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1
Githeko A, Lindsay S, Confalonieri U, Patz J, 2000. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ 78 :200–207.
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2
Boëte C, Koella J, 2002. A theoretical approach to predicting the success of genetic manipulation of malaria mosquitoes in malaria control. Malaria J 1 :3.
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3
Elias M, Rahman M, 1987. The ecology of malaria carrying mosquito Anopheles and its relation to malaria in Bangladesh. Bangladesh Med Res Counc Bull 13 :15–28.
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4
Smith D, McKenzie E, 2004. Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria J 3 :13.
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5
Rosenberg R, Maheswary N, 1982. Forest malaria in Bangladesh, I. Parasitology. Am J Trop Med Hyg 31 :175–191.
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6
Nagpal B, Sharma V, 1995. Indian Anophelines. New Delhi: Baba Barkha Nath Printers, 416–423.
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7
Faiz MA, Yunus EB, Rahman MR, Hossain MA, Pang LW, Rahman ME, Bhuiya SN, 2001. Failure of national guidelines to diagnose uncomplicated malaria in Bangladesh. Am J Trop Med Hyg 67 :396–399.
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8
Kidwell KB, 1997. Global Vegetation Index User’s Guide. Technical Report, NOAA. Suitland, MD: U.S. Department of Commerce, 30–45
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9
Kogan F, 2001. Operational space technology for global vegetation assessment. Bull Am Meteorol Soc 82 :1949–1964.
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10
Rogers D, Hay S, Packer M, 1996. Predicting the distribution of tsetse flies in west Africa using temporal Fourier processed meteorological satellite data. Ann Trop Med Parasitol 90 :225–241.
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11
Hay S, Snow R, Rogers D, 1998. Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92 :12–20.
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12
Thomson M, Connor S, 2001. The development of malaria early warning systems for Africa. Trends Parasitol 17 :438–445.
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ANALYSIS OF MALARIA CASES IN BANGLADESH WITH REMOTE SENSING DATA
ATIQUR RAHMAN
ATIQUR RAHMAN
National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center, The City College of New York, New York, New York; National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service, Camp Spring, Maryland
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FELIX KOGAN
FELIX KOGAN
National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center, The City College of New York, New York, New York; National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service, Camp Spring, Maryland
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LEONID ROYTMAN
LEONID ROYTMAN
National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center, The City College of New York, New York, New York; National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service, Camp Spring, Maryland
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Epidemiologic data of malaria cases were correlated with satellite-based vegetation health (VH) indices to investigate if they can be used as proxy for monitoring malaria epidemics in Bangladesh. The VH indices were represented by the vegetation condition index (VCI) and the temperature condition index (TCI). The VCI and TCI estimate moisture and thermal conditions, respectively. Sensitivity of VCI and TCI was assessed using correlation and regression analysis. During cooler months (November–March) when mosquitoes are less active, the correlation was low. It increased considerably during the warm and wet season (April–October), reaching 0.7 for the TCI in early October and −0.66 for the VCI in mid September.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1
Githeko A, Lindsay S, Confalonieri U, Patz J, 2000. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ 78 :200–207.
-
2
Boëte C, Koella J, 2002. A theoretical approach to predicting the success of genetic manipulation of malaria mosquitoes in malaria control. Malaria J 1 :3.
-
3
Elias M, Rahman M, 1987. The ecology of malaria carrying mosquito Anopheles and its relation to malaria in Bangladesh. Bangladesh Med Res Counc Bull 13 :15–28.
-
4
Smith D, McKenzie E, 2004. Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria J 3 :13.
-
5
Rosenberg R, Maheswary N, 1982. Forest malaria in Bangladesh, I. Parasitology. Am J Trop Med Hyg 31 :175–191.
-
6
Nagpal B, Sharma V, 1995. Indian Anophelines. New Delhi: Baba Barkha Nath Printers, 416–423.
-
7
Faiz MA, Yunus EB, Rahman MR, Hossain MA, Pang LW, Rahman ME, Bhuiya SN, 2001. Failure of national guidelines to diagnose uncomplicated malaria in Bangladesh. Am J Trop Med Hyg 67 :396–399.
-
8
Kidwell KB, 1997. Global Vegetation Index User’s Guide. Technical Report, NOAA. Suitland, MD: U.S. Department of Commerce, 30–45
-
9
Kogan F, 2001. Operational space technology for global vegetation assessment. Bull Am Meteorol Soc 82 :1949–1964.
-
10
Rogers D, Hay S, Packer M, 1996. Predicting the distribution of tsetse flies in west Africa using temporal Fourier processed meteorological satellite data. Ann Trop Med Parasitol 90 :225–241.
-
11
Hay S, Snow R, Rogers D, 1998. Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92 :12–20.
-
12
Thomson M, Connor S, 2001. The development of malaria early warning systems for Africa. Trends Parasitol 17 :438–445.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 17 | 17 | 4 |
| Full Text Views | 278 | 83 | 0 |
| PDF Downloads | 88 | 18 | 0 |