Overview of The application of GIS and. Remote Sensing in Vector-Borne Disease Ecology

Transcription

1 Steven Engborg NRS 509 December 14, 2017 Final Project Overview of The application of GIS and Remote Sensing in Vector-Borne Disease Ecology Anyone who learns how to create, manage, or use a Geographic Information System (GIS) will immediately be able to see its very important role in the field of spatial epidemiology and public health. In fact, many of the first examples which students learning GIS will encounter are disease outbreak or transmission maps. By its very nature, a GIS lends itself to mapping widespread, dynamic, and pressing issues which impact human lives. Although remote sensing may not seem to lend itself to spatial epidemiology at first glance, it is just as powerful a tool at identifying where, when, and why outbreaks occur. Studies of vector-borne ecology are no different in their reliance on GIS and remote sensing than other spatial epidemiological studies, however, it is possible to map many more aspects of the possible transmission dynamics. The focus of this review was to chronical the application of both these technologies in the field of vector-borne disease ecology. The two most common approaches to using GIS/RS in the field of vector-borne disease ecology and spatial epidemiology are to create risk maps/distribution maps, and to generate metapopulation models of disease transmission. What is common to all these approaches of generating map visualizations are that they all use disease data from a country s National Institute of Health or equivalent. This is necessary to ensure up to date accurate information is being used. All visualizations use models which are created using data from NASA Terrestrial Observation and Prediction System (TOPS), Advanced Very High Resolution Radiometer (AVHRR), 3d Elevation Program (3DEP), Digital Elevation Model (DEM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LandSat (TM, ETM+, OLI) and the programs Microsoft Access, ERDAS IMAGINE, and ArcGIS. The purpose of creating a risk map is to visualize where the highest likelihood of infection are. The purpose of creating a distribution map is to see where a vector or reservoir habitat is located, which can be used for management decisions. There are typically three ways to use these technologies to make the risk and distribution visualizations, they are; mapping the distribution of vectors, mapping of disease outbreaks, and mapping the distribution of reservoirs (Ostfeld 2005). Although it is possible to spatially reference almost any abiotic variable on a map, it has been shown that arthropod vectors tend to be especially sensitive to variations in temperature and moisture, so most spatial epidemiological studies mapping vectors focus on these variables (Brownstein 2003; Diuk-Wasser 2010; Ostfeld 2005). Mapping disease outbreaks retrospectively can be performed to understand which variables influence the geographic patterns or rate of spread (Martin 2002; Rodriguez-Morales 2017). The least common mapping approach is to map the distribution of reservoirs of a human pathogen, as this will give some prediction power towards transmission risks for directly transmitted diseases (Glass 2000; Wang 2017).

2 My review has shown that there have been many studies that examine different aspects of the spatial dynamics of infectious diseases. What has not been as heavily studied is the connection between infectious disease and the landscape in which it occurs, that is to say, which abiotic factors, alone or in conjunction, and in what degree, dictate how and why an infectious disease appears or spreads. The future of GIS/RS application in vector-borne disease ecology is very exciting. Researchers will continue working to try and find the critical variable(s) that dictate the transmission dynamics of vector-borne disease. As ground level and platform level sensor technology continues to increase in capabilities, I am confident that more will be discovered about how to manage and prevent the outbreak of infectious diseases. Analogous to the effect which the development of vaccinations had on their targeted pathogens, I believe GIS/RS tools will lead to the extermination of certain infectious diseases in the near future.

