Risk maps are outcomes of models of disease transmission based on spatial and temporal data. These models incorpo-rate, to varying degrees, epidemiological, entomological, climatic and environmen-tal information. New data sources such as remote sensing, and tools for computer-ized mapping [geographical information systems (GIS)] and for quantitative analy-sis of spatial data1 provide
unprece-dented amounts of information and a ca-pability to describe, explain, predict and communicate risk and the outcome of in-terventions. But computerized mapping produces maps that may appear more in-formative than the data upon which they are based; the lack of data from areas is readily masked, and area boundaries do not necessarily conform to biologically or epidemiologically significant measures.
Mapping efforts are conducted on geographic scales ranging from village to continental levels, and on temporal scales ranging from the duration of an outbreak to multi-year models. The choice of spatial, temporal and spectral resolutions determines the degree of precision, realism and general applicabil-ity of the models but, even with rapid ad-vances in computing power, these can-not be maximized simultaneously2. The
appearance and utility of risk maps de-pend on whether they are geared to test a hypothesis, identify gaps in our knowl-edge, provide a direction for surveillance and control efforts, or evaluate the actual or potential effectiveness of an interven-tion. Indeed, ‘the detailed analysis of a model for purposes other than that for which it was constructed may be as meaningless as studying a map under a microscope’2. A plethora of risk maps for
vector-borne diseases, particularly malaria and Lyme disease, have recently emerged in the scientific literature3–20.
Risk maps for malaria in Africa are epitomized by the MARA (Mapping Malaria Risk in Africa) initiative at the con-tinent level16,17 and by country-level
studies in Kenya and the Gambia10,19,20.
Exceptions to these macro-scale studies include a study by Ribeiro et al.14 of
the distribution of Anophelesmosquitoes in an Ethiopian village, and a micro-epidemiological study in Mozambique by Thompson et al.18. For malaria, not only is
the spatial and temporal association be-tween mosquito larvae and risk of severe
disease complex, but the density of adult female mosquitoes is not always corre-lated with parasite transmission intensity and occurrence of severe disease21. The
paucity of accurate epidemiological data and the high degree of heterogeneity in transmission patterns, even on a micro-scale, provide a challenge for risk-map-ping efforts. As data are never recorded at every location or point in time, values between sampling points are typically es-timated with the assumption of a mono-tonic change in parameters. This assump-tion is more likely to hold for surrogate environmental or meteorological data than for the less readily available epi-demiological and entomological data that tend to exhibit a more discontinuous pat-tern. Spatial autocorrelation of surrogate measures and interpolation for epidemi-ological parameters may obscure local differences, providing an incentive for uni-form control efforts such as insecticide-treated bednets over locally unique larval or other control strategies.
For Lyme disease in the USA, risk maps range in scale from the township and county levels to maps encompass-ing the whole country5–9,11. Some of
these maps are limited to tick or human case distributions, while others repre-sent an attempt to calculate actual risk of disease transmission based on a consid-eration of entomological, epidemiologi-cal and environmental determinants, using analytical techniques such as logis-tic regression9. While some mapping
ef-forts were closely tied to direct field ob-servations and studies by researchers6,9,
and appear to provide an accurate pic-ture of actual risk, others5,7,8 brought
together highly diverse data, with which the mappers may have had limited direct experience. Meta-analysis of multiple studies can be associated with a bias to-wards publication of positive results and multiple publications of the same data22.
For decisions such as recommendations for vaccine use, however, regional risk maps based on multiple studies are an integral part of a rational decision-making process.
Funding support for the application of GIS and satellite imagery, as well as demands from national and international health and other government agencies, provide further impetus for develop-ment of risk maps, but the result might
be map boundaries unrelated to physi-cal or biologiphysi-cal processes that affect the impact of disease. An important chal-lenge for researchers of vector-borne diseases is to make use of these new ad-vances to generate support for appro-priate field research, and to develop maps that are based on models of trans-mission dynamics. Questions such as: ‘how does spatial variation in disease transmission alter the likelihood and severity of epidemics23?’ and ‘how do
non-random spatial patterns affect the basic reproductive rate (R0) of a disease
and the threshold for endemicity or pe-riodicity of epidemics?’24can then be
ad-dressed. Landscape ecology and ecolog-ical risk assessment provide some means with which to address these issues.
