The Application of Remote Sensing and GIS Tools in the Study of Lyme Disease Risk Prediction Kathryn Berger

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1 The Application of Remote Sensing and GIS Tools in the Study of Lyme Disease Risk Prediction Kathryn Berger Lyme disease has become one of the most prevalent vector borne diseases in the United States and threatens to expand its territory if current warming trends continue. Many investigators propose that an increase in Lyme disease distribution may be caused by a shift in the habitat range of the blacklegged tick, Ixodes scapularis, as a response to climate change. Improved knowledge of the environmental factors related to the survival of I. scapularis is essential due to the unprecedented rate of increase in both regional distribution and abundance in North America over the past twenty years (Estrada-Peña 2002). Static maps of documented Lyme disease cases or tick presence may be helpful, but cannot be used to predict changes in disease risk over time. Nor do these maps provide additional information about the ecological processes responsible for changes in the vector s expanding regional distribution. Scientists have devoted a considerable effort to studying the ecologic and social factors influencing tick abundance and distribution. However, the integration of these results into a model that explains tick colonization of new regions and changes in local abundance is still work in progress (Estrada-Peña 2002). Ticks are not highly mobile organisms by nature and so depend on their hosts (e.g. the white-footed mouse, white-tailed deer) for movement over long distances. However, not all environments that a host might inhabit are suitable for tick survival and therefore the presence of an adequate host population by itself is not a risk factor for the disease transmission (Kalluri et al. 2007). Scientists have been working to associate satellite-derived environmental variables such as temperature, relative humidity, and land cover type to characterize vector habitats. In fact, a growing number of analyses within GIS have indicated that both small- and large-scale ranges of tick species can be illustrated more by climate and vegetation than by host-related factors (Randolph 2000) The last thirty years have shown an increasing adoption of remote sensing techniques and geographic information system (GIS) applications to the study of vector borne disease. Several investigations have used land cover maps derived from Landsat Thematic Mapper (TM) images to distinguish areas of suitable tick habitat as a method of Lyme disease risk prediction (Glass et al. 1995; Guerra et al. 2002; Mather et al. 1996). Land cover maps are a result of digitized satellite imagery with a hierarchical classification system according to land cover type (e.g. forest: deciduous or conifer). These maps are often used as an overlay image into a GIS map of points where entomologic sampling has demonstrated I. scapularis abundance and distribution. Characteristics of the landscape in high tick abundance areas are used to create predictive maps of Lyme disease risk based on habitat suitability. The investigation by Glass and colleagues (1995) overlaid GIS layers of environmental variable data (e.g. land use/land cover, forest type), derived from Landsat TM images, over epidemiological data of reported Lyme disease cases in Baltimore County from 1989 to Besides the well documented relationship between forested area residences and high tick abundance, results of this study also demonstrated significant associations between vector abundance and loamy soils (Glass et al. 1995). Land cover map/habitat suitability analysis has proven successful in increasing the knowledge of environmental variables associated with the tick abundance and distribution. This type of analysis will continue to aid in the development of predictive Lyme disease distribution maps, especially if trends continue and this vector s North American distribution continues to expand.

