Warm Oceans Predict Rift Valley Epidemics

Ocean temperatures off Tahiti and Madagascar can predict outbreaks of disease in Kenya, according to a study in the current issue of Science (16 July, p. 397). The researchers found that a precise combination of local and global measurements can accurately predict outbreaks of Rift Valley fever months in advance--early enough to take precautionary measures.

Rift Valley fever, which affects both livestock and people, is caused by a mosquito-borne virus. Every few years, flooding of the normally dry areas of the Rift Valley--particularly in Kenya, Somalia, and parts of Tanzania--brings on an epidemic as the eggs of the virus-carrying mosquito hatch in large numbers. "Large herds of cattle, sheep, and goats can be wiped out, devastating the economy," says co-author Assaf Anyamba, a meteorologist at the NASA Goddard Space Flight Center in Greenbelt, Maryland.

In an attempt to predict Rift Valley fever epidemics, Anyamba and his colleagues compared outbreak data from the past 40 years to weather and water temperature records, some dating as far back as 1951. The periods of high rainfall that touch off Rift Valley fever epidemics coincided with El Niño, a global climate event signaled by warming of the Pacific Ocean. Only two out of every three El Niños resulted in an outbreak of Rift Valley fever, but the team found that all three outbreaks since 1982, when satellite data on ocean water temperature became available, have coincided with an El Niño in which unusually warm waters were found just off the East African coast, in the western Indian Ocean, as well as in the Pacific. Monitoring these signs will enable scientists to predict a November outbreak as early as June, they say.

Later, once the rainy season has begun, forecasters can locate the regions of highest risk with satellites that monitor the greening of the continent; the greenest areas--which have received the most rainfall--are at the highest risk. With such forewarning, local farmers can be armed with insecticides and vaccinations, possibly preventing an epidemic altogether, says Anyamba.

Paul Epstein, an epidemiologist at Harvard Medical School in Boston, calls the work "pivotal." "We've been looking at El Niño for a decade, trying to use it as an indicator [of disease outbreaks]. Now we have better ways of predicting [them] based on local as well as global conditions," he says.

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A 3D plot from a model of the Ebola risk faced at different West African regions over time.
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