Using Satellite and Ground-based Data to Develop Malaria Risk Maps

  • Released Monday, July 24, 2017
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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 color bar for reported malaria cases (shown as cylinders) from 1 to 100, going from light red to dark red.

The color bar for reported malaria cases (shown as cylinders) from 1 to 100, going from light red to dark red.

The color bar for precipitation rate from 0.0 to 0.0042 kilogram in one square meter per second, going from light blue to dark blue.

The color bar for precipitation rate from 0.0 to 0.0042 kilogram in one square meter per second, going from light blue to dark blue.

The color bar for total soil moisture from 2.9 to 1000 kg per square meter, going from light green to dark green.

The color bar for total soil moisture from 2.9 to 1000 kg per square meter, going from light green to dark green.

The color bar for special humidity from 0.0 to 0.023 kilogram per kilogram, going from light blue to purple to dark purple.

The color bar for special humidity from 0.0 to 0.023 kilogram per kilogram, going from light blue to purple to dark purple.

The color bar for air temperature from 264.93 to 309.25 Kelvin, going from light blue to light brown to dark brown.

The color bar for air temperature from 264.93 to 309.25 Kelvin, going from light blue to light brown to dark brown.

These are the frames of the background/land cover as the camera zooms in.

These are the frames of the background/land cover as the camera zooms in.

The layer of reported malaria cases shown as cylinders.

The layer of reported malaria cases shown as cylinders.

These are the frames of the matching mask for Peru as the camera zooms in.

These are the frames of the matching mask for Peru as the camera zooms in.

These are the  frames of date in week and year format. This format is original in the health-post level dataset.

These are the frames of date in week and year format. This format is original in the health-post level dataset.



Credits

Please give credit for this item to:
NASA's Scientific Visualization Studio

Release date

This page was originally published on Monday, July 24, 2017.
This page was last updated on Wednesday, November 15, 2023 at 12:11 AM EST.


Datasets used in this visualization

Note: 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.