2 Satellite sensor derived landscape indices are used as an additional method for Lyme disease risk prediction. Landscape indices are spectral patterns that result from one (or a combination of) spectral band(s) derived from remotely sensed satellite imagery. Algorithms are developed from these spectral signatures to create additional ecological variables for vector detection. One such variable is the calculation of a Normalized Derived Vegetation Index (NDVI) which measures vegetation stress and is an indirect measurement of relative humidity (one of the most important ecological components of tick survival). NDVI uses the concept that healthy vegetation reflects very well in the near infrared spectrum, to measure the degree of vegetative stress in the environment. This ecological variable is one of the most used landscape indices because of its simplicity and sensitivity in detecting dynamic changes in the environment over time (Herbreteau et al. 2005). The NDVI index is the most consistently used variable for predicting tick distribution, because of its sound biological foundation in tick survival. The index calculates an indirect measurement of moisture availability that can be directly correlated to free-living ticks mortality rates (Randolph 2000). Estrada-Peña (2002) used NDVI and surface temperature datasets derived from the Natioanl Oceanic and Atmospheric Administration s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite to demonstrate favorable tick habitats are increasing within the United States. Available in raster form, these landscape indices can be used as additional overlays within a GIS to illustrate areas areas within an ecosystem that support tick survival and reproduction. Similarly, wetness and greenness indices created from Landsat TM images have been used to predict nymphal I. scapularis abundance with mixed results (Rodgers and Mather 2006). Developed from an image processing technique called tasseled cap transformation (TCT), the greenness index measures abundance of vegetation while the wetness index expresses the amount of moisture available in the soil and surrounding vegetation. A special case of principal component analysis, the x and y axes are transformed to corrrelate with the environment s physical vegetative characteristics. Much like the NDVI, these vegetation indices can be incorporated as an additional overlay in GIS to associate environmental variables with higher tick abundance and higher risk for Lyme disease. Lastly, continuous field data, such as land surface temperatures (LST), obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites have greatly enhanced the spatial data availability for predictive epidemiological studies (Neteler 2005). The use of satellite data into vector borne disease research greatly augments the spatiotemporal resolution of climatological data, particularly in regions were meterological stations or ground surveys are unavailable or difficult to perform. A raster data set, this type of data may require pre-processing before it can be incorporated into a GIS to illustrate meteorological trends in vector borne disease risk. Remote sensing and GIS applications provide very promising human health applications especially in the area of vector borne disease transmission. Advancement in satellite sensors, increased collaboration between remote sensing scientists and biologists, and easily accessible GIS statistics and image processing tools provide a rich environment for the advancement of this field (Kalluri et al. 2007). The combination of GIS tools and epidemiologic analysis allow scientists to study the spatial patterns of vector borne disease with greater accuracy over larger geographic areas than previously thought achievable. The application of remote sensing and GIS techniques to human health studies is not without its limitations. Users should be cautious when using remotely sensed environmental and/or meteorological data of varying scales of both vector and host habitats or Lyme disease

3 case records before attempting to make any interpretation about its relevance to human health. While researchers attempt to predict tick distributions with relatively simple statistical methods correlating environmental variables with tick presence, detailed descriptive information on the I. scapularis distribution based on field observations are needed create sufficient predictive maps (Randolph 2000). Remotely sensed imagery should be used as a complimentary tool in understanding the processes and underlying patterns that influence tick distribution. The ultimate goal will be to recognize the most useful combination of environmental variables as predictors of vector abundance and relate them to tick biology. This effort will help to enhance our understanding of the factors involved with I. scapularis distribution and improve Lyme disease risk prediction. Literature Cited Estrada-Pena, A Increasing habitat suitability in the United States for the tick that transmits Lyme disease: a remote sensing approach. Environmental Health Perspectives 110 (7): Glass, G. E., B. E. Schwartz, J. M. Morgan, D. T. Johnson, P. E. Noy, and E. Israel Environmental risk factors for Lyme disease identified with geographic information systems. American Jouranl of Public Health 85 (7): Guerra, M., E. Walker, C. Jones, S. Paskewitz, M.R. Cortinas, A. Stancil, L. Beck, M. Bobo, and U. Kitron Predicting the risk of Lyme disease: habitat suitability for Ixodes scapularis in the north central United States. Emerging Infectious Diseases 8 (3): Herbreteau, V., G. Salem, M. Souris, J-P. Hugot, and J-P. Gonzalez Sizing up human health through remote sensing: uses and misuses. Parassitologia 47: Kalluri, S., P. Gilruth, D. Rogers, and M. Szczur "Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review." PLoS Pathogens 3 (10): Mather, T. N., M. C. Nicholson, E. F. Donnelly, and B. T. Matyas Entomologic index for human risk of Lyme disease." American Journal of Epidemiology 144 (11): Neteler, M Time series processing of MODIS satellite data for landscape epidemiological applications." International Journal of Geoinformatics 1 (1): Randolph, S. E. "Ticks and tick-borne disease systems in space and from space." Remote Sensing and Geographical Information Systems in Epidemiology, 2000: Rodgers, S. E., and T. N. Mather Evaluating satellite sensor-derived indices for Lyme disease risk prediction." Journal of Medical Entomology 43 (2):

4 Annotated Bibliography Daniel, M., J. Kolar, and P. Zeman GIS tools for tick and tick-borne disease occurence. Parasitology 129: S329-S352. The authors present an extensive review paper on the application of geographic information systems (GIS) and remote sensing tools to study landscape epidemiology. Daniel et al (2004) describe how the study of tick borne disease is uniquely suited for GIS and remote sensing applications because of their close relationship with the environment around them. Additionally, the authors give a broad history of modeling, displaying and analyzing Lyme disease occurrence in a pre-gis era by introducing the theory of natural focality of diseases (NFD), first described by Pavlovsky in Defined as a natural focus of a disease, or a geographically demarcated part of the landscape which has formed during the course of natural evolution and contains an association of organisms in which there circulates a disease agent as its integral component, independently of man and domestic animals (Daniel et al. 2004). The authors further describe how this theory has evolved into the predictive mapping of tick borne diseases based on either (1) experimentally-verified dependence of tick species on certain environmental factors leading to empirical models (e.g. CLIMEX) or, (2) training databases reflecting the relationship between occurrence and covariates ad hoc. This latter model allows for the application of mathematical models (e.g. regression, discriminant analysis) permitting a statistical estimation of the relationship between tick occurrence and/or tick borne diseases and the number of covariates in a dataset. An extremely helpful review paper, the authors provide basis for introduction to the application of GIS and remote sensing tools to the study of Lyme disease risk surveillance. Estrada-Pena, A Increasing habitat suitability in the United States for the tick that transmits Lyme disease: a remote sensing approach. Environmental Health Perspectives 110 (7): The aim of the this study was to detect changes in habitat for I. scapularis Say in the United States between 1982 and 2000, using a modeling approach with remotely sensed climate and vegetation features to explain how the change of these variables may influence predicted tick habitat suitability over time. Estrada-Peña (2002) used Normal Derived Vegeation Index (NDVI) and surface temperature measurements from the Natioanl Oceanic and Atmospheric Administration s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite to demonstrate that favorable tick habitat is increasing within the United States. Satellite dervied NDVI measurements were used as an indirect calculation of relative humidity (one of the most important variables in combination with temperature to predict tick survival) and incorporated into the model for the entire period of the study. The model uses a point-to-point similarity metric to assign a classification value to unkown site based on the closeness in environmental space of the most similar record site. Results of this study, demonstrated the model s success in predicting areas of estabilished habitat suitability (e.g. northeastern Atlantic Coast) in every county where ticks had reproductive populations and produced no falsepositives. The results support the use of NDVI and land surface temperature variables in this model as important factors for determining the potentila distriubution of this vector. Additionally, Estrada-Peña s (2002) work showed an increasing trend in habitat suitability in the United States over the duration of this 20 year study. A helpful application of remote sensing to vector borne disease surveillence, the results of this paper can be regarded as an early warning of future tick colonization, if increasing trends in NDVI and temperature variables remain.

5 Glass, G. E., B. E. Schwartz, J. M. Morgan, D. T. Johnson, P. E. Noy, and E. Israel Environmental risk factors for Lyme disease identified with geographic information systems. American Jouranl of Public Health 85 (7): Glass and colleagues (1995) combine GIS with logistic regression analysis to generate an environmental risk model for Lyme disease. Using epidemiological and data obtained from Lyme disease case patients in Baltimore County from 1989 to 1990 and GIS layers of environmental variable data (landuse/land cover, forest type, etc.) derived from Landsat TM imagery, the authors results found 11 environmental variables associated with Lyme disease risk. In addition to the obvious connection between forested area residences and Lyme disease prevalence, significant associations were found with loamy soils and high vector abundance. This paper was a simple