Geographic Information Science (GIS) has many applications in the field of Public Health. GIS has the ability to combine data from many sources for identification and mapping of environmental factors associated with disease factors which make it particularly useful for disease surveillance and monitoring. It can be a useful tool for analyzing the spread of diseases in both developed and developing countries as well as a management strategy for allocating resources and for understanding high risk areas of disease. This web page was intended to be a resource of useful articles for anybody wishing to learn more about the use of GIS in disease mapping.
Brooker, S., and Utzinger, J. (2007). Integrated disease mapping in a polyparasitic world Geospatial Health 2. pp. 141-146.
This paper focuses on the need for spatial analysis of neglected tropical diseases (NTD’s) such as Chagas disease in South America, human African trypanosomiasis, leishmaniasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiasis and trachoma. With funding coming in to tackle NTD’s the authors feel there is a need to allocate public-health resources as an essential first step in order to delineate and understand the spatial distribution of different parasitic diseases. They propose work for different parasite species in varying transmission settings, along with an improved understanding of spatial risk factors of different parasite species which would allow the projection of co-endemicity on the basis of remotely sensed satellite data, as well as behavioural, demographic, epidemiological and socio-economic risk factors. They feel that this research would aid in the development of risk maps which could then be used to identify large-scale patterns of potential overlap, and therefore guide regional and national level integrated disease control efforts. Numerous geospatial issues require attention, including geostatistical techniques that would go hand-in-hand with field studies and operational research, which would require collaboration among geospatial scientists and public-health specialists.
Clements, A., Moyeed, R., and Brooker, S. (2006). Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology, vol. 133, 711–719.
The authors of this study argue that intensity of infection has greater relevance to the transmission dynamics of a given host-parasite system and for understanding the occurrence of morbidity. They developed a Bayesian geostatistical model to predict the intensity of infection with Schistosoma mansoni in East Africa. They combined epidemiological data for 31 458 Kenyan schoolchildren with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. They identified the role of environmental risk factors in explaining geographical
heterogeneity in infection intensity and showed how these factors can be used to develop a predictive map. This map would provide an empirical basis for identifying priority areas when implementing control and for predicting the potential impact of control.
Cromley, E. (2003). GIS and Disease. Annual Review of Public Health. Vol. 24, pp.7–24.
This paper is a review that looks at the use of GIS in mapping diseases in the United States. The first section of the review surveys GIS research on the sources and distribution of disease agents. This section includes research on the sources and distribution of environmental contaminants and biological agents and considers the effects of environmental conditions on biological agents. The second part of the review considers how GIS are being used to investigate exposure to disease agents. This includes the role GIS can play in assessing environmental quality, modeling different exposure mechanisms, and documenting environmental inequities, differential exposure to agents based on race and class. The third section examines studies that describe and analyze the geographical distribution of health outcomes alone and studies that attempt to integrate and analyze information on all stages in the hazard-exposure-outcome process. This is a helpful review looking at the work that had been done in the U.S. using GIS to map various diseases and exposures.
Hendrickx, G., de La Rocque, S. et al. (2001). Spatial trypanosomosis management: from data-layers to decision making. Trends in Parasitology. Vol.17 No.1 January, pp. 35-41.
This article looks at using GIS as a management tool to assist decisions on allocation of resources, prioritization of control areas, and planning and management of field operations for African animal trypanosomosis in sub-Saharan Africa. They look at developing different data layers based on vector distribution and abundance and disease mapping. They look at different GIS systems that have been developed at continental,
national and local level in order to determine areas where the control of the disease is mostly likely to enhance livestock production, an integration of livestock and cropping which is not only economically beneficial, but also environmentally sound and technically feasible. This is an interesting article about using GIS to make important resource allocation decisions.
Lindsay, S.W., Parson, L., and C.J. Thomas. (1998). Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proceedings of the Royal Society of London. B Vol. 265, pp. 847-854.
This study looked at predicting the range and relative abundance of the two main malaria vectors by using GIS to map climate surfaces. The maps created showed very good agreement with published maps. This technique represents a new approach to mapping the distribution of malaria vectors over large areas and may facilitate species-specific vector control activities. The authors used a simple nonlinear model to describe relative abundance that was derived from survey data collected in West Africa and applied it to the whole of Africa and tested the generated map against field data on Tanzania. These findings can potentially be of relevance to control programmes because they were able to identify different patterns of malaria transmission in areas where field data was not available. This is a great example of using GIS to look at vegetation and habitat to predict the range of mosquitoes and to provide information that can be used as a disease management tool.
Njemanze, P.C., Anozie, J., Ihenacho, J.O., Russell, M.J., and Uwaeziozi, A.B. (1999). Application of Risk Analysis and Geographic Information System Technoloies to the Prevention of Diarrheal Diseases in Nigeria. American Journal of Tropical Medicine Hygiene. Vol. 61, Issue 3, pp. 356–360.
This paper shows the use of GIS and Risk Analysis (RA) methods in evaluating the health impact of water resources of Imo State, Nigeria. Their purpose was to devise a model that will ultimately lead to reduction in the incidence of diarrheal diseases related to water-borne infections. The authors converted maps of the state into a digital form using ARC/INFO GIS software. The resulting coverages included geology, hydrology, towns, and villages. They collected reports of diarrhea and evaluated water sources. In order to look at the contribution from these various layers to the overall probability of a hazard occurring, each data layer was considered in order of what is referred to as a probabilistic layer analysis (PLA). This is a method of risk assessment that begins with identification of a hazard endpoint and weighs the factors contributing to this endpoint or its probability at each layer of a spatially referenced database. This paper is a good example of converting maps to a digital format in order to analyze trends in diarrhea and water-borne diseases.
