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A brief review of spatial analysis concepts and tools used for mapping, containment and risk modelling of infectious diseases and other illnesses

Published online by Cambridge University Press:  13 December 2013

GRAZIELLA CAPRARELLI  and
STEPHANIE FLETCHER
GRAZIELLA CAPRARELLI*
Affiliation:
Faculty of Science, School of the Environment, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia
STEPHANIE FLETCHER
Affiliation:
Faculty of Health, WHO Collaborating Centre for Nursing, Midwifery and Health Development, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia
*
* Corresponding author: School of Natural and Built Environments, University of South Australia, Bonython Jubilee Building, GPO Box 2471, Adelaide, SA 5001, Australia. E-mail: Graziella.Caprarelli@unisa.edu.au
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Summary

Fast response and decision making about containment, management, eradication and prevention of diseases, are increasingly important aspects of the work of public health officers and medical providers. Diseases and the agents causing them are spatially and temporally distributed, and effective countermeasures rely on methods that can timely locate the foci of infection, predict the distribution of illnesses and their causes, and evaluate the likelihood of epidemics. These methods require the use of large datasets from ecology, microbiology, health and environmental geography. Geodatabases integrating data from multiple sets of information are managed within the frame of geographic information systems (GIS). Many GIS software packages can be used with minimal training to query, map, analyse and interpret the data. In combination with other statistical or modelling software, predictive and spatio-temporal modelling can be carried out. This paper reviews some of the concepts and tools used in epidemiology and parasitology. The purpose of this review is to provide public health officers with the critical tools to decide about spatial analysis resources and the architecture for the prevention and surveillance systems best suited to their situations.

Keywords

Spatial analysisgeostatisticsgeographic information systemsepidemiologyparasitology
Type
Review Article
Information
Parasitology , Volume 141 , Issue 5 , April 2014 , pp. 581 - 601
Copyright
Copyright © Cambridge University Press 2013 

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References

Allen, T. R. and Wong, D. W. (2006). Exploring GIS, spatial statistics and remote sensing for risk assessment of vector-borne diseases: a West Nile virus example. International Journal of Risk Assessment and Management 6, 253–275.Google Scholar
Anselin, L. (1995). Local Indicators of Spatial Association – LISA. Geographical Analysis 27, 93–116.Google Scholar
Atkinson, J.-A. M., Gray, D. J., Clements, A. C. A., Barnes, T. S., McManus, D. P. and Yang, Y. R. (2013). Environmental changes impacting Echinococcus transmission: research to support predictive surveillance and control. Global Change Biology 19, 677–688.Google Scholar
Bailey, T. C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Longman Scientific & Technical, Harlow, UK.Google Scholar
Barati, M., Keshavarz-valian, H., Habibi-nokhandan, M., Raeisi, A., Faraji, L. and Salahi-moghaddam, A. (2012). Spatial outline of malaria transmission in Iran. Asian Pacific Journal of Tropical Medicine 5, 789–795.Google Scholar
Berke, O. (2004). Exploratory disease mapping: kriging the spatial risk function from regional count data. International Journal of Health Geographics 3, 18. doi: 10.1186/1476-072X-3-18.Google Scholar
Besag, J., York, J. and Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43, 1–59.Google Scholar
Best, N., Richardson, S. and Thomson, A. (2005). A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research 14, 35–59.CrossRefGoogle ScholarPubMed
