The Use of GIS in Wildlife Disease Surveillance

Jennalee Bauman

GEO 565, Geographic Information Systems & Science

Annotated Articles

Bany, S.A. & Freier, J.E. (2000). Use of GIS to evaluate livestock-wildlife interactions relative to tuberculosis spread on Molokai Island, Hawaii. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Centers for Epidemology and Animal Health, Fort Collins, CO.

Researchers Stephanie Bany and Jerome Freier from the Center for Epidemiology and Animal Health in Fort Collins, Colorado, attempt to identify the cause of the re-occurrence of bovine tuberculosis on Molokai Island in Hawaii using a variety of spatial analysis techniques with ArcView. The spatial database was built using hunter surveys of species which have had a history of TB infection including deer, feral pigs and mongoose. The location of these species along with attribute data on histopathology and culture results were imported onto ArcView. They then used a kernal home range technique in ArcView to determine the home range of these species on the island and to analyze where they intersect with the presence of livestock. Their results showed the presence of the M. bovis infection in two wild swine located near current farms and historical disease outbreak sites. They conclude that it is likely there will be further hunting of these species in the southern parts of the island to prevent disease outbreaks and protect the interests of the Hawaiian cattle industry. This article is a good example of the practical use of GIS in identifying potential disease reservoirs in wildlife populations and mitigating threats to livestock.

Beasley, J. C., Beatty, W. S., Atwood, T. C., Johnson, S. R. and Rhodes, O. E. (2012), A comparison of methods for estimating raccoon abundance: Implications for disease vaccination programs. The Journal of Wildlife Management, 76:1290–1297.

This paper discusses methods for estimating raccoon abundance and the importance of an accurate estimate in determining where to administer rabies vaccinations to best prevent the spread of wildlife diseases. They compared a raccoon abundance index (RAI) with capture-mark-recapture (CMR) methods to determine how accurate current index based methods are at determining raccoon density and abundance. They found that using the RAI approach significantly underestimated raccoon populations compared to CMR data. On average only 30% of raccoons sampled had been administered the rabies vaccination, supporting their conclusion that the RAI method was not accurately estimating raccoon densities and that because of this bait densities were administered too low. ArcGIS was used to help determine the CMR estimate by overlaying and merging trapping grid buffers to determine the overall density for each site. They also used ArcGIS with their radio-telemetry data to identify the home ranges and concentrated activity of raccoons in the research area. This paper demonstrates how GIS can be used to determine if wildlife disease management tactics are successful or if they need to be improved.

Beckett, S., & Garner, M. G. (2007). Simulating disease spread within a geographic information system environment. Veterinaria Italiana,43(3), 595-604.

Authors Samuel Beckett and M. G. Garner discuss new developments in disease simulation modeling used by the Australian Government Department of Agriculture, Fisheries and Forestry (DAFF). The paper covers the many spatial data layers used in a MapBasic GIS to model disease transmission pathways. They conclude that while some studies will require some of the model’s components to be enhanced, they have a solid framework for the development of regionally specific disease models. Using a GIS for these models makes it easier to develop strategies for the surveillance and control of diseases from wildlife populations. This is a great read for anyone interested in how GIS can help prepare governments to respond to the threat of exotic animal disease outbreaks.

Blanton, J. D., Manangan, A., Manangan, J., Hanlon, C. A., Slate, D., & Rupprecht, C. E. (2006). Development of a GIS-based, real-time Internet mapping tool for rabies surveillance. International journal of health geographics, 5(1), 47.

The goal of this project was to create a GIS-based surveillance system to better direct the efforts of rabies vaccine baiting programs in the United States. The result was a program which combines up to date information from local, state and federal agencies and displays it alongside geographic features. It is a very useful tool in determining where it is best to administer bait. Any local effort to manage rabies in wildlife will be able to quickly and easily discover the most sensible place to invest their resources to make the most impact. With the access to real-time, accurate data these rabies management efforts should be more successful in controlling or even eliminating the presence of rabies in a wildlife population. This project is also very useful as it is possible to use the same technology and process to map other diseases.

Broadfoot, Jim D., Richard C. Rosatte and David T. O’Leary. Raccoon and Skunk Population Models for Urban Disease Control Planning in Ontario, Canada. Ecological Applications, V11 N1: 295-303 (2001).

