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--------------------------------------------------------- ARS ELECTRONICA FESTIVAL 99 LIFESCIENCE Linz, Austria, September 04 - 09 http://www.aec.at/lifescience --------------------------------------------------------- Satellites help predict disease outbreaks - report Updated 2:17 PM ET July 16, 1999 By Maggie Fox, Health and Science Correspondent WASHINGTON (Reuters) - Outbreaks of disease can be predicted months in advance using satellite images and other climate data, researchers said Friday. By analyzing information collected by a U.S. government weather satellite, a team of scientists studied the density of green vegetation in Africa to predict outbreaks of Rift Valley Fever, which can kill livestock and humans. Kenneth Linthicum of the Walter Reed Army Institute of Research and colleagues found the amount of green vegetation was a reliable indicator of rainfall, which in turn, predicted the rise and fall of mosquito populations. In Africa, rainfall encourages mosquitoes, which carry Rift Valley Fever, a hemorrhagic disease spread from livestock to humans by mosquitoes or by contact with infected animals. In 1998 the disease killed more than 600 people in Kenya. Examining the weather and climate data could be used to warn about a range of diseases and could improve efforts to warn of drought, flood and other disasters, the researchers said. "Several climate indices can be used to predict outbreaks up to 5 months in advance," they wrote in their report, published in Friday's issue of the journal Science. "For the first time we now have 18 or 19 years of data," Compton Tucker of the NASA Goddard Space Flight Center in Maryland, who worked on the study, said in a telephone interview. "We have satellite data and ground-based data and the actual documentation of the presence of the disease from traditional medical sources. So all these things have sort of come together." The team compared measurements of sea temperatures, including the El Nino-southern oscillation weather patterns -- which increase rain in some regions of East Africa and result in droughts in southern Africa -- to their satellite data and information about past outbreaks of Rift Valley Fever. The associations were clear and will be easy to predict in the future, the researchers said. Paul Epstein of Harvard Medical School, who wrote a commentary on the study, said the findings will greatly help efforts to predict outbreaks not only of Rift Valley Fever, but of other insect-borne diseases such as malaria and dengue fever. "You can distribute bed nets and pesticides and medicines and make a dramatic decrease in the amount of expected malaria," he said in a telephone interview. In South America it is drought that allows mosquitoes to breed. "In Colombia, El Nino is usually associated with drought, which in mountainous areas dries up streams and makes breeding sites for malaria and dengue fever," Epstein said. In the United States, he said, the data could be used to predict a rise in hantavirus, an untreatable and deadly virus carried by rodents whose victims' fluid-filled lungs often drown them. "In the southwest we had a tremendous drought in Arizona, Nevada, all through the spring. Right now there is flooding in the Grand Canyon and flooding in Las Vegas. This drought punctuated by rain is a perfect situation for an explosion in the mouse population. We should be getting our vigilance up for mice and hantavirus in the southwest," Epstein said. Satellite measurements could even be used to predict cholera outbreaks, Epstein said. Data from instruments that measure temperature and algae concentrations could show where conditions are right for cholera, which is often spread in polluted seawater. --------------------------------------------------------------------------- You are subscribed to the English language version of LIFESCIENCE To unsubscribe the English language version send mail to lifescience-en-request@aec.at (message text 'unsubscribe') Send contributions to lifescience@aec.at --------------------------------------------------------------------------
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