Bone, C., Wulder, M. A., White, J. C., Robertson, C. and Nelson, T. A. (2013). A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran's I statistic. Applied Geography 40, 161–170.CrossRefGoogle Scholar
Brooker, S. (2010). Estimating the global distribution and disease burden of intestinal nematode infections: adding up the numbers – a review. International Journal for Parasitology 40, 1137–1144.CrossRefGoogle ScholarPubMed
Brooker, S. and Clements, A. C. A. (2009). Spatial heterogeneity of parasite co-infection: determinants and geostatistical prediction at regional scales. International Journal for Parasitology 39, 591–597.CrossRefGoogle ScholarPubMed
Brooker, S., Hay, S. I. and Bundy, D. A. P. (2002). Tools from ecology: useful for evaluating infection risk models? Trends in Parasitology 18, 70–74.Google Scholar
Brooker, S., Katabereine, N. B., Gyapong, J. O., Stothard, J. R. and Utzinger, J. (2009 a). Rapid mapping of schistosomiasis and other neglected tropical diseases in the context of integrated control programmes in Africa. Parasitology 136, 1707–1718.Google Scholar
Brooker, S., Kabatereine, N. B., Smith, J. L., Mupfasoni, D., Mwanje, M. T., Ndayishimiye, O., Lwambo, N. J. S., Mbotha, D., Karanja, P., Mwandawiro, C., Muchiri, E., Clements, A. C. A., Bundy, D. A. P. and Snow, R. W. (2009 b). An updated atlas of human helminth infections: the example of East Africa. International Journal of Health Geographics 8, 42. doi: 10.1186/1476-072X-8-42.Google Scholar
Brooker, S., Hotez, P. J. and Bundy, D. A. P. (2010). The Global Atlas of Helminth Infection: mapping the way forward in neglected tropical disease control. PLoS Neglected Tropical Diseases 4, e779. doi: 10.1371/journal.pntd.0000779.Google Scholar
Brooker, S. J., Pullan, R. L., Gitonga, C. W., Ashton, R. A., Koaczinski, J. H., Katabereine, N. B. and Snow, R. W. (2012). Plasmodium–helminth coinfection and its sources of heterogeneity across East Africa. Journal of Infectious Diseases 205, 841–852.CrossRefGoogle ScholarPubMed
Brus, D. J. and de Gruijter, J. J. (1997). Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with Discussion). Geoderma 80, 1–44.Google Scholar
Carrat, F. and Valleron, A.-J. (1992). Epidemiologic mapping using the “kriging” method: application to an influenza-like illness epidemic in France. American Journal of Epidemiology 135, 1293–1300.CrossRefGoogle Scholar
Chang, K.-t. (2010). Introduction to Geographic Information Systems, 5th Edn. McGraw Hill, New York, NY, USA.Google Scholar
Choo, L. and Walker, S. G. (2008). A new approach to investigating spatial variations of disease. Journal of the Royal Statistical Society A 171, 395–405.Google Scholar
Chung, K., Yang, D. and Bell, R. (2004). Health and GIS: toward spatial statistical analyses. Journal of Medical Systems 28, 349–360.Google Scholar
Clark, P. J. and Evans, F. C. (1954). Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology 35, 445–453.Google Scholar
Clements, A. C., Pfeiffer, D. U. and Martin, V. (2006 b). Application of knowledge-driven spatial modelling approaches and uncertainty management to a study of Rift Valley fever in Africa. International Journal of Health Geographics 5, 57. doi: 10.1186/1476-072X-5-57.Google Scholar
Clements, A. C. A., Moyeed, R. and Brooker, S. (2006 a). Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology 133, 711–719.Google Scholar
Clements, A. C. A., Brooker, S., Nyandindi, U., Fenwick, A. and Blair, L. (2008 a). Bayesian spatial analysis of a national urinary schistosomiasis questionnaire to assist geographic targeting of schistosomiasis control in Tanzania, East Africa. International Journal for Parasitology 38, 401–415.Google Scholar
Clements, A. C. A., Garba, A., Sacko, M., Touré, S., Dembelé, R., Landouré, A., Bosque-Oliva, E., Gabrielli, A. F. and Fenwick, A. (2008 b). Mapping the probability of schistosomiasis and associated uncertainty, West Africa. Emerging Infectious Diseases 14, 1629–1632.CrossRefGoogle ScholarPubMed