3 Annotated Bibliography Brownstein, J. S., Holford, T. R., & Fish, D A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States. Environmental Health Perspectives, 111(9), In this paper, Brownstein et al. used a GIS and remotely sensed data to create a model to determine if deer ticks can live where they are not currently found. They did this using ground based observations were used along with ERDAS IMAGE with ARC GIS to interpolate climate patterns and calculate cell statistics for each ½ degree pixel in us. Researchers determined that 14% of the pixels where deer ticks are not currently found, are suitable habitat. This finding implies that deer tick distribution in the U.S is growing, and in the coming years they will continue to be found in new places. This finding is of importance to the health of people in these areas, as they will not be aware of these tick s behavior and the pathogens which they vector. This study highlights how GIS can be used to predict future epidemiological risks. Additionally, it shows that GIS can be used take steps to prevent a disease outbreak from occurring. Diuk-Wasser, M. A., Vourc'h, G., Cislo, P., Hoen, A. G., Melton, F., Hamer, S. A., Rowland, M., Cortinas, R., Hickling, G. J., Tsao, J. I., Barbour, A. G., Kitron, U., Piesman, J. and Fish, D Field and climatebased model for predicting the density of host-seeking nymphal Ixodes scapularis, an important vector of tick-borne disease agents in the eastern United States. Global Ecology and Biogeography, 19, In this paper, Diuk-Wasser et al. created a climate based model to predict nymphal stage black legged tick abundance. The climate model was created using the NASA Terrestrial Observation and Prediction System (TOPS), which is a tool that takes into account daily maximum/minimum temp, dew point, and solar radiation, to interpolate and average areas between data collection. Data from the Advanced Very High Resolution Radiometer (AVHRR) sensor was used for NDVI. The authors then overlaid field collected data onto the model in a GIS to create a risk map with both data displayed. Field data was collected at 304 sites over 3 years. Sites were selected from a spatially stratified design by setting up a sampling grid over the all 37 United States east of the 100 th meridian. From within each 2 degree grid state parks or other publicly accessibly forested areas were randomly selected to be sampled. It was found that the highest abundance of nymphal blacklegged ticks were found in the northeast and midwest as predicted by the model. The authors acknowledge that there are several incidence of bias within the study. I included it due to the impressive length and breadth showing the potential of GIS in large scale studies. Glass, G. E., Cheek, J. E., Patz, J. A., Shields, T. M., Doyle, T. J., Thoroughman, D. A., Hunt, D. K., Enscore, R. E., Gage, K. L., Irland, C., Bryan, R Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerging Infectious Diseases, 6(3),

4 In this paper, Glass et al. used remotely sensed data to create risk maps of Hantavirus in the southwestern United States. The authors accomplished this by modeling the distributions of the pathogen s reservoir, a deer mouse. The abundance of the deer mouse reservoir in the was predicted using the 6 bands from the Landsat thematic mapper images. Data from these images were used to determine plant community composition. DEM data was used to generate elevation, slope, and aspect values. The plant community composition coupled with abiotic DEM data was used to map deer mouse habitat. Although Hantavirus is not a vector-borne disease, I thought the use of remote sensing in this study was unique and clever, and the produce was a striking risk map for an infectious wildlife disease. The authors did not at the time add this remotely sensed data to a GIS, but did in subsequent works. Martin, C., Curtis, B., Fraser, C., Sharp, B The use of a GIS-based malaria information system for malaria research and control in South Africa. Health & Place, 8, This paper by Martin et al. describes the development of the first applications of pairing disease data from National Department of Health with GIS mapping to report malaria incidence in south Africa. Although this paper was not as current as others which I used for my project, I included it because it was very innovative for the time, and similar applications have mirrored it many times since. By coupling digital datasets (Microsoft Access) and GIS Mapinfo, a GIS, which the authors called a Malaria Information System, the malaria reporting process in South Africa was streamlined and standardized. Before this system was implemented, a monthly malaria summary report took 8 weeks to complete because much of the diagnosis, and notification of surveillance agents was done with hard copies. After the implementation, South African officials had access to timely, accurate, and relevant information for decision-making and research. The maps produced were used extensively in formulating malaria insecticide and drug policies, providing appropriate information for tourists, planning water resource and infrastructural developments, evaluating changes in malaria transmission over time, and allocating resources to control malaria (Martin et al 2002). Another perk to this system created by the authors is that it can be used to show trends in the disease over long periods of time, in order to monitor the efficacy of management approaches. This paper made it clear how a GIS can be used to create knowledge for informed decisions, and ultimately save human lives on a large scale. Ostfeld, R.S., Glass, G.E., Keesing, F Spatial epidemiology: an emerging (or re-emerging) discipline. Trends in Ecology & Evolution. 20(6), In this paper, Ostfeld et al. describe and review the approaches to spatial epidemiology, with several examples of each, as well as strengths and weaknesses. The review was split into three sections, mapping distribution of vectors, mapping of disease outbreaks over time, and mapping distribution of reservoirs. Although it is possible to spatially reference almost any abiotic variable on a map, it has been shown that arthropod vectors tend to be especially sensitive to variations in temperature and moisture, so most spatial epidemiological studies mapping vectors focus on these variables. Mapping disease outbreaks retrospectively can be performed to understand which variables influence the geographic patterns or rate of spread. The third section covered a lesser common approach which is to map the distribution of reservoirs of a human pathogen, as this will give some prediction power towards transmission risks for directly transmitted diseases (no vector). This paper was particularly informative because Glass has done much pioneering work in the United States using remote sensing data in order to model habitat and risk maps. Highlighted in this paper was the fact that most studies to that point