The authors of this paper developed population models of striped skunks and raccoons using RAMAS/GIS software for use in disease control efforts. Their goal was to create a model which identified areas in the city with a high density of raccoons or skunks and also to identify which parts of these populations were most likely to be a site of dispersal for diseases. These subpopulations with a high likelihood of dispersal (stabilizing at a population density well over carrying capacity) can then be targeted by disease control efforts. The result was a model which combined landscape map data with a population model comprised of animal dispersal functions and habitat specific demographic data. A useful read for those interested in how GIS can be used to assess which wildlife populations are at a high risk for disease.

Conner, M. M., & Miller, M. W.. (2004). Movement Patterns and Spatial Epidemiology of a Prion Disease in Mule Deer Population Units. Ecological Applications,14(6), 1870–1881.

This paper evaluates how the seasonal movements and dispersal of mule deer populations in north-central Colorado affect the spatial epidemiology of chronic wasting disease (CWD). The authors accomplished this by using cluster analyses on the location data of radio-collared mule deer to define population units and then compared the presence of CWD between these units. This study included spatial and temporal information to account for the importance of seasonal migration in the spread of the disease. Seasonal-ranges were delineated using the animal movements extension of ArcView GIS. Their results showed that migration was a more likely source of disease spread than dispersal movements and that CWD was more likely to spread within population units than between population units in the winter, when ranges were smaller and barely overlapped with other populations. This article is a good example of using GIS tools to track the spread of wildlife disease within and between populations.

Foley, J. E., Queen, E. V., Sacks, B., & Foley, P. (2005). GIS-facilitated spatial epidemiology of tick-borne diseases in coyotes (Canis latrans) in northern and coastal California. Comparative immunology, microbiology and infectious diseases, 28(3), 197-212.

The authors, researchers from University of California, Davis and California State University, examined the spatial and temporal factors affecting the transmission of two pathogens from ticks to coyotes. The latitude and longitude of each coyote sampled was joined to precipitation and vegetation polygons in ArcMap to analyze the relationship between the prevalence of the two pathogens with precipitation and ground cover. Their results showed an increased prevalence of the two diseases with increased rainfall and in blue oak foothill pine, montane hardwood, and redwood habitats. The prevalence decreased in coastal sagebrush and crop land habitat. These results give valuable information on what areas have the highest risk of tick-borne diseases. This study is useful for anyone interested in how GIS can help us understand the ecology of diseases.

French, Nigel P. & Piran C.L. White. (2004). The use of GIS in modeling the spatial and temporal spread of animal diseases. P. Durr & A. Gatrell (Eds.), GIS and Spatial Analysis in Veterinary Science (pp. 177-205). Cambridge, MA: CABI Publishing.

This is a book chapter on how spatial simulation models in GIS help increase our knowledge of infectious diseases in animal populations and our ability to manage them. My particular interest is in its emphasis on the use of GIS in understanding infections in wildlife such as rabies and bovine tuberculosis. It reviews wildlife disease models developed by other researchers including the fox rabies models by Mollison and Kuulasmaa (1985) which can be found in the further readings section of this bibliography. Anyone interested in using GIS to monitor the spatial and temporal spread of wildlife or livestock diseases and whether a disease management strategy is likely to fade out a disease can find the basics in this chapter. A separate chapter on using these models to manage wildlife diseases is also found in the same book.

Kalluri S, Gilruth P, Rogers D, Szczur M (2007) Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review. PLoS Pathog 3(10): 116.

This article focuses on the use of remote sensing and GIS as a method of active surveillance for vector-borne diseases. Satellite data on the temperature, humidity and land cover type of an area are compiled and used to identify environments which would support an abundance of vector species. Remote sensing techniques used to study mosquitoes, ticks, blackflies, tsetse flies, and sandflies are discussed. The authors also mention the prospect of using GIS mapping to track climate change and extreme weather events which may impact vector abundance in the future. The paper concludes that remote sensing has been effective in disease surveillance but that there are costs in gathering, processing and interpreting the data which must not come at the expense of cutting budgets for ground-level disease prevention techniques. Although this paper is focused on the spread of disease in humans, wildlife can also serve as a host to some of these diseases and this is a good reference for the use of GIS in wildlife disease surveillance.