Clements, A. C. A., Deville, M.-A., Ndayishimiye, O., Brooker, S. and Fenwick, A. (2010). Spatial co-distribution of neglected tropical diseases in the West African Great Lakes region: revisiting the justification for integrated control. Tropical Medicine and International Health 15, 198–207.CrossRefGoogle Scholar
Cliff, A. D. and Ord, K. J. (1973). Spatial Autocorrelation. Pion, London, UK.Google Scholar
Cramb, S. M., Mengersen, K. L. and Baade, P. D. (2011). Developing the atlas of cancer in Queensland: methodological issues. International Journal of Health Geographics 10, 9. doi: 10.1186/1476-072X-10-9.Google Scholar
Cudmore, T. J., Björklund, N., Carroll, A. L. and Lindgren, B. S. (2010). Climate change and range expansion of an aggressive bark beetle: evidence of higher beetle reproduction in naïve host tree population. Journal of Applied Ecology 47, 1036–1043.Google Scholar
Dangendorf, F., Herbst, S., Reintjes, R. and Kistemann, T. (2002). Spatial patterns of diarrhoeal illnesses with regards to water supply structures – a GIS analysis. International Journal of Hygiene and Environmental Health 205, 183–191.CrossRefGoogle ScholarPubMed
Demírel, R. and Erdoğan, S. (2009). Determination of high risk regions of cutaneous leishmaniasis in Turkey using spatial analysis. Türkiye Parazitoloji Dergisi 33, 8–14.Google Scholar
De Smith, M. J., Goodchild, M. F. and Longley, P. A. (2013). Geospatial analysis: a comprehensive guide to principles, techniques and software tools. http://www.spatialanalysisonline.com/HTML/index.html.Google Scholar
Diggle, P. J. and Ribeiro, P. J. Jr. (2007). Model-based Geostatistics. Springer, New York, NY, USA.CrossRefGoogle Scholar
Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics. Applied Statistics 47, 299–350.Google Scholar
Duncombe, J., Clements, A., Hu, W., Weinstein, P., Ritchie, S. and Espino, F. E. (2012). Review: geographical information systems for dengue surveillance. American Journal of Tropical Medicine and Hygiene 86, 753–755.Google Scholar
Duncombe, J., Clements, A., Davis, J., Hu, W., Weinstein, P. and Ritchie, S. (2013). Spatiotemporal patterns of Aedes aegypti populations in Cairns, Australia: assessing drivers of dengue transmission. Tropical Medicine and International Health 18, 839–849.CrossRefGoogle ScholarPubMed
Ekpo, U., Mafiana, C. F., Adeofun, C. O., Solarin, A. R. T. and Idowu, A. B. (2008). Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria. BMC Infectious Diseases 8, 74. doi: 10.1186/1471-2334-8-74.Google Scholar
Fernández-Navarro, P., García-Pérez, J., Ramis, R., Boldo, E. and López-Abente, G. (2012). Proximity to mining industry and cancer mortality. Science of the Total Environment 435–436, 66–73.Google Scholar
Field, A., Miles, J. and Field, Z. (2012). Discovering Statistics Using R. Sage, London, UK.Google Scholar
Flegg, J. A., Patil, A. P., Venkatesan, M., Roper, C., Naidoo, I., Hay, S. I., Sibley, C. H. and Guerin, P. J. (2013). Spatiotemporal mathematical modelling of mutations of the dhps gene in African Plasmodium falciparum . Malaria Journal 12, 249. doi: 10.1186/1475-2875-12-249.Google Scholar
Fletcher, S. (2013). Gastrointestinal illnesses caused by microbes in Sydney, Australia. Ph.D. thesis. University of Technology, Sydney, Australia.Google Scholar
García-Pérez, J., López-Cima, M. F., Boldo, E., Fernández-Navarro, P., Aragonés, N., Pollán, M., Pérez-Gómez, B. and López-Abente, G. (2010). Leukemia-related mortality in towns lying in the vicinity of metal production and processing installations. Environment International 36, 746–753.Google Scholar
García-Pérez, J., Fernández-Navarro, P., Castelló, A., López-Cima, M. F., Ramis, R., Boldo, E. and López-Abente, G. (2013). Cancer mortality in towns in the vicinity of incinerators and installations for the recovery or disposal of hazardous waste. Environment International 51, 31–44.CrossRefGoogle ScholarPubMed
Getis, A. and Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189–206.Google Scholar
