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10 Health Applications of Remote Sensing and Climate Modeling Paul R. Epstein Remote satellite sensing of the oceans, land masses, ice cover, and the atmo- sphere has been used for understanding biogeochemical cycles and their relation- ships to biotic activity. An important insight emerging from research on climate and ecosystems is that climatic changes and variations alter the ecology of dis- ease organisms that attack human beings and their food supplies. Remotely sensed data being used to monitor climatic phenomena and improve understanding and forecasting of climate are proving useful for forecasting the spread of disease and offer great potential for the development of health early warning systems. Re- mote sensing can aid in the monitoring of disease carriers (vectors), breeding sites, and animal reservoirs in both marine and terrestrial ecosystems. Integrated into geographic information systems, it can advance disease surveillance, as well as aid in the development of timely, environmentally sound public health interventions. This chapter examines five applications of remote sensing for disease sur- veillance: (1) monitoring coastal algal blooms and toxic phytoplankton to sup- port early warning systems for paralytic shellfish poisoning and cholera; (2) monitoring terrestrial habitats to identify and control mosquito and rodent disease vectors; (3) building models of climate variability that can be used to predict conditions conducive to disease outbreaks; (4) using climate-change models to project potential disease distribution; and (5) detecting tropospheric temperatures to help understand physical and biological changes, particularly the spread of disease, at high altitudes. 197
198 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING MONITORING COASTAL ALGAL BLOOMS Remote sensing data from the Coastal Zone Color Scanner (CZCS) and the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS), as well as measurements of sea-surface temperatures using the Advanced Very High Resolution Radiometer (AVHRR), have been used to assess phytoplankton blooms (Feldman et al., 1984; Brock et al., 1991; Brock and McClain, 1992) and primary productivity (Aiken et al., 1992~. In one application, the AVHRR instrument, which senses red and infrared wavelengths, provides measures of sea-surface temperatures, which are correlated with the appearance of algal blooms. In Woods Hole, Massachusetts, AVHRR images are used in real time to detect and follow spring plumes from Massachusetts rivers flowing to the coast. Based on these images, boats are sent out for targeted sampling to detect blooms of Alexandrium tamarense, a toxic phytoplankton responsible for paralytic shellfish poisoning. When A. tamarense is found, shellfish beds in the area are closed to harvesting, thus preventing outbreaks of the disease (D. Anderson, Woods Hole Oceano- graphic Institution, personal communication, 1993~. Similar remote sensing technology can provide early warning of cholera outbreaks. Since the 1960s, researchers in Bangladesh have associated outbreaks of cholera with seasonal coastal algal blooms (Cockburn and Cassanos, 1960~. Recently, Colwell and associates have used fluorescent antibody probes to iden- tify a viable, nonculturable "dormant" form of Vibrio cholerae, which attaches to a wide range of marine life (Colwell et al., 1985) and reemerges to an infectious state along with algal blooms. Algal blooms are grazed by zooplankton that, over days to weeks, are capable of concentrating V. cholerae bacteria to infectious doses in their egg sacs. Zooplankton may be consumed directly in drinking water (as in Bangladesh), or be passed up the marine food chain (through shellfish filter feeders or finfish) and thus be introduced into human populations indirectly. Consequently, early detection of phytoplankton blooms and targeted sam- pling for cholera bacteria can constitute a cholera early warning system. Early detection in the marine environment can allow for timely institution of public health measures that include temporary bans on shellfish and finfish consump- tion, use of new oral recombinant immunizations, increased chlorination of wa- ter, and preparation of medical treatment facilities. Early treatment can reduce the case fatality rate from 50 to 1 percent. We are currently in the Seventh World Pandemic of cholera (E1 Tor strain). This pandemic began in the Bay of Bengal in the 1960s, arrived in Africa in the 1970s, and infected Latin America in the 1990s. In 1992, a new strain of cholera emerged, called V. cholerae 0139 Bengal. There is no cross-immunity between the older and newly emerged forms (i.e., previous infection with E1 Tor does not protect against 0139 Bengal). While currently confined to the Bay of Bengal, this new organism could find its way in bilge or ballast water, or with a traveler,