Ecological risk assessment25,26 deals
with the underlying reasons for the exis-tence of a risk (as well as its magnitude and extent) and is based on the consid-eration of a hazard, defined as the source of a disturbance to an ecosystem25. The
spread of disturbance across a landscape is a function of the proportion of the landscape that is disturbance-prone, dis-turbance intensity and frequency, and thei spatial patterns (which may differ across a range of spatial scales) of environmen-tal and biotic determinants. The loss in degree of heterogeneity as we move from a fine to a coarse scale can help identify mechanisms responsible for de-partures from predictions and gaps in our knowledge. When disturbance is trans-lated to imply disease, pathogen or vec-tor, the guidelines provided by ecological risk assessment can be adapted to the risk mapping of vector-borne diseases and are particularly relevant to emerging dis-eases and invasions of exotic vectors or arthropod-borne pathogens.
Based on a landscape ecology ap-proach, a hierarchical framework for the study of heterogeneity can be estab-lished for consideration of multiple scales of patchiness and patch struc-ture27. This approach is particularly
use-ful when patch boundaries are difficult to identify, and when spatial statistics may be used to quantify patchiness at various scales. Under varying spatial (and temporal) scales, underlying causative risk factors and processes operate dif-ferentially28,29. Ecological risk assessment
distinguishes between local and regional
Comment
324 0169-4758/00/$ – see front matter © 2000 Elsevier Science Ltd. All rights reserved. PII: S0169-4758(00)01708-7 Parasitology Today, vol. 16, no. 8, 2000
Risk Maps:
Transmission and Burden of Vector-borne Diseases
risk assessment25 and recognizes that,
for larger geographic areas, models that account for uncertainty due to spatial heterogeneity on finer scales and in-creased consideration of social, economic and institutional processes are needed.
Multi-temporal satellite-derived envi-ronmental and meteorological data are available on the countrywide, regional and continental levels around which re-cent mapping efforts for Lyme disease and malaria have centered. Risk mapping on these geographic scales takes into ac-count variation associated with environ-mental factors. On a finer spatial scale, biotic factors are likely to play a major role23,27–29, and this fine-grain
hetero-geneity may result in high variability of transmission rates and infection pat-terns. Thus, while such efforts can esti-mate mean risk, finer-scale studies will highlight the variance around these esti-mates. A challenge remains to integrate fine-scale studies, such as the Matola micro-epidemiological malaria study18
or the county-level Lyme disease studies6,9with coarse-scale efforts such
as MARA16,17 and the USA Lyme disease
risk map8. Stronger links are needed
be-tween satellite data with a coarse spatial or temporal resolution, and field epi-demiological/entomological studies lim-ited in spatial extent or temporal range. A specific challenge is to relate coarse-scale predictions of climate models to processes at the scale of ecological information, especially as changes in disease patterns may be triggered by climate changes, but show no direct correlation with them28.
A model for integration of local and regional studies may be derived using tsetse, for which some environmental determinants of survival and distribution may be readily estimated, both on the macro- and micro-scale, from re-motely sensed surrogate measures15,12.
Analytical tools, such as spatial filter-ing12,20, discriminant analysis and
tem-poral Fourier-series representation of satellite data10,15have been successfully
applied to risk mapping of trypanoso-miasis and malaria. Variograms, correlo-grams and fractals allow for consider-ation of heterogeneity over a range of spatial scales30. These and other spatial
statistics and analytical tools have many applications for malaria and Lyme dis-ease, where the interactions between environmental determinants, transmis-sion patterns and disease risk may be more complex.
A comprehensive risk analysis is based on simultaneous efforts along a continuum of spatial and temporal scales, coupled with the realization that
extrapolation and interpolation across scales need to be conducted with cau-tion. Given the heterogeneity in com-plex biological systems, our objective is as much the accounting for differ-ences as the discovery of generalities. Variability in a system will be conditional on the scale of description28, and the
rel-evant scales have to be determined for specific disease systems. To achieve these objectives, risk maps have to be based on consideration of risk factors in-tegrated with satellite imagery across spatial and temporal scales. Using im-proved satellite data, advanced analytical tools and a landscape ecology approach, such risk maps can play a major role in refining research questions and surveil-lance needs, and in guiding control ef-forts and field studies.
Acknowledgments
I thank Louisa Beck, Greg Glass, Andy Long, John Malone Michael Stoto, Sarah Randolph, David Rogers, Randy Singer and Byron Wood for helpful comments. This work was supported, in part, by grant A136917, from the National Institutes of Health. Apologies to original authors that all work could not be cited due to space constraints.
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Uriel Kitronis at the College of Veterinary Medicine, University of Illinois, 2001
South Lincoln, Urbana, IL 61802, USA. Tel: +1
217-244-6221, Fax: +1 217 244-7421, e-mail: [email protected]