Gilbert, E. W. (1958). Pioneer maps of health and disease in England. Geographical Journal 124, 172–183.CrossRefGoogle Scholar
Glickman, M. E. and van Dyk, D. A. (2007). Basic Bayesian methods. In Methods in Molecular Biology 404: Topics in Biostatistics (ed. Ambrosius, W.), pp. 319–338. Humana Press Inc., Totowa, NJ, USA.Google Scholar
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology 228, 113–129.Google Scholar
Goovaerts, P. (2005). Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics 4, 31. doi: 10.1186/1476-072X-4-31.Google Scholar
Goovaerts, P. (2009 a). Medical geography: a promising field of application for geostatistics. Mathematical Geosciences 41, 243–264.Google Scholar
Goovaerts, P. (2009 b). Combining area-based and individual-level data in the geostatistical mapping of late-stage cancer incidence. Spatial and Spatio-temporal Epidemiology 1, 61–71.Google Scholar
Goovaerts, P. (2010). Combining areal and point data in geostatistical interpolation: applications to soil science and medical geography. Mathematical Geosciences 42, 535–554.Google Scholar
Goovaerts, P. (2012). Geostatistical analysis of health data with different levels of spatial aggregation. Spatial and Spatio-temporal Epidemiology 3, 83–92.Google Scholar
Goovaerts, P. and Gebreab, S. (2008). How does Piosson kriging compare to the popular BYM model for mapping disease risk? International Journal of Health Geographics 7, 6. doi: 10.1186/1476-072X-7-6.Google Scholar
Goujon-Bellec, S., Demoury, C., Guyot-Goubin, A., Hémon, D. and Clavel, J. (2011). Detection of clusters of a rare disease over a large territory: performance of cluster detection methods. International Journal of Health Geographics 10, 53. doi: 10.1186/1476-072X-10-53.Google Scholar
Gowland, R. L. and Western, A. G. (2012). Morbidity in the marshes: using spatial epidemiology to investigate skeletal evidence for malaria in Anglo-Saxon England (AD 410-1050). American Journal of Physical Anthropology 147, 301–311.Google Scholar
Green, D. M., Kiss, I. Z. and Kao, R. R. (2006). Modelling the initial spread of foot-and-mouth disease through animal movements. Proceedings of the Royal Society B 273, 2729–2735.Google Scholar
Guernier, V., Hochberg, M. E. and Guégan, J.-F. (2004). Ecology drives the worldwide distribution of human diseases. PLoS Biology 2, 0740–0746.Google Scholar
Haase, P. (1995). Spatial pattern analysis in ecology based on Ripley's K-function: introduction and methods of edge correction. Journal of Vegetation Science 6, 575–582.CrossRefGoogle Scholar
Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press, Cambridge, UK.Google Scholar
Hall, A. and Holland, C. (2000). Geographical variation in Ascaris lumbricoides fecundity and its implications for helminth control. Parasitology Today 16, 540–544.CrossRefGoogle ScholarPubMed
Hay, S. I. and Snow, R. W. (2006). The Malaria Atlas Project: developing global maps of malaria risk. PLoS Medicine 3, e473. doi: 10.1371/journal.pmed.0030473.Google Scholar
Hay, S. I., Battle, K. E., Pigott, D. M., Smith, D. L., Moyes, C. L., Bhatt, S., Brownstein, J. S., Collier, N., Myers, M. F., George, D. B. and Gething, P. W. (2013). Global mapping of infectious disease. Philosophical Transactions of the Royal Society B 368, 20120250.Google Scholar
Hooten, M. B. and Wikle, C. K. (2008). A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian collared-dove. Environmental and Ecological Statistics 15, 59–70.Google Scholar
Hooten, M. B. and Wikle, C. K. (2010 a). Statistical agent-based models for discrete spatio-temporal systems. Journal of the American Statistical Association 105, 236–248.Google Scholar
Hooten, M. B. and Wikle, C. K. (2010 b). Assessing North American influenza dynamics with a statistical SIRS model. Spatial and Spatio-temporal Epidemiology 1, 177–185.Google Scholar
