1921
Volume 82, Issue 6
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract.

Satellite data may be used to map climatic conditions conducive to malaria outbreaks, assisting in the targeting of public health interventions to mitigate the worldwide increase in incidence of the mosquito-transmitted disease. This work analyzes correlation between malaria cases and vegetation health (VH) indices derived from satellite remote sensing for each week over a period of 14 years for Bandarban, Bangladesh. Correlation analysis showed that years with a high summer temperature condition index (TCI) tended to be those with high malaria incidence. Principal components regression was performed on patterns of weekly TCI during each of the two annual malaria seasons to construct a model as a function of the TCI. These models reduced the malaria estimation error variance by 57% if first-peak (June–July) TCI was used as the estimator and 74% if second-peak (August–September) was used, compared with an estimation of average number of malaria cases for each year.

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References

  1. Rahman A, Kogan F, Roytman L, , 2006. Analysis of malaria cases in Bangladesh with remote sensing data. Am J Trop Med Hyg 74: 1719. [Google Scholar]
  2. Nagpal B, Sharma V, , 1995. Indian Anophelines. New Delhi: Baba Barkha Nath Printers, 416423. [Google Scholar]
  3. Rosenberg R, Maheswary N, , 1982. Forest malaria in Bangladesh. I. Parasitology. Am J Trop Med Hyg 31: 175191. [Google Scholar]
  4. Russel F, West L, Manwell D, Macdonald G, , 1963. Practical Malariology. London, UK: Oxford University Press. [Google Scholar]
  5. Elias M, Rahman M, , 1987. The ecology of malaria carrying mosquito Anopheles philippinensis Ludlow and its relation to malaria in Bangladesh. Medical Research Council Bulletin, Bangladesh 13: 1528. [Google Scholar]
  6. Ingrid F, Van B, , 2004. Drug resistance in Plasmodium falciparum from the Chittagong Hill Tracts, Bangladesh. Trop Med Int Health 9: 680687.[Crossref] [Google Scholar]
  7. Paresul A, , 2008. Malaria country report. Malaria and Parasitic Disease Control Unit. Bangladesh: Directorate General of Health Services. [Google Scholar]
  8. Hay I, Rogers J, Randolph E, Stern I, Cox J, Shanks D, Snow W, , 2002. Hot topic or hot air? Climate change and malaria resurgence in east African highlands. Trends Parasitol 18: 530534.[Crossref] [Google Scholar]
  9. Faiz M, Yunus B, Rahman R, Hossain A, Pang W, Rahman E, Bhuiya N, , 2002. Failure of national guidelines to diagnose uncomplicated malaria in Bangladesh. Am J Trop Med Hyg 67: 396399. [Google Scholar]
  10. Mcmichael J, Haines A, Slooff R, , 1996. Climate Change and Human Health. Geneva, Switzerland: World Health Organization, 29. [Google Scholar]
  11. Githeko A, Lindsay S, Confalonieero U, Patz J, , 2000. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ 78: 11361147. [Google Scholar]
  12. Bouma M, , 2003. Methodological problems and amendments to demonstrate effects of temperature on the epidemiology of malaria. A new perspective on the highland epidemics in Madagascar, 1972–89. Trans R Soc Trop Med Hyg 97: 133139.[Crossref] [Google Scholar]
  13. Pampana E, , 1969. A Text Book of Malaria Eradication. London, UK: Oxford University Press, 1763. [Google Scholar]
  14. Wickramasinghe R, Gunawardena M, Mahawithanage T, , 2002. Use of routinely collected past surveillance data in identifying and mapping high risk areas in a malaria endemic area of Sri Lanka. SE Asian J Trop Med Publ Health 33: 678684. [Google Scholar]
  15. Brockwell P, Davis R, , 2000. Introduction to Time Series and Forecasting. New York: Springer, 1539. [Google Scholar]
  16. Salazar L, Kogan F, Roytman L, , 2008. Use of remote sensing data for estimation of winter wheat yield in the United States. Int J Remote Sens 29: 175189.[Crossref] [Google Scholar]
  17. Kidwel B, , 1997. Global Vegetation Index User's Guide. Camp Springs, MD: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Service, National Climatic Data Center, Satellite Data Services Division. [Google Scholar]
  18. Kogan F, , 1997. Global drought watches from space. Bull Am Meteorol Soc 78: 621636.[Crossref] [Google Scholar]
  19. Jensen R, , 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Prentice Hall. [Google Scholar]
  20. Kogan F, Bangjie Y, Guo W, Pei Z, Jiao X, , 2005. Modeling corn production in China using AVHRR-based vegetation health indices. Int J Remote Sens 26: 23252336.[Crossref] [Google Scholar]
  21. Chatterjee S, Hadi A, Price B, , 2000. Regression Analysis by Example. New York: Wiley. [Google Scholar]
  22. Draper N, Smith H, , 1981. Applied Regression Analysis. New York: Wiley. [Google Scholar]
  23. Myer H, , 1986. Classical and Modern Regression with Applications. Boston, MA: Duxbury Press. [Google Scholar]
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  • Received : 20 Apr 2009
  • Accepted : 27 Feb 2010

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