PAUL R. EPSTEIN 199 to other parts of the world. Thus a cholera early warning system could help anticipate and reduce the impact of an Eighth World Pandemic of cholera. MONITORING TERRESTRIAL HABITATS OF DISEASE VECTORS Remote sensing has been used to delineate the habitats of vectors bearing diseases such as African sleeping sickness (Epstein, Rogers, and Slooff, 1993; Rogers and Packer, 1993) and malaria (Dister et al., 1993) so that controls can be instituted. The potential of the approach can be illustrated with the example of Eastern Equine Encephalitis (FEE), a disease transferred to humans by the bite of Aedes Texans mosquitoes. EKE most often affects children, and even in small outbreaks the consequences are grave and are terrifying for communities af- fected. Up to half of those infected may die, and half of the survivors are left with permanent neurological complications. Knowing where temporary pools of standing water are, when they will ap- pear, and perhaps how long they will last is necessary so that environmentally appropriate actions can be taken to control populations of EEE-infected mosqui- toes. Early use of Bti (Bacillus thuringiensis var. israelensis), a nontoxic, inex- pensive larvicide, is the alternative to widespread spraying of the adulticide malathion. Maturation of larvae to adults occurs in about 7 days, so accurate information on standing pools of water within 2 days after a rain will allow time for dip sampling to test for the presence of vectors in pools and subsequent application of Bti (Epstein, Rogers and Slooff, 1993~. The best approach for mapping standing water dependably involves the ac- quisition of remotely sensed images. These and other data layers can then be used together for analysis in an appropriate geographic information system. Real- time information following summer rains can be obtained from oblique-angled synthetic aperture radar (SAR), which can penetrate vegetative and cloud cover (Imhoff and McCandless, 1988; Imhoff and Gesch, 1990) to distinguish smooth water surfaces, thus helping to focus dip sampling and the application of larvi- cidal treatment in a timely fashion. Imagery from Landsat, with 30-m spatial resolution and coverage every 16 days (at best), or from Systeme pour ['Observation de la Terre (SPOT), which has the advantage of relatively more on-demand coverage and 20- and 10-m spatial resolution, will be helpful in developing a series of baseline maps identifying areas at risk for infection. SAR may be most appropriate for providing real-time accurate estimates of the locations of standing water. Aircraft-collected SAR data could be acquired and processed at an appropriate scale and processed for use in a timely fashion.
200 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING MODELING CLIMATE VARIABILITY AND DISEASE OUTBREAKS During the warm phase of the E1 Nino/Southern Oscillation (ENSO), spe- cific areas of the globe are consistently affected by drought, whereas others experience excessive precipitation. While Southeast Brazil has rain, for example, Northeast Brazil has intensified drought. The climatic effects of ENS O are stronger (more consistent) in some areas; Southern Africa repeatedly experiences drought during an E1 Nino. All tropical oceans warm in relation to the ENSO pattern, and evaporation from the Atlantic can cause floods in a warmer Central Europe. Tracking ENS O events in relation to epidemics is a key to identifying the impacts of climate variability and weather on disease patterns. Associations in themselves are not proof of causation, but a preponderance of globally distributed evidence and a plausible mechanism (extremes of precipitation and temperature) lend credence to a strong role for climate in disease distribution. E1 Nino warm events are associated with upsurges of cholera in Bangladesh (R.B. Sack, The Johns Hopkins University School of Public Health, personal communication); typhoid, shigellosis, and hepatitis after flooding in South America (Cabello,1991~; viral encephalitides (Murray Valley and epidemic poly- arthritis from Ross River virus) in Australia (Nicholls, 1993~; and Eastern Equine Encephalitis in Massachusetts (Edman et al., 1993~. Other ENSO-related out- breaks of disease include malaria worldwide (Bouma et al., 1994a), in Pakistan (Bouma et al., 1994b), and in Venezuela (Bouma and Dye, 1997~; malaria and dengue ("breakbone") fever upsurges in Costa Rica (Kassutto and Epstein, un- published data); dengue fever in Northeast Brazil (unpublished data); epidemic malaria in the Indian subcontinent, 1874-1945 (M.J. Bouma, London School of Hygiene and Tropical Medicine, personal communication); and agricultural ro- dent infestations in Zimbabwe (1973-1983, 1994) (Epstein and Chikwenhere, 1994~. There is also a direct relationship between monsoons, which are biennially related to ENSO, and the spread of the brown plant hopper (Nilaparvata lugans) rice pest in Southeast Asia (Walker, 1994~. Interannual climate variability such as ENSO may be related to upsurges of soil-borne organisms as well. From the 428 reported cases of San Joaquim Valley fever (due to the fungus Coccidioidomycosis immitis) in the 1980s, 1200 cases occurred in 1991, and over 4000 in the E1 Nino years of 1992 and 1993 (Centers for Disease Control and Prevention, 1994~. An earthquake and a pro- longed drought followed by torrential rains are considered to have been contribu- tory factors. Additionally, disease events across taxa appear to cluster during ENSO warm-phase years. Disease events along the U.S. Atlantic coast during 1987, an ENS O warm-phase year heralding the warmest year of this century to that time, included extensive Caribbean coral bleaching; a large Florida sea grass die-off;
