Using Satellite and Ground-based Data to Develop Malaria Risk Maps
- Visualizations by:
- Cheng Zhang
- Scientific consulting by:
- Ben Zaitchik and
- William Pan
- Produced by:
- Joy Ng
- View full credits
Malaria is a major problem in the Amazon where malaria mosquitoes tend to prefer wet, hot areas with more standing water. Seasonal occupational movement along rivers and in forested areas increases transmission and concentrates malaria in specific regions.
The objective of Malaria Project, an ongoing study led by William Pan and Ben Zaitchik, is to develop a detection and early warning system for malaria risk in the Amazon. Using data from NASA satellites and a Land Data Assimilation System (LDAS), the scientists hope that their research can help health officials pinpoint where to deploy resources and what resources to deploy during a disease outbreak.
By incorporating NASA data such as precipitation, soil moisture, air temperature, and humidity into their new system, scientists are better able to predict where malaria-spreading mosquitoes are breeding. These climate factors in conjunction with a population density and human movement model will help scientists better understand where and when people are at high risk for malaria. The malaria warning system will predict outbreaks and simulate response to help a country's health care system to more strategically determine where to deploy their resources.
Visualizations focus on Peru, one of the central areas of malaria transmission in the Amazon. Four LDAS data sets -- precipitation, soil moisture, air temperature, and humidity are illustrated below. Combined with public health data, the animations show how these factors may affect the outbreak and evolvement of the disease.
The objective of Malaria Project, an ongoing study led by William Pan and Ben Zaitchik, is to develop a detection and early warning system for malaria risk in the Amazon. Using data from NASA satellites and a Land Data Assimilation System (LDAS), the scientists hope that their research can help health officials pinpoint where to deploy resources and what resources to deploy during a disease outbreak.
By incorporating NASA data such as precipitation, soil moisture, air temperature, and humidity into their new system, scientists are better able to predict where malaria-spreading mosquitoes are breeding. These climate factors in conjunction with a population density and human movement model will help scientists better understand where and when people are at high risk for malaria. The malaria warning system will predict outbreaks and simulate response to help a country's health care system to more strategically determine where to deploy their resources.
Visualizations focus on Peru, one of the central areas of malaria transmission in the Amazon. Four LDAS data sets -- precipitation, soil moisture, air temperature, and humidity are illustrated below. Combined with public health data, the animations show how these factors may affect the outbreak and evolvement of the disease.
Credits
Please give credit for this item to:
NASA's Scientific Visualization Studio
Visualizers
- Cheng Zhang (USRA) [Lead]
- Greg Shirah (NASA/GSFC)
- Horace Mitchell (NASA/GSFC)
Writer
- Samson K. Reiny (Wyle Information Systems)
Scientists
- Ben Zaitchik (Johns Hopkins University) [Lead]
- William Pan (None) [Lead]
Producers
- Joy Ng (KBRwyle) [Lead]
- Ryan Fitzgibbons (KBRwyle)
Technical support
- Ian Jones (ADNET)
- Laurence Schuler (ADNET)
Datasets used in this visualization
health-post level dataset
Observed Data
|
Duke University
Terra and Aqua BMNG (A.K.A. Blue Marble: Next Generation) (Collected with the MODIS sensor)
Credit: The Blue Marble data is courtesy of Reto Stockli (NASA/GSFC).
Dataset can be found at: http://earthobservatory.nasa.gov/Newsroom/BlueMarble/
See more visualizations using this data setNote: While we identify the data sets used in these visualizations, we do not store any further details nor the data sets themselves on our site.
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