Norman, S. A. (2008). Spatial epidemiology and GIS in marine mammal conservation medicine and disease research. EcoHealth, 5(3), 257-267.

The purpose of this article is to review the current use of GIS and spatial epidemiology in marine mammal disease surveillance and management and to recommend future research opportunities. The author notes that there are many unique challenges involved in studying diseases in marine mammals, such as poorly delineated population boundaries and difficulties finding animals to sample. However, spatial epidemiology and GIS have still been valuable tools for mapping populations, analyzing disease clusters and identifying environmental predictors of disease. The use of GIS in non-disease studies is also discussed. She concludes that because location is an extremely important component of disease investigation, spatial epidemiology and GIS are necessary tools for studying marine mammal diseases. The author makes suggestions for future efforts such as improving disease data collection and engaging in more multidisciplinary collaborations. This article serves as a great introduction to the definition of spatial epidemiology and Geographic Information Systems and how they can be applied to marine mammal research and wildlife disease surveillance.

Norman, S. A., Ronald F. DiGiacomo, Frances M. D. Gulland, John Scott Meschke, and Mark S. Lowry. Risk factors for an outbreak of leptospirosis in California sea lions (Zalophus californianus) in California. Journal of Wildlife Diseases(2008)44 (4), 837-844.

Stephanie A. Norman, the lead author in this article, also authored the spatial epidemiology and GIS in marine mammal conservation medicine and disease research article annotated above. While her work on that article provided a broad overview of the use of GIS in marine mammal disease research, this article is a more specific study on the disease leptospirosis in California sea lions. The goal of the article was to help determine where the disease was coming from and how it was being transmitted by evaluating the risk factors for the disease. They used GIS and logistic regression to compare demographic characteristics between sea lions with leptospirosis and control cases. Their results showed that seasonality, age, sex and proximity to dog parks were the most likely contributing factors to the spread of leptospirosis. More studies need to be completed to discover what factors surrounding dogs or dog parks are affecting this disease. This article shows a successful example of the use of a Geographic Information System for multivariate analyses to determine what risk factors are associated with a disease.

Pfeiffer, D.U. & M. Hugh-Jones. Geographical information systems as a tool in epidemiological assessment and wildlife disease management. Scientific and Technical Review of the Office International des Epizooties, V21 N1: 91-102 (2002).

This articles explains the use of GIS in studying wildlife diseases and follows the process from data input to presentation. The authors conclude that GIS is a particularly useful tool in studying wildlife because they are more mobile than livestock or companion animals, and GIS allows you to quickly interpret information stretching over a large area and with many variables. However they caution against misusing GIS now that it is readily available, and stress the importance of making sure the data and analyses you choose to use are correct. Although this article is from 2002, when the functionality of GIS wasn’t as developed as today, it is still a good resource for anyone with an interest in the basic understanding of how GIS can be used for wildlife disease management.

Rinaldi, L., Musella, V., Biggeri, A., & Cringoli, G. (2006). New insights into the application of geographical information systems and remote sensing in veterinary parasitology. Geospatial Health, 1(1), 33-47.

The authors of this article first discuss the basic concepts of Geographic Information Systems and Remote Sensing and then focus on their uses in veterinary parasitology. They highlight ecological analysis on the relationship between the distribution of a disease and its environmental risk factors and give the steps required for such an analysis. Also included is a section on the components of a geographical surveillance epidemiological system, which is a map of expected cases which can be used in decision making and impact assessment regarding different diseases. They conclude that epidemiology is a logical application of GIS in veterinary science. However, they also mention that GIS and RS do not fully solve research concerns about the availability and quality of data. This article does a good job of summarizing the many applications of GIS and RS in veterinary parasitology.