PAUL R. EPSTEIN 201 transfer of the agent of neurological shellfish poisoning (Gymnodinium breve) from the Gulf of Mexico to North Carolina; a large die-off of sea mammals in New England and the North Sea; the emergence of amnesic shellfish poisoning in Prince Edward Island (caused by a newly discovered diatom toxin, domoic acid, and later appearing worldwide) (Epstein, Ford, and Colwell, 1993~; and an out- break of spruce budworms in the Northeast Canadian balsam forests. ENSO warm events are also correlated with new appearances of harmful algal blooms in Asia (Hallaegraeff, 1993) and along the U.S. Atlantic coast (M. Altalo, Scripps Institution of Oceanography, personal communication). This evidence of relationships between ENS O and disease outbreaks sug- gests that predictions of ENS O can be used in health early warning systems. Dynamic atmospheric-oceanic coupled general circulation models that depend in part on remote sensing of sea-surface temperatures provide predictions of ENSO and of its climatic effects. These models and their predictions are based on analysis of geographic patterns of climate since 1877 (Kaladis and Diaz, 1989; Glantz et al., 1991) and are improving in their ability to make regional predic- tions of climatic events. As the models increase in predictive skill, they will become increasingly useful for predicting climatic conditions conducive to dis- ease outbreaks. USING CLIMATE CHANGE MODELS TO PROJECT DISEASE DISTRIBUTION Animals and plants have clear thresholds for viability, as well as temperature and humidity ranges in which they mature, replicate, and thrive (Gill, 1920a, b; Burgos, 1990; Burgos et al., 1994; Dobson and Carper, 1993~. Shifts in tempera- ture isotherms in latitude and altitude with climate change could thus have pro- found impacts on ecotones (the geographical dividing lines between ecosystems), on biota, and in particular on the distribution of pests and pathogens. Several models using remote sensing, geographic information systems, and general circulation climate models have been used to project for particular areas of the world how conditions conducive to vector-borne diseases may change with global warming scenarios. The affected diseases include malaria, schistosomia- sis (Martens et al., 1994; Matsuoka et al., 1994), and dengue fever (rocks et al., 1995~. Plate 10-1 (facing page 183) shows the output of one such model for malaria. UNDERSTANDING THE SPREAD OF DISEASE AT HIGH ALTITUDES Recent reports indicate that malaria and dengue fever are appearing at higher altitudes than at any time during this century. In addition, plants have been observed to be moving to higher altitudes on 30 Alpine peaks, in the U.S. Sierra
202 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING Nevada, in Alaska, and in New Zealand. Moreover, summit glaciers are retreat- ing on many continents, and ice caps show evidence of accelerated warming during this century (Thompson et al., 1995~. An initial examination of data from the Microwave Sounding Unit (MSU) and infrared sensors (Susskind et al., 1997) suggests that in E1 Nino years, warm- ing in the upper atmosphere may exceed warming occurring on the earth's sur- face. There are several possible contributing factors to explain these observa- tions. One is the increased relative heat absorption of carbon in the upper troposphere, because it is cooler at higher altitudes. Second, increased tropo- spheric water evaporation due to deep oceanic warming (Southwest Pacific, At- lantic, and Indian Oceans) (Parilla et al., 1994; Thwaites, 1994), exaggerated during E1 Nino years (Graham, 1995), may augment greenhouse warming and increase high, heat-trapping clouds. Third, sulfur-enriched lower clouds may also increase with increased atmospheric water vapor, and may reflect and absorb solar energy and cool the earth's surface with rain. Such analyses can help in developing causal models that link predictable seasonal-to-interannual climate changes, such as ENS O and global warming pro- cesses, to their effects on the ecology of disease organisms. This sort of under- standing will be valuable to public health officials in affected areas, such as major high-altitude tropical cities, in forecasting the potential for epidemics and taking appropriate action. SUMMARY AND CONCLUSION The costs of not understanding present climate instability and likely changes in climate due to human activities may be enormous. Disease outbreaks cause disability and mortality, and the impacts can ripple through societies and econo- mies. In 1995, the dengue outbreak cost Central American nations $7.5 million in control efforts alone; Peruvian fisheries lost $750 million in seafood exports during the 1991 cholera epidemic; and international airline and hotel industries lost an estimated $2 billion from the Indian plague in 1994. The global resur- gence of malaria, dengue fever, and cholera and the emergence of relatively new diseases such as Ebola can impact development, trade and tourism, agri- culture, and livestock. Remote sensing alone or integrated into geographical information systems and general circulation models has multiple applications for understanding bio- logical processes, and in particular, disease phenomena. Health early warning systems that can identify climate conditions conducive to outbreaks and disease clusters are becoming feasible enabling early, environmentally sound public health interventions. For vector-borne diseases, such interventions include, among others, immunizations, where appropriate; source reduction, such as neigh- borhood cleanups and the clearing of breeding sites; selective applications of pesticides or the nontoxic Bti; and community distribution of bednets.
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