1 Laboratoire dEpidémiologie, Institut Pasteur de la Guyane, Cayenne, French Guiana.
2 Laboratoire Régional de Télédétection, Institut de Recherche pour le Développement Guyane, Cayenne, French Guiana.
Received for publication September 5, 2001; accepted for publication May 24, 2002.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
disease outbreaks; incidence; maps; population density; Q fever; remote sensing
Abbreviations: Abbreviations: HRV, high resolution visible; HRVIR, high resolution visible and infrared; IRIS, Ilots Regroupés pour lInformation Statistique; SPOT, Satellite Pour lObservation de la Terre.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
During an outbreak investigation, incidence rate estimation requires a census of the risk population. In many cases, notably in the field of infectious diseases, that amounts to estimation of the number of people who live in the target area. However, population census data are often expensive and difficult to obtain quickly. In some cases, particularly in developing countries, such data are obsolete or nonexistent.
Because it provides information on land cover, which is often linked to population densities, remote sensing could offer potential for epidemiologists as a new, rapid, and relatively cheap tool with which to compute and map incidence. In the health domain, various remote sensing data at different resolutions have been used to study the temporal and spatial distributions of disease or arthropod vectors. In most studies, parameters are derived from the images and their relation with epidemiologic or entomologic field data is exploited to map disease risk or vector density (24). They are mostly natural environmental parameters, such as type of vegetation (5, 6), vegetation index (7, 8), sea (9, 10) and land (11, 12) surface temperatures, or amount of water (1315). Nevertheless, remote sensing could also be used to calculate social, urban, or demographic parameters relevant to epidemiologic studies.
The use of remote sensing for demographic studies allows urban growth monitoring, in qualitative and quantitative terms, at low cost and with regular updating. It is based on the relation between population and urban morphology (1618). For quantitative surveys, homogeneous areas are identified by interpretation of remotely sensed images and then linked with population densities (1922). Nevertheless, the use of satellite data for precise demographic surveys encounters serious difficulties linked with the complexity of urban morphology (2325).
The purpose of the present study was to evaluate the efficiency of remote sensing data for the characterization of high-incidence areas. We used data from a recently described investigation of a Q fever outbreak in Cayenne, French Guiana, and its suburbs (26). In the target area, the incidence rate was computed alternatively with census data used as the denominator and with the estimation of population density obtained using multispectral data from the Satellite Pour lObservation de la Terre (SPOT).
The study was conducted in French Guiana, where Q fever, a zoonosis caused by the bacterium Coxiella burnetii, has been producing an epidemic since 1996. People become infected mainly by inhaling aerosols generated during parturition of contaminated animals. In French Guiana, which is located in the Amazonian forest complex between Brazil and Suriname, the incidence of Q fever has increased significantly since 1996 (26, 27). However, the behavior of this original epidemic significantly differs from the usual case. On the one hand, this epidemic occurred in the main urban area of the country, whereas Q fever is mostly considered a rural disease in the literature. On the other hand, the reservoir responsible for transmission has not been identified, but many facts strengthen the hypothesis of there being a wild reservoir, whereas it is usually constituted by domestic ungulates.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
The HRV-XS sensor onboard SPOT satellites measures the intensity of solar radiation reflected by objects on the Earth in three wavelengths: green, red, and near infrared. The high resolution visible and infrared (HRVIR) sensor from SPOT-4 provides an additional measurement in the middle infrared range. The pixel (or picture element) size, corresponding to the smallest area for which the sensor can record data, is 20 m x 20 m.
Because of the existing link between the characteristics of an object and its spectral properties, spectral information given by multispectral sensors allows researchers to differentiate between objects having different spectral responses, and in some cases to characterize or identify them. The process used to discriminate and map different types of land cover, called image classification, is based on the spectral properties of the landscape: Pixels with similar spectral responses are merged into the same class. In order to generate a land cover map of Cayenne, we used a supervised classification, which requires field knowledge to define the different classes. Unsupervised classification is a more automatic process but is less meaningful and therefore less adapted to our study.