Horst, M. A. and Coco, A. S. (2010). Observing the spread of common illnesses through a community: using geographic information systems (GIS) for surveillance. Journal of the American Board of Family Medicine 23, 32–41.Google Scholar
Hu, W., Clements, A., Williams, G., Tong, S. and Mengersen, K. (2010). Bayesian spatiotemporal analysis of socio-ecologic drivers of Ross River Virus transmission in Queensland, Australia. American Journal of Tropical Medicine and Hygiene 83, 722–728.Google Scholar
Huang, S. S., Yokoe, D. S., Stelling, J., Placzek, H., Kulldorff, M., Kleinman, K., O'Brien, T. F., Calderwood, M. S., Vostok, J., Dunn, J. and Platt, R. (2010). Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study. PLoS Medicine 7, e1000238. doi: 10.1371/journal.pmed.1000238.Google Scholar
Jackson, C., Best, N. and Richardson, S. (2008). Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors. Journal of the Royal Statistical Society A 171, 159–178.Google Scholar
Jackson, M. C., Huang, L., Luo, J., Hachey, M. and Feuer, E. (2009). Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers. International Journal of Health Geographics 8, 55. doi: 10.1186/1476-072X-8-55.Google Scholar
Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L. and Daszak, P. (2008). Global trends in emerging infectious diseases. Nature 451, 990–994.CrossRefGoogle ScholarPubMed
Jutla, A., Akanda, A. S., Huq, A., Faruque, A. S. G., Colwell, R. and Islam, S. (2013). A water marker monitored by satellites to predict seasonal endemic cholera. Remote Sensing Letters 4, 822–831.Google Scholar
Kelly, G. C., Hale, E., Donald, W., Batarii, W., Bugoro, H., Nausien, J., Smale, J., Palmer, K., Bobogare, A., Taleo, G., Vallely, A., Tanner, M., Vestergaard, L. S. and Clements, A. C. A. (2013). A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu. Malaria Journal 12, 108. doi: 10.1186/1475-2875-12-108.Google Scholar
Khan, O. A., Davenhall, W., Ali, M., Castillo-Salgado, C., Vasquez-Prokopec, G., Kitron, U., Soares Magalhães, R. J. and Clements, A. C. A. (2010). Geographical information systems and tropical medicine. Annals of Tropical Medicine and Parasitology 104, 303–318.Google Scholar
Kitron, U., Otieno, L. H., Hungerford, L. L., Odulaja, A., Brigham, W. U., Okello, O. O., Joselyn, M., Mohamed-Ahmed, M. M. and Cook, E. (1996). Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. Journal of Animal Ecology 65, 371–380.Google Scholar
Kleinschmidt, I., Bagayoko, M., Clarke, G. P. Y., Craig, M. and Le Sueur, D. (2000). A spatial statistical approach to malaria mapping. International Journal of Epidemiology 29, 355–361.Google Scholar
Krivoruchko, K. (2012). Empirical Bayesian Kriging implemented in ArcGIS Geostatistical Analyst. ESRI.com, Fall 2012.Google Scholar
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics – Theory and Methods 26, 1481–1496.CrossRefGoogle Scholar
Kulldorff, M. (1999). Spatial scan statistics: models, calculations, and applications. In Scan Statistics and Applications (ed. Glaz, J. and Balakrishnan, N.), pp. 303–322. Birkhäuser, Boston, MA, USA.Google Scholar
Lawson, A. B. (2009). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Chapman & Hall/CRC, Taylor and Francis, Boca Raton, FL, USA.Google Scholar
Lee, D. (2011). A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-temporal Epidemiology 2, 79–89.Google Scholar
Lee, D. (2013). Package ‘CARBayes’. http://cran.r-project.org/web/packages/CARBayes/CARBayes.pdf.Google Scholar
Leonardo, L. R., Rivera, P. T., Crisostomo, B. A., Sarol, J. N., Bantayan, N. C., Tiu, W. U. and Bergquist, N. R. (2005). A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems. Parassitologia 47, 105–114.Google ScholarPubMed
Leyland, A. H. and Davies, C. A. (2005). Empirical Bayes methods for disease mapping. Statistical Methods in Medical Research 14, 17–34.CrossRefGoogle ScholarPubMed
Loiseau, C., Harrigan, R. J., Bichet, C., Julliard, R., Garnier, S., Lendvai, A. Z., Chastel, O. and Sorci, G. (2013). Predictions of avian Plasmodium expansion under climate change. Scientific Reports 3, 1126. doi: 10.1038/srep01126.Google Scholar