Rushton, S.P., Lurz, P.W.W., Gurnell, J. and Fuller, R. (2000), Modeling the spatial dynamics of parapoxvirus disease in red and grey squirrels: a possible cause of the decline in the red squirrel in the UK?. Journal of Applied Ecology, 37: 997–1012

In this study, researchers used habitat information held in a GIS to create a population dynamics model for red and gray squirrels. This population dynamics model was then integrated into a disease model to create a simulation of how the parapoxvirus disease would spread throughout different populations. The goal was to assess whether the interactions of red and gray squirrels in the presence of parapoxvirus could have been the cause of the extinction of the red squirrel in Norfolk. The results showed that the spread of this disease between squirrel populations and competition mediated by this disease could have been a cause in the red squirrels’ decline in the UK. Higher gray squirrel mortality rates led to better outcomes for red squirrels, while interactions with gray squirrels led to negative outcomes for red squirrels. However, they conclude that more information on how the disease transfers within and between species is necessary before it can be stated with certainty that gray squirrels were the main reservoir of this disease and that they were the cause of the red squirrels decline. This study shows the use of GIS in studying the interactions of different species and identifying potential wildlife disease reservoirs.

Shuai, J., Buck, P., Sockett, P., Aramini, J., & Pollari, F. (2006). A GIS-driven integrated real-time surveillance pilot system for national West Nile virus dead bird surveillance in Canada. International Journal of Health Geographics, 5 (1), 17.

The goal of the authors of this paper was to create a system using GIS which could show up to date information on the location of dead birds found with West Nile virus in Canada. This would be used to determine whether people in a certain area are at risk of the virus so they can take preventative measures early on. The result was a pilot system which delivered real-time updates on West Nile dead bird surveillance to the web. It has reduced the operation costs and improved the productivity of West Nile dead bird surveillance efforts. This system is a great example of how GIS can track wildlife diseases and their project results could be adapted to monitor other wildlife diseases. This article is similar to the Blanton et al. 2006 article previously annotated which created a real-time GIS-based rabies surveillance program in the United States.

Staubach, C., V. Schmid, L. Knorr-Held, M. Ziller. A Bayesian model for spatial wildlife disease prevalence data. Preventative Veterinary Medicine, V56: 75-87 (2002).

Because many disease estimates are produced through sampling from hunters they are not always a good indication of how prevalent the disease actually is. This is due to different sample sizes and spatial dependencies in different areas. In order to create a more accurate map of the occurrence of wildlife diseases you must account for the missing data that always comes with the impossibility of complete coverage during a survey of wildlife. This paper compares Beta-binomial or mixture models with a Bayesian model for spatial wildlife disease prevalence data of Pseudorabies in wild boar. They conclude that the Bayesian model is advantageous due to its ability to estimate disease prevalence in areas which are missing data. This is a useful article for those interested in the pros and cons of different models for spatial data and their use in wildlife disease monitoring.

Zou, L., Miller, S. N., & Schmidtmann, E. T. (2006). Mosquito larval habitat mapping using remote sensing and GIS: implications of coalbed methane development and West Nile virus. Journal of Medical Entomology, 43(5), 1034-1041.

The focus of this paper was to analyze coalbed methane extraction sites to determine if they would support mosquito development and potentially add to the risk of West Nile virus in Wyoming. Habitats which have the potential to host larval Coquillett mosquitoes were identified using GIS and RS technology. Their results showed that their method had satisfactory results at determining potential habitats. They found a 75% increase in potential larval habitats from 1999-2004 as a result of the discharge ponds at coalbed methane extraction sites. This article is similar to the Kalluri et al. 2007 article on surveillance of arthropod vector-born diseases annotated previously. It shows how GIS and RS can be used effectively to identify potential disease vectors and assess human environmental damage.

Recommended books and articles:

Bradley, Catherine A., and Sonia Altizer. “Urbanization and the ecology of wildlife diseases.” Trends in ecology & evolution 22, no. 2 (2007): 95-102.

Cromley, E. K., & McLafferty, S. L. (2011). GIS and public health. Guilford Press.

Elliot, Paul, Jon C. Wakefield, Nicola G. Best, and D. J. Briggs. Spatial epidemiology: methods and applications. Oxford University Press, 2000.

Foreyt, William J. Veterinary parasitology reference manual. John Wiley & Sons, 2013.

Mollison, D., & Kuulasmaa, K. (1985). Spatial epidemic models: theory and simulations. Population dynamics of rabies in wildlife, 8, 291-309.

Robinson, T. P., Stevenson, M., Stevens, K. B., Rogers, D. J., & Clements, A. C. (2008). Spatial analysis in epidemiology (pp. 32-44). Oxford: Oxford University Press.