Omumbo, J. et al. (1998). Mapping malaria transmission intensity using geographical information systems (GIS): an example from Kenya. Annals of Tropical Medicine & Parasitology, Vol. 92, No. 1, 7-21.
There are few detailed maps of either the risks of exposure or disease for malaria which is Africa’s single largest cause of mortality. The current paper focuses on the idea that transmission intensity is an important determinant of disease outcome and should be used to guide control activities. One of the intentions of the study was to establish how much epidemiological data related to malaria transmission intensity already existed within Kenya. They looked at parasitological data from 682 cross-sectional surveys conducted in Kenya and spatially defined them. A review of this data on malaria was then used to examine the parasite ratio’s robustness over time, across age-groups and within communities and to produce endemicity maps using geographical information systems. Through the use of GIS, models of endemicity can be developed to provide high resolution maps of malaria risks. They can also be used in combination with data on population distribution to identify target control stratagems which combine resource availability and an appreciation of local epidemiology. This paper provided a useful review of the information that has already been done towards mapping malaria exposure in Kenya and what still needs to be done.
Snow, R., Marsh, K., and le Sueur, D. (1996). The Need for Maps of Transmission Intensity to Guide Malaria Control in Africa. Parasitology Today, vol 12,
This paper also stresses the need and value of creating maps in order to build a spatial information platform using geographic information systems (GIS) that would integrate digital and attribute data. These digital data sets would include existing continental data bases such as population, topography, climate and administrative boundaries. These maps are necessary for understanding, analyzing and controlling disease burdens and risks of malaria in Africa which up to this point had not been done.
Snow, R. et al. (1998). Models to predict the intensity of Plasmodium falciparum transmission: applications to the burden of disease in Kenya. Transactions of the Royal Society of Tropical Medicine and Hygiene. Vol.92, pp. 601-606.
This paper presents a model to estimate the burden of malaria and its fatal consequences in Kenya. They find that climate affects the vectorial capacity of P. falciparum transmission. They used empirical data on P. falciparum infection rates among 124 childhood populations in Kenya to develop a climate-based statistical model of transmission intensity. They applied this model to meteorological and remote sensed data using a geographical information system to provide estimates of endemicity for all of the 1080 populated fourth level administrative regions in Kenya. This information provided the basis for an informed estimate of the annual morbidity and mortality burden of malaria among Kenyan children. This information can also be used to provide an epidemiological basis for rational disease control.
Tanser, F. and David le Sueur. (2002). The application of geographical information systems to important public health problems in Africa. International Journal of Health Geographics. Vol 1. Issue 4.
This article evaluates whether using GIS to map different diseases in Africa is applicable and sustainable in that setting. They look at HIV, tuberculosis and malaria as these three diseases have a great impact on public health and all have very different modes of transmission. The authors review Africa’s health priorities and the health related GIS research that has been done recently. They also outline the work that they have done, the general trends for GIS relevant to Africa and describe some obstacles in sustaining GIS. They give two viable GIS health applications for Africa, first as a research tool and secondly as a health planning and management tool and for exploratory data analysis. This article is useful as it provides information for three different diseases that have varying routes of transmission and health outcomes.
Other useful articles not reviewed here:
Abler, R. F. 1987. The National Science Foundation National Center for Geographic Information and Analysis. International Journal of Geographic Information Systems 1:303-26
Beck LR, Rodrigues MH, Dister SW, Rodrigues AD, Rejmankova E, Ulloa A, et al. 1994. Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am J Trop Med Hyg 51:271-80.
Clarke KC, Osleeb JR, Sherry JM, Meert JP, Larsson RW. 1991. The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm) eradication effort. Prev Vet Med 11:229- 35.
Clarke, K. C., McLafferty, S. L. and B. J. Tempalski. 1996. On Epidemiology and Geographic Information Systems: A Review and Discussion of Future Directions. Emerging Infectious Diseases 2:85-92.
Glass G.E., Schwartz B.S., Morgan J.M. III, Johnson D.T., Noy P.M., Israel E. 1995. Environmental risk factors for Lyme disease identified with geographic information systems. Am J Public Health 85:944-8.
Griffith, D. A. 1992. Distance Calculations and Error in Geographic Databases. In Goodchild, M and S. Gopal (eds.) The Accuracy of Spatial Databases. New York: Taylor and Francis 81-90.
Kitron U, Pener H, Costin C, Orshan L, Greenberg Z, Shalom U. 1994. Geographic information system in malaria surveillance: mosquito breeding and imported cases in Israel, 1992. Am J Trop Med Hyg 50:550-6.
Le Sueur, D and C. Martin. 1996. Regional and Continental Initiatives in Malaria Control. Proceedings of the Seventh International Symposium in Medical Geography. Portsmouth, July 30 – August 2, 1996. 184-186.
Oppong, J. R. 1998. A Vulnerability Interpretation of the Geography of HIV-AIDS in Ghana 1986-1995. Professional Geographer 50(4):437-448.
Richards F. O., Jr. 1993. Use of geographic information systems in control programs for onchocerciasis in Guatemala. Bull Pan Am Health Organ 27:52-5.
Roger D. J., Williams B.G. 1993. Monitoring trypanosomiasis in space and time. Parasitology 106(Suppl):277-92.
Tempalski B. J. 1994. The case of Guinea worm: GIS as a tool for the analysis of disease control policy. Geographic Information Systems 4:32-8.
Gould, P. 1993. The Slow Plague - A Geography of the AIDS Pandemic. Cambridge: Blackwell.