IF you can predict where puddles are likely to form, you鈥檙e halfway to
preventing an outbreak of malaria. At least that鈥檚 the theory behind the latest
attempt to control epidemics of the killer disease.
The system works by tracking local water cycles to work out where the
mosquitoes that carry malarial parasites are most likely to breed. 鈥淚f you have
an indication of the availability of breeding sites,鈥� says David Grass of NASA鈥檚
Goddard Space Flight Center, near Washington DC, who led the study, 鈥渢hat鈥檚
going to bring you one step closer to predicting how many malaria cases you
should expect鈥�.
But in remote areas, where malaria is most prevalent, detailed information
about relevant factors such as rainfall and soil moisture is hard to come by,
and satellite information only tells part of the story.
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So the team turned to a new system developed at Goddard that models the water
cycle in a given area by combining satellite data, ground-based measurements,
and computer models. They took information from the model and compared it with
five years of data on malaria cases in the Mpumalanga Province of South Africa
to pinpoint the most effective indicators of impending malaria outbreaks.
From their results they created a malaria prediction algorithm that
predicted, one month in advance each time, 11 of 13 malaria outbreaks over a
span of three years.
Grass said the algorithm is not intended to stand alone. Instead, it could be
just one of several linked components in future early warning systems to tell
people when to apply pesticides or distribute antimalarial drugs.
