Global warming is likely reshaping the ecology of many medically important arthropod vectors. Warmer temperatures have been shown to directly increase mosquito and tick vector reproduction, biting, and pathogen transmission despite shortening mean daily survivorship.1 Related changes in rainfall, humidity, and the El Niño/Southern Oscillation (ENSO) may alter the quality and availability of some vector breeding sites.2 In the face of these changes, remote sensing (RS) and geographic information systems (GIS) have become powerful tools to study vector populations that transmit diseases such as Rift Valley fever, malaria, and Lyme disease.
Satellite RS, which involves acquisition of environmental data by orbital sensors, has tracked climate variables affecting important disease-propagating vectors. These instruments measure the intensity of ambient solar energy reflected or radiated from the Earth's surface or atmosphere, and yield quantitative indices of land surface temperature, vegetation, atmospheric moisture and rainfall that can be referenced to a precise location and time. While selected climatic data have been collected and recorded for more than a century,3 computer-based software tools collectively known as GIS have led to an unprecedented capability to consolidate environmental data. An increasing number of medical scientists are turning to these tools to study the effects of climate on global patterns of vector-borne disease.
One such disease is Rift Valley fever (RVF), a viral disease of humans and livestock in much of sub-Saharan Africa. Hemorrhagic fever syndrome from RVF is seen in about 1% of human cases with a case fatality rate of about 50%.4 Outbreaks of RVF in East Africa5 have been associated with unusually heavy rainfall thought to result from local ENSO effects. These events likely foster an ideal environment for mosquito eggs harboring the virus and increase the population of the major vector, Culex mosquitoes.6 One indicator of the relative availability of water, which is important for vector propagation, is the extent of actively photosynthetic vegetation, which can be spectrally differentiated from senescent vegetation. Remotely sensed anomalies in both vegetation density and sea surface temperatures could have predicted excessive rainfall and, in turn, each of the 3 RVF outbreaks in East Africa from 1982 to 1998.6
Malaria is a tropical vector-borne disease that kills more than 1 million people each year.2 In Chiapas, Mexico, Beck, et al used RS data to associate transitional wetlands and unmanaged pasture with a greater abundance of an important malaria vector—the adult Anopheles albimanus mosquito—in nearby villages.7 Models based on these findings were used to predict malaria risk for 40 randomly selected villages in a neighboring region, with an overall accuracy of 70%.8
Tick development and survival are highly sensitive to temperature and humidity, with decreased survival resulting during dry periods. As in the case of mosquito vectors, remotely sensed indicators of moisture availability have reliably predicted the distribution of ticks.9 Lyme disease, transmitted to humans through Ixodes ticks, is the most common vector-borne disease in the United States, where some 12 500 cases were reported from 1993 to 1997.2 Using remotely sensed land cover data, Dister et al showed that suburban residential properties in New York state with a high moisture and density of green vegetation had greater tick abundance and a higher theorized risk for exposure to Lyme disease.10
Environmental data gathered by RS and analyzed using GIS may provide a cost-efficient way to identify regions at high risk for exposure to vector-borne diseases. Because these technologies can monitor environmental variables across wide regions, they may be particularly useful in countries unable to carry out routine field surveys of vector populations. The ability to predict outbreaks months in advance based upon climate change indicators may make it possible to implement early vaccination initiatives or aggressive vector control programs and guide the relocation of human populations away from trouble spots. In short, RS and GIS will likely enhance our understanding of the relationship between climate and vector-borne disease and prepare health professionals for changes in the distribution of important infectious pathogens.
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