Long, J. A., Robertson, C., Nathoo, F. S. and Nelson, T. A. (2012). A Bayesian space-time model for discrete spread processes on a lattice. Spatial and Spatio-temporal Epidemiology 3, 151–162.Google Scholar
Lunn, D. J., Thomas, A., Best, N. and Spiegenhalter, D. (2000). WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10, 325–337.Google Scholar
Martinez, R., Vidaurre, M., Najera, P., Loyola, E., Castillo-Salgado, C. and Eisner, C. (2001). SIGEpi: geographic information system in epidemiology and public health. Epidemiological Bulletin 22, 4–5.Google Scholar
Métras, R., Collins, L. M., White, R. G., Alonso, S., Chevalier, V., Thuranira-McKeever, C. and Pfeiffer, D. U. (2011). Rift Valley Fever epidemiology, surveillance, and control: what have models contributed? Vector-Borne and Zoonotic Diseases 11, 761–771.Google Scholar
Métras, R., Porphyre, T., Pfeiffer, D. U., Kemp, A., Thompson, P. N., Collins, L. M. and White, R. G. (2012). Exploratory space-time analyses of Rift Valley Fever in South Africa in 2008–2011. PLoS Neglected Tropical Diseases 6, e1808. doi: 10.1371/journal.pntd.0001808.Google Scholar
Moore, D. A. and Carpenter, T. E. (1999). Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiologic Reviews 21, 143–161.Google Scholar
Morand, S. (2012). Phylogeography helps with investigating the building of human parasite communities. Parasitology 139, 1966–1974.Google Scholar
Nieberding, C. M. and Olivieri, I. (2007). Parasites: proxies for host genealogy and ecology? Trends in Ecology and Evolution 22, 156–165.Google Scholar
Nieberding, C. M., Durette-Desset, M.-C., Vanderpoorten, A., Casanova, J. C., Ribas, A., Deffontaine, V., Feliu, C., Morand, S., Libois, R. and Michaux, J. R. (2008). Geography and host biogeography matter for understanding the phylogeography of a parasite. Molecular Phylogenetics and Evolution 47, 538–554.Google Scholar
Noor, A. M., Mohamed, M. B., Mugyenyi, C. K., Osman, M. A., Guessod, H. H., Kabaria, C. W., Ahmed, I. A., Nyonda, M., Cook, J., Drakeley, C. J., Mackinnon, M. J. and Snow, R. W. (2011). Establishing the extent of malaria transmission and challenges facing pre-elimination in the Republic of Djibouti. BMC Infectious Diseases 11, 121. doi: 10.1186/1471-2334-11-121.Google Scholar
Oliver, M. A., Muir, K. R., Webster, R., Parkes, S. E., Cameron, A. H., Stevens, M. C. G. and Mann, J. R. (1992). A geostatistical approach to the analysis of pattern in rare disease. Journal of Public Health Medicine 14, 280–289.Google Scholar
Oliver, M. A., Webster, R., Lajaunie, C., Muir, K. R., Parkes, S. E., Cameron, A. H., Stevens, M. C. G. and Mann, J. R. (1998). Binomial cokriging for estimating and mapping the risk of childhood cancer. IMA Journal of Mathematics Applied in Medicine and Biology 15, 279–297.Google Scholar
Ord, J. K. and Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis 27, 286–306.Google Scholar
Ortiz Valencia, L. I., De Paula Menezes Drumond Fortes, B. and de Andrade Medronho, R. (2005). Spatial Ascariasis risk estimation using socioeconomic variables. International Journal of Environmental Health Research 15, 411–424.Google Scholar
Patil, A. P., Gething, P. W., Piel, F. B. and Hay, S. I. (2011). Bayesian geostatistics in health cartography: the perspective of malaria. Trends in Parasitology 27, 246–253.Google Scholar
Pearson, R. L., Wachtel, H. and Ebi, K. L. (2000). Distance-weighted traffic density in proximity to a home is a risk factor for leukemia and other childhood cancers. Journal of the Air and Waste Management Association 50, 175–180.Google Scholar
Pfeiffer, D., Robinson, T., Stevenson, M., Stevens, K., Rogers, D. and Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford University Press, Oxford, UK.Google Scholar
Piantadosi, S., Byar, D. P. and Green, S. B. (1988). The ecological fallacy. American Journal of Epidemiology 127, 893–904.Google Scholar