The 11 landscape elements identified were dense urban areas, suburban areas, mangrove areas, dense secondary forest, sparse secondary forest, swamp, sand, bare soil, roads, free water, and a miscellaneous nonlandscape class (clouds, cloud shadows). The classification was performed on the four channels of the 1999 SPOT image using a Bayesian process called maximum likelihood classification (Imagine software; ERDAS, Atlanta, Georgia) (28, 29). The 1991 SPOT image was processed in the same way on areas corresponding to the nonlandscape class (13.7 percent of the 1999 image). Although results of the two classifications are not rigorously comparable (different spectral inputs, different dates), we considered it better to use a second image rather than suffer from a total lack of data. We did not compute radiometric and atmospheric corrections, which are not required for a supervised classification process.
Calculation of a population density index
Because the classification allows discrimination between urban areas (including dense urban areas, suburban areas, bare soil, and roads) and natural areas (mangrove, dense and sparse secondary forest, swamp, sand, and free water), we merged the different landscape elements into these two classes. Then, assuming that population density is related to the presence of urban elements like buildings, roads, houses, etc., we empirically computed a population density index for each pixel (corresponding to a 20- x 20-m area). This index is defined as the number of neighboring pixels belonging to the urban class, within 200 m of the central pixel boundary (figure 2, part a). Using a binary image, where urban pixels take the value 1 and all others take the value 0, this can be done by convolving the binary image with a 20- x 20-pixel circular low pass filter (Imagine software).
|
|
Population census data
We used 1999 population census data from the French National Institute of Statistics and Economic Studies (Institut National de la Statistique et des Etudes Economiques) to estimate the validity of our population estimation. Cayenne and its suburbs are divided into 35 districts called statistical block groups (Ilots Regroupés pour lInformation Statistique (IRIS)), with a mean surface area of 6.2 km2 (32). For each one, the average population density is known (figure 2, part b).
We computed the incidence value for each IRIS district by dividing the number of cases in the district by the number of inhabitants. We obtained an accurate incidence map with a low spatial resolution corresponding to the IRIS district size. To compare maps with the same spatial resolution, we interpolated the incidence values taken for each Q fever case to resample the disease incidence map with a 200-m pixel (figure 4).
|
We visually compared the maps in terms of information content (qualitative validation). We then computed the average population density index for each IRIS district and compared it with the population density value generated by the census (quantitative validation).
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The disease incidence map derived from the population census data (figure 4) highlighted several areas with high Q fever incidence rates (Camp du Tigre, Rorota, Bourda, Prison, Rochambeau, La Chaumière, and Cogneau-Larivot), all located on the outskirts of Cayenne. Visual control showed that the incidence map obtained using satellite imagery (figure 3) was very similar to the map obtained using population census data (figure 4). The same incidence spots were observed, though not with the same intensity.
Two examples illustrate two main sources of difference. The first example concerns additional spots: Two additional peaks were observed in the population census incidence map near the Camp du Tigre peak (figure 4). The second example concerns intensity differences: Some spots, such as the Cogneau-Larivot area, appear with a much lower intensity in the incidence index map than in the real incidence map. These differences are due to the low spatial resolution of the census data (example 1) and to the reduced accuracy of the population density index calculation (example 2).
In spite of these differences, our incidence index map is relevant for epidemiologic study as long as absolute incidence values are not needed. Indeed, epidemiologic surveys using this map would be concentrated on the same strong incidence areas as surveys using the real incidence map obtained with population census data.
Quantitative evaluation
We used logarithmic transformation in order to reduce the saturation of our population density index for high population densities. It is then correlated with the real density given by the population census with a high correlation coefficient (r = 0.91; p < 105). These results demonstrate the efficiency of our method for obtaining a rough estimate of population density (figure 5).
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The comparison between our incidence index map and the incidence map obtained using population census data confirms the efficiency of remote sensing techniques in identifying areas of high disease incidence, in the same way that classical population census demographic data should do. In that way, estimation of the population distribution with satellite imagery can be used to compensate for a lack of population census data; moreover, it presents many advantages: It is rapid, relatively cheap, and computationally easy, and it provides a visual product simplifying analysis and interpretation of the results.
