NATURAL SCIENCE:
This passage is adapted from the article "Fair-Weather Warning" by Julia Mittlebury (© 2007 by Julia Mittlebury).
Could the sun be causing epidemics? Take cholera, for example, an often fatal disease caused by the bacterium Vibrio cholerae (V cholerae). Every so often, coastal areas suffer massive outbreaks of cholera due to infected food or water. Where
Line 5 do these outbreaks come from? The bacterium that causes cholera is found in areas that contain the copepod, a certain type of crustacean. The copepod depends on zooplankton for nourishment, and these zooplankton in turn depend on phytoplankton for their nourishment.
10 Phytoplankton use photosynthesis to feed on Sunlight. Although one might need to go to the bottom of the food chain, the evidence shows that an increase in sunlight might mean an increase in the potential for cholera.
Interested in this correlation, Rita Calwell and her fellow
15 researchers at the University of Maryland are studying ways to use satellite measurements of Sea temperatures, sea height, and chlorophyll concentrations in order to predict when conditions favoring a cholera outbreak are more likely. As sea temperatures rise, photosynthetic organisms such as phytoplankton become
20 more abundant. As sea levels rise, the phytoplankton, zooplankton, copepods, and, by extension, the cholera bacterium are all brought closer to the shore. This increases the likelihood of food and Water contamination.
By monitoring the cholera food chainin reverse, Calwell and
25 her colleagues believe they can predict the emergence of cholera 4 to 6 weeks in advance. Calwell's model predicted the rate of infection during one recent cholera outbreak in Bangladesh with 95 percent accuracy. Unfortunately, because this field of study is so new and its insights are so speculative, local public health
30 officials have not yet begun to base any preventative measures on these satellite-based forecasts.
Just up the road from Calwell and the University of Maryland, Kenneth Linthicum is leading similar efforts at the NASA Goddard Space Flight Centre in Greenbelt, Maryland. He has
35 designed a model to analyze the spread of Rift Valley fever, a mosquito-spread virus that killed about 100,000 animals and 90,000 people back in December 1997.
Scientists observed that prior to the outbreak, the equatorial region of the Indian Ocean saw a half-degree increase in surface
40 temperature. Although half of a degree sounds like only a slight difference, the temperature of an ocean does not change easily. Warmer ocean water in this region corresponds with strong and prolonged rains, increased cloud cover, and warmer air over equatorial parts of Africa. These characteristics favor the pro
45 liferation of mosquitoes and help keep them alive long enough for the virus to become easily transmittable.
In September 2007, Linthicum and his team became alerted to similar environmental changes. Over the next few months, they Warned local health officials in Kenya, Somalia, and Tanzania that
50 conditions were ripe for a mosquito-based outbreak. As a result, only 300 lives were lost, an almost miraculous improvement from the devastation of the 1997 outbreak. While it is impossible to know if this outbreak would have been as far-reaching as that of 1997, it seems likely that the advance warning succeeded in
55 saving thousands, if not tens of thousands, of lives.
Similarly, a study by David Rogers at Oxford University has helped to predict outbreaks of sleeping sickness, a parasitic disease caused by West African tsetse flies. Here, Rogers first calibrated regional levels of photosynthesis to the size of a vein
60 in the wings of the flies. The vein size is a good measure of how numerous and robust the tsetse fly population is. Today, by reading the photosynthetic levels from satellite data, even researchers outside of West Africa can predict potential epidemics in the region.
65 This type of research is encouraging to many in the disease prevention field, because traditional methods involve slow, costly research. The newfound ability to cull massive amounts of meteorological data from satellites and to run that data through computer models has been much more efficient.
70 The goal of these models is to study the relationships between disease data and climate data. However, to do so requires decades, if not centuries, worth of high quality data to identify correlating factors with accuracy. Currently, the climatic data is much more reliable than the disease data. Nevertheless,
75 excitement about the potential usefulness of satellite-based predictions is persuading health agencies to compile and integrate their disease data more efficiently to give easier access to those trying to discover climate-disease links. It may still take a good deal of time and energy before this
80 technology is ready for practical application. Critics claim that the number of variables underlying the spread of disease are too numerous and varied for a climate-based approach ever to be reliable. Fluctuations in the immunity of local populations, human and animal migrations, and the resistance to drugs used
85 to commonly treat certain diseases could confuse climate-based models. Advocates respond, though, that these non-climatic factors can similarly be incorporated into their research as long as the relevant data is collected, and the resulting models will have even better accuracy.