Poulin, R., Morand, S. and Skorping, A. (2000). Evolutionary Biology of Host–parasite Relationships: Theory Meets Reality. Elsevier, New York, NY, USA.Google Scholar
Pullan, R., Sturrock, H. J. W., Soares Magalhães, R. J., Clements, A. C. A. and Brooker, S. J. (2012). Spatial parasite ecology and epidemiology: a review of methods and applications. Parasitology 139, 1870–1887.Google Scholar
Randremanana, R. V., Richard, V., Rakotomanana, F., Sabatier, P. and Bicout, D. J. (2010). Bayesian mapping of pulmonary tuberculosis in Antananarivo, Madagascar. BMC Infectious Diseases 10, 21. doi: 10.1186/1471-2334-10-21.Google Scholar
Raso, G., Matthys, B., N'Goran, E. K., Tanner, M., Vounatsou, P. and Utzinger, J. (2005). Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d'Ivoire. Parasitology 131, 97–108.Google Scholar
Rava, M., Crainicianu, C., Marcon, A., Cazzoletti, L., Pironi, V., Silocchi, C., Ricci, P. and de Marco, R. (2012). Proximity to wood industries and respiratory symptoms in children: a sensitivity analysis. Environmental International 38, 37–44.Google Scholar
Reid, H., Haque, U., Clements, A. C. A., Tatem, A. J., Vallely, A., Masud Ahmed, S., Islam, A. and Haque, R. (2010). Mapping malaria risk in Bangladesh using Bayesian geostatistical models. American Journal of Tropical Medicine and Hygiene 83, 861–867.Google Scholar
Reingold, A. (2003). If syndromic surveillance is the answer, what is the question? Biosecurity and Bioterrorism: Biodefence Strategy, Practice, and Science 1, 77–81.Google Scholar
Reinhardt, M., Elias, J., Albert, J., Frosch, M., Harmsen, D. and Vogel, U. (2008). EpiScanGIS: an online geographic surveillance system for meningococcal disease. International Journal of Health Geographics 7, 33. doi: 10.1186/1476-072X-7-33.Google Scholar
Riley, S. (2007). Large-scale spatial-transmission models of infectious disease. Science 316, 1298–1301.Google Scholar
Ripley, B. D. (1981). Spatial Statistics. John Wiley and Sons, Hoboken, NJ, USA.Google Scholar
Robertson, C., Nelson, T. A., MacNab, Y. and Lawson, A. B. (2010). Review of methods for space-time disease surveillance. Spatial and Spatio-temporal Epidemiology 1, 105–116.Google Scholar
Rogers, D. J. and Sedda, L. (2012). Statistical models for spatially explicit biological data. Parasitology 139, 1852–1869.Google Scholar
Soares Magalhães, R. J., Biritwum, N.-K., Gyapong, J. O., Brooker, S., Zhang, Y., Blair, L., Fenwick, A. and Clements, A. C. A. (2011 a). Mapping helminth co-infection and co-intensity: geostatistical prediction in Ghana. PLoS Neglected Tropical Diseases 5, e1200. doi: 10.1371/journal.pntd.0001200.Google Scholar
Soares Magalhães, R. J., Clements, A. C. A., Patil, A. P., Gething, P. W. and Brooker, S. (2011 b). The applications of model-based geostatistics in helminth epidemiology and control. Advances in Parasitology 74, 267–296.Google Scholar
Soares Magalhães, R. J., Langa, A., Sousa-Figueiredo, J. C., Clements, A. C. A. and Vaz Nery, S. (2012). Finding malaria hot-spots in northern Angola: the role of individual, household and environmental factors within a meso-endemic area. Malaria Journal 11, 385. doi: 10.1186/1475-2875-11-385.Google Scholar
Standley, C. J., Adriko, M., Alinaitwe, M., Kazibwe, F., Kabatereine, N. B. and Stothard, J. R. (2009). Intestinal schistosomiasis and soil-transmitted helminthiasis in Ugandan schoolchildren: a rapid mapping assessment. Geospatial Health 4, 39–53.Google Scholar
Stensgaard, A.-S., Vounatsou, P., Onapa, A. W., Simonsen, P. E., Pedersen, E. M., Rahbek, C. and Kristensen, T. K. (2011). Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity. Malaria Journal 10, 298. doi: 10.1186/1475-2875-10-298.Google Scholar
Sturrock, H. J. W., Pullan, R. L., Kihara, J. H., Mwandawiro, C. and Brooker, S. J. (2013). The use of bivariate spatial modelling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in coastal Kenya. PLoS Neglected Tropical Diseases 7, e2016. doi: 10.1371/journal.pntd.0002016.Google Scholar