5 had focused on the spatial dynamics of infectious diseases, while few have focused on the connection between landscape composition/configuration and infectious disease. The paper argues that adoption of a more modern concept of ecological landscapes would prove to be very fruitful and greatly improved the prediction of disease risk. Rodriguez-Morales, A. J., Galindo-Marquez, M. L., García-Loaiza, C. J., Sabogal-Roman, J. A., Marin- Loaiza, S., Ayala, A. F., Lozada-Riascos, C. O., Sarmiento-Ospina, A., Vásquez-Serna, H., Jimenez- Canizales, C.E., Escalera-Antezana, J. P Mapping Zika virus infection using geographical information systems in Tolima, Colombia, F1000 Research, 5, 568. In this study, Rodriguez-Morales et al. were the first researchers that used a GIS to map the Zika virus infections. Using surveillance case data from the Colombia National Institute of Health, they were able to map the rates of infection (cases/100,000 pop.) in the Tolima department of Colombia. The authors took the surveillance data, input it into a Microsoft Access database, and then imported the data into the open source GIS software KOSMO Desktop 3.0 RCI. The geographic data was acquired from the Regional Information System of the Coffee-Triangle region. The paper was able to create a wonderful disease outbreak map of the Tolima department, as well as a second close up map showing the Ibague municipality, which was home to about half of all the Zika cases. I found this paper to be the most interesting because it focused on one a very recent epidemiological outbreak, and due to the Zika virus tetragenic effect, it received much publicity around the U.S. This paper highlighted the use of GISbased maps to help guide important decisions for control and management of infectious diseases on a regional scale. These decisions include pesticide applications, how to aliquot resources, risk information for travelers and citizens, and much more. Wang, Y.C., Yuen, R., Feng, C.C., Sithithaworn, P., Kim, I.H Assessing the role of landscape connectivity on Opisthorchis viverrini transmission dynamics. Parasitology International, 66(4), In this paper, Wang et al. discuss how the proximity of landscape features influences transmission dynamics of Opisthorchis viverrini (a parasitic liver fluke) in Southeast Asia. The authors focused on OV transmission between the human (definitive host) and the first intermediate snail host. To do this, they identified habitat patches using google earth 2014 imagery. Field surveys were done and GPS units were used to geo reference and mark all septic tanks in study areas, and to map drainage pipes and rice field boundaries. ASTER and DEM data was used to create a flow raster. All of the above mentioned products were then combined into ArcGIS to visualize and analyze connection between septic tanks and snail habitats. They found that the sites with the largest landscape component size and most connected snail patches has the most liver fluke parasites. This finding can be used to manage and decrease transmission of Ov in the region. This paper highlights the ability of a GIS to tease apart connections of very complex interactions within an ecosystem.

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