NASA Study Predicted Outbreak of Deadly Virus

Responding to a deadly 1997 outbreak of the mosquito-borne disease Rift Valley fever, researchers had developed a “risk map,” pictured above, using NASA and National Oceanic and Atmospheric Administration measurements of sea surface temperatures, precipitation, and vegetation cover. As reported in a recent NASA-led study, the map gave public health officials in East Africa up to six weeks of warning for the 2006-2007 outbreak of the deadly Rift Valley fever in northeast Africa — enough time to lessen human impact.

On the map above, pink areas depict increased disease risk, while pale green areas reflect normal risk. Yellow dots represent reported Rift Valley fever cases in high-risk areas, while blue dots represent occurrences in non-risk areas. The researchers have detailed the map’s effectiveness in the Proceedings of the National Academy of Sciences.

During an intense El Niño event in 1997, the largest known outbreak of Rift Valley fever spread across the Horn of Africa. About 90,000 people were infected with the virus, which is carried by mosquitoes and transmitted to humans by mosquito bites or through contact with infected livestock. That outbreak prompted the formation of a working group — funded by the U.S. Department of Defense Global Emerging Infections Surveillance and Response System — to try to predict future outbreaks.

The working group didn’t start from scratch. The link between the mosquito life cycle and vegetation growth was first described in a 1987 Science paper by co-authors Kenneth Linthicum of the U.S. Department of Agriculture and Compton Tucker of NASA’s Goddard Space Flight Center. Later, a 1999 Science paper described a link between Rift Valley fever and the El Niño-Southern Oscillation, a cyclical, global phenomenon of sea surface temperature changes that can contribute to extreme climate events around the world.

Building on that research, Assaf Anyamba of NASA Goddard and the University of Maryland, and his colleagues, set out to predict when conditions were ripe for excessive rainfall — and thus an outbreak. They started by examining satellite measurements of sea surface temperatures. One of the first indicators that El Niño will boost rainfall is a rise in the surface temperature of the eastern equatorial Pacific Ocean and the western equatorial Indian Ocean. Perhaps the most telling clue is a measure of the mosquito habitat itself. The researchers used a satellite-derived vegetation data set that measures the landscape’s “greenness.” Greener regions have more than the average amount of vegetation, which means more water and more potential habitat for infected mosquitoes. The resulting risk map for Rift Valley fever, showing areas of anomalous rainfall and vegetation growth over a three-month period, is updated and issued monthly as a means to guide ground-based mosquito and virus surveillance.

As early as September 2006, the monthly advisory from Anyamba and colleagues indicated an elevated risk of Rift Valley fever activity in East Africa. By November, Kenya’s government had begun collaborating with non-governmental organizations to implement disease mitigation measures—restricting animal movement, distributing mosquito bed nets, informing the public, and enacting programs to control mosquitoes and vaccinate animals. Between two and six weeks later—depending on the location—the disease was detected in humans.

After the 2006-2007 outbreak, Anyamba and colleagues assessed the effectiveness of the warning maps. They compared locations that had been identified as “at risk” with the locations where Rift Valley fever was reported. Of the 1,088 cases reported in Kenya, Somalia, and Tanzania, 64 percent fell within areas delineated on the risk map. The other 36 percent of cases did not occur within “at risk” areas, but none were more than 30 miles away, leading the researchers believe that they had identified most of the initial infection sites.

The potential for mapping the risk of disease outbreaks is not limited to Africa. Previous research has shown that risk maps are possible whenever the abundance of a virus can be linked to extremes in climate conditions. Chikungunya in east Africa and Hantavirus and West Nile virus in the United States, for example, have been linked to conditions of rainfall extremes.

“We are coming up on almost 30 years of vegetation data from satellites, which provides us with a good basis for predicting,” said Linthicum, co-author on the 1987 paper, upon his return from a Rift Valley fever workshop in Cairo, Egypt last month. “At this meeting, it was clear that using this tool as a basis for predictions has become accepted as the norm.”

Sources: NASA and the Proceedings of the National Academy of Sciences