example of how the application of GIS tools can be used to generate risk models for Lyme disease, which may decrease the dependence on the labor intensive field sampling methods for disease risk studies in the future. In particular, this paper demonstrates how epidemiologic methods (used to identify factors associated with disease) can be used in conjunction with GIS tools (used to determine where these factors occur) to provide an approach to the study of disease, like Lyme, that are influenced by multiple environmental factors. Guerra, M., E. Walker, C. Jones, S. Paskewitz, M.R. Cortinas, A. Stancil, L. Beck, M. Bobo, and U. Kitron Predicting the risk of Lyme disease: habitat suitability for Ixodes scapularis in the north central United States. Emerging Infectious Diseases 8 (3): The goal of this paper was to examine the risk of Lyme disease based on habitat suitability for the tick Ixodes scapularis in Wisconsin, northern Illinois, and parts of the Upper Peninsula of Michigan. The authors gathered local level environmental data to create habitat profiles for the study area in a GIS database. Tick presence in this region was positively associated with decidous trees, dry forests and sandy or loam-sand soils overlaid on sedimentary rock, while tick absence was associated wth grasslands, conifer forests, especially wet forests, and acidic or clay soils. Additional analyses demonstrated that soil classification and land cover were the most dominant factors in tick presence and was used to construct a risk map of Lyme disease based on habitat suitability. Results illustrated the presence and abundance of I. scapularis was varied, even when host populations are sufficient for tick survival. The results of this study build upon earlier Lyme disease risk maps that merely mapped tick distribution, by displaying habitat suitability for potential future tick establishment. While this study correlates habitat suitability with tick presence or absence to determine current Lyme disease risk areas, it does not place importance on tick host (white-footed mice, whitetailed deer) habitat, and so does not give us a better picture of the interactive biological conditions related future I. scapularis establishment and future Lyme disease risk. Herbreteau, V., G. Salem, M. Souris, J-P. Hugot, and J-P. Gonzalez Sizing up human health through remote sensing: uses and misuses. Parassitologia 47: Herbreteau et al. (2005) raises some valid concerns about the use and misuse of remote sensing techniques to human health applications. Despite approximately 30 years work dedicated to this field of study, the authors state that current studies are limited by four predominant issues: (1) cost of satellite imagery, (2) availability of imagery, (3) choice of specific wavelengths, and (3) the time consuming and technical nature of pre-processing and postprocessing of the images. The authors performed an extensive survey of the available literature and found that despite the praise of remote sensing applications to epidemiological studies by

6 scientists, current investigations have often been extremely simplified because of the difficulty to assess the correlations between ecological and physical phenonmen (Herbreteau et al. 2005). The authors stress the need for dedicated field work to validate remote sensing observations because of the subjective nature of photo interpretation. This paper was very helpful in highlighting the limitations of remote sensing applications in the field of vector borne disease. Issues such as differences in scale between remotely sensed environmental and meteorological data and epidemiological records should also be considered in the interpretation of human health related studies. The authors provide fundamental concerns that should be reviewed prior to any application of remote sensing techniques to studies of vector borne disease. Kalluri, S., P. Gilruth, D. Rogers, and M. Szczur "Surveillance of arthropod vectorborne infectious diseases using remote sensing techniques: a review." PLoS Pathogens 3 (10): Kalluri and colleagues (2007) recognize the increase in adoption of remote sensing techniques by epidemiologists to study vector-borne infectious disease research and provide a very helpful review of current progress that has evolved in this field over the past 25 years. Of the studies focused on Lyme disease risk surveillance, Landsat Thematic Mapper (TM) derived land cover maps are often used to classify potential tick habitat. Ticks are highly dependent on their hosts (e.g., the white-footed mouse, white-tailed deer) for transportation; however, not all potential host habitats are suitable for tick survival. Therefore land cover maps are used in conjunction with maps of soil, geology, elevation and climate data to derive significant associations between environmental data and tick abundance. A study examining habitat suitability to predict Lyme disease risk in Wisconsin used discriminant analysis to determine significant environmental factors to separate positive tick sites. Similarly, the same study used logistic regression to create maps of habitat suitability for the tick, I. scapularis. Both studies resulted in more than 80% classification accuracy and demonstrated that soil order and land cover were the most dominant factors predicting tick presence. As suggested by the authors of this paper, the number of available techniques used to map vector distribution using satellite data may have increased significantly over the past 25 years, however, only those techniques used to aid our understanding