. 309–343.

Anyamba A., Eastman J. R., Int. J. Remote Sens. 17, 2533 (1996);

Farmer G., J. Climatol. 8, 489 (1988).

Myneni R. B., Los S. O., Tucker C. J., Geophys. Res. Lett. 23, 729 (1996).

NDVI data were processed after C. J. Tucker W. W. Newcomb and H. E. Dregne [ Int. J. Remote Sens. 15 3547 (1994)]. SOI and SST data are produced and archived by the National Oceanographic and Atmospheric Administration/Climate Prediction Center (). A previous paper (4) proposed the use of a satellite-derived potential virus activity factor. We now use monthly NDVI data normalized to represent departures from the 1982–95 mean to better characterize rainfall anomalies associated with RVF activity.

Logan T. M., Linthicum K. J., Davies F. G., Binepal Y. S., Roberts C. R., J. Med. Entomol. 28, 293 (1991);

Logan T. M., Davies F. G., Linthicum K. J., Ksiazek T. G., Trans. R. Soc. Trop. Med. Hyg. 86, 202 (1992).

Cane M. A., Eshel G., Buckland R. W., Nature 370, 204 (1994).

Nicholson S. E., Int. J. Climatol. 17, 345 (1997);

; ___ and J. Kim ibid. p. 117.

There is a direct relation between rainfall and green vegetation growth between green vegetation growth and the NDVI and hence between rainfall and the NDVI. This relation applies to areas receiving precipitation of <800 mm/year. W. K. Lauenroth in Perspectives in Grassland Ecology N. French Ed. (Springer-Verlag New York 1979) pp. 3–24;

Le Houerou H. N., Hoste C. H., J. Range Manage. 30, 181 (1977);

Malo A. R., Nicholson S. E., J. Arid Environ. 19, 1 (1990);

Nicholson S. E., Davenport M. L., Malo A. R., Clim. Change 17, 209 (1990);

; C. J. Tucker and S. E. Nicholson Ambio in press.

Holben B. N., Kaufman Y. J., Kendall J. D., Int. J. Remote Sens. 11, 1511 (1990);

Vermote E. F. Kaufman Y. J. 16 2317 (1995);

; S. O. Los ibid. 14 1907 (1994);

Che N., Price J. C., Remote Sens. Environ. 41, 19 (1992).

Correlation coefficients were determined for a data series calculated by the differences between adjacent values with SPSS Trends 6.1 software (SPSS Chicago 1994). Nairobi NDVI anomalies were derived from average monthly composite data within 8 by 8 grid cells each with a spatial resolution of 8 km centered close to Nairobi Kenya. Monthly AVHRR data were derived from global area coverage data that are produced by the on-board processing of large area coverage data (1.1 km by 1.1 km) and subsequently transmitted to receiving stations in Virginia or Alaska. Composite data were formed by selecting the highest NDVI for each grid cell location from daily data for that month to minimize cloud and atmospheric contamination. NDVI data were calculated and mapped to a Hammer-Aitof projection. The highest value during a monthly period was selected to represent the monthly composite for each grid cell location.

AutoRegressive Integrated Moving Average (ARIMA) analysis determined by SPSS Trends 6.1 software.

Logan T. M., et al., J. Am. Mosq. Control Assoc. 6, 736 (1990).

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American Association for the Advancement of Science (AAAS)

Tập 285 Số 5426

397-400

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