Sun, D., Robert, K., Kim, H. and He, Z. (2000). Spatio-temporal interaction with disease mapping. Statistics in Medicine 19, 2015–2035.Google Scholar
Tango, T. and Takahashi, K. (2005). A flexibly shaped spatial scan statistics for detecting clusters. International Journal of Health Geographics 4, 11. doi: 10.1186/1476-072X-4-11.Google Scholar
Thomas, A., Best, N., Lunn, D., Arnold, R. and Spiegelhalter, D. (2004). GeoBUGS User Manual. (version 1.2). http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/geobugs12manual.pdf.Google Scholar
Tukiendorf, A. (2001). An ecological analysis of leukemia incidence around the highest 137Cs concentration in Poland. Cancer Causes and Control 12, 653–659.Google Scholar
Vinceti, M., Malagoli, C., Fabbi, S., Teggi, S., Rodolfi, R., Garavelli, L., Astolfi, G. and Rivieri, F. (2009). Risk of congenital anomalies around a municipal solid waste incinerator: a GIS-based case-control study. International Journal of Health Geographics 8, 8. doi: 10.1186/1476-072X-8-8.Google Scholar
Visser, O., van Wijnen, J. H. and van Leeuwen, F. E. (2005). Incidence of cancer in the area around Amsterdam Airport Schiphol in 1988–2003: a population-based ecological study. BMC Public Health 5, 127. doi: 10.1186/1471-2458-5-127.Google Scholar
Wakefield, J. (2009). Multi-level modelling, the ecologic fallacy, and hybrid study designs. International Journal of Epidemiology 38, 330. doi: 10.1093/ije/dyp179.Google Scholar
Waller, L. and Gotway, C. A. (2004). Applied Spatial Statistics for Public Health Data. John Wiley and Sons, Hoboken, NJ, USA.Google Scholar
Wanjala, C. L., Waitumbi, J., Zhou, G. and Githeko, A. K. (2011). Identification of malaria transmission and epidemic hotspots in the western Kenya highlands: its application to malaria epidemic prediction. Parasites and Vectors 4, 81. doi: 10.1186/1756-3305-4-81.Google Scholar
Weng, H.-H., Tsai, S.-S., Chiu, H.-F., Wu, T.-N. and Yang, C.-Y. (2008). Childhood leukemia and traffic air pollution in Taiwan: petrol station density as an indicator. Journal of Toxicology and Environmental Health, Part A: Current Issues 72, 83–87.Google Scholar
Whitworth, K. W., Symanski, E., Lai, D. and Coker, A. L. (2011). Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study. Environmental Health 10, 21. doi: 10.1186/1476-069X-10-21.Google Scholar
Wolfe, N. D., Panosian Dunavan, C. and Diamond, J. (2007). Origins of major human infectious diseases. Nature 447, 279–283.Google Scholar
World Health Organization (2002). Prevention and Control of Schistosomiasis and Soil-Transmitted Helminthiasis: Report of a WHO Expert Committee. WHO Technical Report Series No. 912. World Health Organization, Geneva, Switzerland.Google Scholar
Yang, G.-J., Vounatsou, P., Zhou, X.-N., Utzinger, J. and Tanner, M. (2005). A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Tropica 96, 117–129.Google Scholar
Yang, Y. R., Clements, A. C. A., Gray, D. J., Atkinson, J.-A. M., Williams, G. M., Barnes, T. S. and McManus, D. P. (2012). Impact of anthropogenic and natural environmental changes on Echinococcus transmission in Ningxia Hui Autonomous Region, the People's Republic of China. Parasites and Vectors 5, 146. doi: 10.1186/1756-3305-5-146.Google Scholar
Yu, C.-L., Wang, S.-F., Pan, P.-C., Wu, M.-T., Ho, C.-H., Smith, T. J., Li, Y., Pothier, L., Christiani, D. C. and Kaohsiung Leukemia Research Group (2006). Residential exposure to petrochemicals and the risk of leukemia: using geographic information system tools to estimate individual-level residential exposure. American Journal of Epidemiology 164, 200–207.Google Scholar
Zhong, S., Xue, Y., Cao, C., Cao, W., Li, X., Guo, J. and Fang, L. (2005). Explore disease mapping of Hepatitis B using geostatistical analysis techniques. In ICCS 2005 (eds. Sunderam, V. S., van Albada, G. D., Sloot, P. M. A. and Dongarra, J. J.), pp. 464–471. Springer-Verlag, Berlin, Germany.Google Scholar