Predicting Disease Outbreaks from Space
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- Visualizations by:
- Helen-Nicole Kostis
- View full credits
These visualizations were created for the May 18, 2012 Library of Congress Talk Predictiding Disease Outbreaks from Space. In this talk NASA scientist Assaf Anyamba, will present how using remote-sensing data we can see links among weather, diseases and famine.
An early warning system more than a decade in development successfully predicted the 2006-2007 outbreak of the deadly Rift Valley Fever (RVF) in East Africa and subsequent outbreaks in Sudan (2007) and South Africa (2008-2011). RVF is a deadly hemorrhagic disease transmitted by mosquitoes that infects livestock and human populations episodically. An international team of research scientists, public-health professionals, agricultural specialists and military personnel had worked for a decade to successfully predict when and where an outbreak of RVF would occur.
Credits
Please give credit for this item to:
NASA/Goddard Space Flight Center Scientific Visualization Studio
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Animators
- Helen-Nicole Kostis (USRA) [Lead]
- Ernie Wright (USRA)
- Greg Shirah (NASA/GSFC)
- Horace Mitchell (NASA/GSFC)
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Scientist
- Assaf Anyamba (USRA)
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Project support
- Jennifer Small (SSAI)
Datasets used in this visualization
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Aqua Sea Surface Temperature Anomaly
ID: 239 -
CPC ARC (Climate Prediction Center (CPC) Africa Rainfall Estimate Climatology)
ID: 709Input data used for operational rainfall estimates are from 4 sources: 1) Daily GTS rain gauge data for up to 1000 stations 2) AMSU microwave satellite precipitation estimates up to 4 times per day 3) SSM/I satellite rainfall estimates up to 4 times per day 4) GPI cloud-top IR temperature precipitation estimates on a half-hour basis.
This dataset can be found at: http://www.cpc.ncep.noaa.gov/products/fews/AFR_CLIM/afr_clim.shtml
See all pages that use this dataset
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.
Related Pages
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ID: 3905 Visualization
Mapping Diseases
Read moreThe print-resolution still images were created for the February 2012 issue of The Scientist (print and online). In an article in the same issue, NASA scientist Assaf Anyamba explains how he can predict diseases with remote-sensing data.The data used are: 1. NDVI is an index that quantifies the photosynthetic capacity of vegetation. It is derived from visible and near-infrared reflectance measurements made by Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA's polar orbiting satellites (in this case NOAA-17). Taken as time series measurements, NDVI indicates the response of vegetation to seasonal and interannual variations in climate.2. SST data are a blend of direct observations from ships, buoys, satellite imagery also from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments, and SSTs simulated by sea-ice cover. The monthly optimum interpolated fields were derived by a linear interpolation of the weekly fields to daily fields, and then averaging daily values over a month.All anomaly fields (as shown here) are derived by subtracting the monthly values from the respective long-term monthly means. ||
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