of the biological processes will provide meaningful information in epidemiology and vector control. Kalluri et al. (2007) provide a very helpful resource for the study of vector borne disease control and provide some recommendations for the future of this field. Mather, T. N., M. C. Nicholson, E. F. Donnelly, and B. T. Matyas Entomologic index for human risk of Lyme disease." American Journal of Epidemiology 144 (11): The purpose of this paper was to develop an entomologic risk index (ERI) for human risk of Lyme disease based upon density estimates of nymphal deer ticks in forested regions of six Rhode Island towns. Validation of the model was performed by comparing the ERI for each town to the number of human Lyme disease cases reported to the Rhode Island State Health Department (RIDH) of the same year. To determine the degree of forest cover for each town, estimations were derived from 1:24,000 scale aerial photography and incorporated into ARC/INFO to create adequate areas for site selection. Selected sites of interest were sampled by dragging twice between late May and late June 1992 (see Mather et al. 1996) to estimate of tick density. An overall tick density and tick infection rate for a town were used in combination to determine its estimated ERI. Mather et al. (1996) observed a strong, positive correlation between ERI and reported Lyme disease cases (p = ) and supported by a strong coeffient of determination for the simple regression (r 2 = 0.978), suggesting the developed ERI

7 was a good predictor of Lyme disease risk. The results of this study, however, only support the hypothesis that peridomestic risk is a principally a function of tick abunadance in adjacent forests (which can narrow the sampling focus) but does not explain the how adult tick abundance affects Lyme disease risk. Ultimately, a more comprehensive understanding of habitat and influencial vaiables on tick abundance will be needed before remote sensing can serve as a reliable forecaster of Lyme disease risk. Neteler, M Time series processing of MODIS satellite data for landscape epidemiological applications." International Journal of Geoinformatics 1 (1): Continuous field data is an imperative component of epidemiological studies, as it enhances GIS by adding spatial density to initial sampling sites. Continous field data that cannot be easily collected from field sampling practices, can be gathered by measurements landscape indices calculated from satellite data. In this study Neteler (2005) uses daily Land Surface Temperature (LST) data acquired from the MODIS-Terra satellite against mean monthly temperatures in an effor to validate the usability of MODIS-Terra data in landscape epidemiological applications in the field of tick borne diseases. While it is important to note that LST temperatures are not identical to air temperatures measured at meteorological stations, earlier efforts have been made to derive air temperatures from LST data in an effort to resolve this issue. An important applicaton of MODIS-Terra LST data to the study of tick-borne disease distribution is the calcuation of autumnal cooling to describe the decline in temperature from August to October (in the northern hemisphere). Neteler (2005) explains that tick-borne encephalitis is characterized by a high rate of autumnal cooling, relative to the annual maximum of monthly mean LST in the midsummer, allowing for MODIS-Terra landscape indices to be applied to GIS to for improved epidemiological risk maps. This paper is an example of the processes needed to introduce MODIS landscape indices into a GIS for landscape epidemiological applications, especially in the field of tick borne diseases. Rodgers, S. E., and T. N. Mather Evaluating satellite sensor-derived indices for Lyme disease risk prediction." Journal of Medical Entomology 43 (2): In this paper, Rodgers and Mather (2006) test two remotely sensed indices (greenness and wetness) derived from Landsat TM data (June 1995 and 1997, and July 2002) to predict location of sites with different levels of the nymphal blacklegged tick, Ixodes scapularis Say, abundance in Rhode Island. Rodgers and Mather (2006) attempted to use this combination of landscape features, derived from remotely sensed images, to define habitat for ticks and their hosts, predict tick abundance, and ultimately determine the risk of contracting Lyme disease. The authors results were mixed, with the study demonstrating that greenness and wetness spectral indices could not differentiate between different categories of tick abundance for a large region of Rhode Island in 2002 (a year of high tick abundance), but could be used, to a limited degree, in both the 1995 and 1997, years of lower tick abundance. These results are consistent with an earlier study by Dister and colleagues (1997) that demonstrated a similar pattern of significance of Landsat TM derived spectral indices with high versus low risk areas, during years of low tick abundance. Lastly, Rodgers and Mather (2006) state that predicting tick abundance over large areas will be likely to require a more complex multivariate model. The authors suggest additional variables such as elevation to be examined in future models, and discuss factors, such as issues with data acquisition that may have confounded their analysis. While the authors results do not provide a significant link between these landscape indices and tick abundance in the state of Rhode Island, they do provide the initial steps required to connect remotely sensed indices with modeling Lyme disease risk over a large area.