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Figure 4. Simulated and observed malaria prevalence
in a cluster of households
5. CONCLUSION
We have shown that remote sensing data can be used to model
the risks for malaria, dengue, and seasonal influenza. These
models can provide early warning and improve the response of
public health organizations to these diseases.
6. REFERENCES
References from Journals:
Alonso, W.J, Viboud, C., Simonsen, L., Hirano, E.W.,
Daufenbach, L.Z. & Miller, M.A. 2007. Seasonality of
influenza in Brazil: a traveling wave from the Amazon to the
subtropics. Amer. J Epidemiol. 165(12), pp. 1434-42.
Feighner, B.H., Pak, S.L, Novakoski, W.L. & Kelsey, L.L.
1998. Reemergence of plasmodium vivax malaria in the
Republic of Korea. Emer. Infect. Dis. 4(2), pp. 295-297.
Kiang, R., Adimi, F., Soika, V., Nigro, J., Singhasivanon, P.,
Sirichaisinthop, J., Leemingsawat, S., Apiwathnasorn, C. &
Looareesuwan, S. 2006. Meteorological, environmental remote
sensing and neural network analysis of the epidemiology of
malaria transmission in Thailand. Geospatial Health 1, pp.71-
84.
Kovats, R.S., Bouma, M.J., Hajat, S., Worrall, E. & Haines, A.
2003. El Niño and health. Lancet 362. pp.1481-89.
Kummerow, C., Barnes, W., Kozu, T., Shiue, J. & Simpson, J.
1998. The Tropical Rainfall Measuring Mission (TRMM)
sensor Package. J. Atmos. & Oceanic Tech. 15. pp.809-817.
Molinari, N.A., Ortega-Sanchez, LR., Messonnier, M.L.,
Thompson, W.W., Wortley, P.M., Weintraub, E. & Bridges,
C.B. 2007. The annual impact of seasonal influenza in the US:
Measuring disease burden and costs. Vaccine 25(27). pp.5086-
96.
Singh, B., Sung, L.K., Matusop, A., Radhakrishnan, A.,
Shamsul, S.S., Cox-Singh, J., Thomas, A. & Conway, DJ.
2004. A large focus of naturally acquired Plasmodium knowlesi
infections in human beings. Lancet 363(9414). pp.1017-1024.
Soebiyanto, R.P., Adimi, F. & Kiang, R.K. 2010. Modeling and
predicting seasonal influenza transmission in warm regions
using climatological parameters. PLoS ONE 5(3). e9450.
Tucker, C.J. 1979. Red and photographic infrared linear
combinations for monitoring vegetation. Rem Sens. Environ. 8.
pp.127-150.
Viboud, C., Alonso, W.J. & Simonsen, L. 2006. Influenza in
tropical regions. PLoS Med 3(4). e89.
Youssef, R., Safi, N., Hemeed, H., Sediqi, W., Naser, J.A. &
Butt, W. 2008. National malaria indicators assessment. Afghan.
Ann. Malaria J. 1(1). pp.37-49.
References from Books:
Smith, J., 1989. Space Data from Earth Sciences. Elsevier,
Amsterdam, pp. 321-332.
References from Other Literature:
WHO-Regional Office for the Eastern Mediterranean. 2007.
Strategic plan for malaria control and elimination in the WHO
Eastern Mediterranean Region 2006-2010. Cairo.
References from websites:
CDC, 2010. Key facts about seasonal influenza.
http://www .cdc.gov/influenza/keyfacts.htm
DigitalGlobe Inc., 2011. QuickBird and WorldView.
http://www.digitalglobe.com
GeoEye, 2011. Ikonos products and specifications.
http://www.geoeye.com
JAXA. 2011. PALSAR.
http://www.eorc.jaxa.jp/ALOS/en/about/palsar.htm
NASA, 2011. ICESat. http://icesat.gsfc.nasa.gov
Roll Back Malaria, 2011. http://www.rbm.who.int
USGS, 2009. Earth Observing 1. http://eol.usgs.gov
WHO, 2009. Influenza (Seasonal) Fact Sheet.
http://www.who.int/mediacentre/fact sheets/fs211/en/
7. ACKNOWLEDEMENTS
This work was supported by NASA Applied Sciences Public
Health Program and CDC Influenza Division.
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