The higher spatial resolution of satellite data limits errors in the detection of incidence spots. For example, the two additional spots detected using the population census data (example 1) are clearly artifacts due to the too-important size of the IRIS districts. Indeed, in the district that included these two spots, all points are supposed to have the same incidence value, which is equal, in that case, to the high incidence value of Camp du Tigre.
Limitations of our method must be pointed out, however. On the one hand, it does not provide an accurate value for disease incidence, only an index linked to this value. Consequently, it is suitable for epidemiologic surveys that need a qualitative analysis of the incidence distribution rather than a quantification of the extent of the epidemic. Indeed, computation of population density using satellite data encounters the same difficulties as those involved in demographic applications of remote sensing: The complexity of urban morphology and of the link existing between population and land use, depending on sociocultural parameters, limits an accurate estimation of population density using satellite data with moderate spatial resolution. On the other hand, the population density index presents several inaccuracies linked with its definition. We have shown that it becomes saturated for dense urban areas. Indeed, the index cannot increase as soon as the 200-m neighborhood is full of buildings, while the population density can still increase.
Moreover, the index calculation is the same for areas with low and high population densities, although the relation between population and building density is very different. Schematically, in areas of low population density, one house corresponds to one family, whereas in high-density areas like town centers, one roof corresponds to one building housing several families. Depending on the district, our index should be adjusted for a more accurate population estimation.
In the same way, the difference between residential and nonresidential buildings is not taken into account in the index calculation. Example 2 shows that although the district of Cogneau-Larivot is detected in the two maps as a strong incidence spot, the spot intensity is much lower in the incidence index map than in the map derived from the population census. It can be explained by the presence of industrial plants in that district: Our population density index indicates a high population density because the number of buildings is important. This contributes to an increase in the population density estimation and therefore to a reduction in the incidence rate.
Such limitations could be overcome using additional information such as selective field surveys, previous population census data, etc. This information would improve investigators knowledge of the existing relation between population and urban landscape characteristics and therefore the index calculation. Moreover, since the use of a SPOT image permits discrimination between different urban classes (dense urban, suburban), index accuracy could also be improved by using more than one urban class in the calculation.
Perspectives
Remote sensing could provide additional information on habitat. Different types of districts could be distinguished (city centers, residential districts, spontaneous settlements, buildings, individual houses with gardens), and habitat could be described in both environmental and social terms. All of this information is relevant for epidemiologic surveys. Indeed, more so than a population density index, remote sensing could provide epidemiologists with a descriptive analysis of the population affected by the disease for further studies.
In the epidemiologic survey of Q fever conducted in French Guiana, many results led to the hypothesis that the reservoir of the bacterium was a wild animal (26). In such conditions, the priority was to identify trapping areas for reservoir identification and habitat characterization. This was allowed by our study.
Application of the method to other sites and other diseases
We implemented a population density index in the particular case of a survey on Q fever in French Guiana (26); it was adapted for a region-scale survey at a study site with a particular urban morphology. In another context, particularities of the disease and the regionsuch as study scale, required accuracy, environmental conditions, location of cases, and local sociocultural practiceswould have to be taken into consideration. Most of the parameters used in our study, like the sensor characteristics, the number of images, and the size of the surface used in calculation of the population density index, were determined on the basis of knowledge from field and disease epidemiology. Further study is needed to determine how they should be adapted for other sites and diseases.
Conclusion
Our results demonstrate that remote sensing can be used as a new tool for rapid mapping of disease incidence in an epidemiologic survey. We were able to identify the areas with high incidence rates, and we validated our approach using population census data.
Locally, this incidence map will aid in further research on the risk factors for Q fever and the reservoir of the bacterium responsible for Q fever in Cayenne. More generally, the method described in this paper could be applied to other diseases in other areas, provided that demographic data could be estimated through a land cover study.
![]() |
ACKNOWLEDGMENTS |
---|
The authors thank M. Guillemet from the Institut National de la Statistique et des Etudes Economiques for discussion and data and the Programme National dEnvironnement Côtier for the 1999 SPOT image.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|