Modelling interactions between vector-borne diseases and environment using GIS

Modelling Interactions between Vector-Borne Diseases and Environment Using GIS

Volumn , Issue , 2015, Pages 1-230

  Khormi, Hassan M ^a   Kumar, Lalit ^b  

^a UMM AL QURA UNIVERSITY   (Saudi Arabia)

^b UNIVERSITY OF NEW ENGLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; CLIMATE MODELS; DEVELOPING COUNTRIES; DISEASE CONTROL; INFORMATION MANAGEMENT; INFORMATION USE; MAPPING; PROGRAM DEBUGGING; QUALITY CONTROL; RISK ASSESSMENT; SPATIAL VARIABLES MEASUREMENT; VECTORS;

CLIMATIC CHANGES; POTENTIAL DISTRIBUTIONS; PREDICTIVE MODELLING; SPATIAL ANALYSIS; SPATIAL AND TEMPORAL SCALE; STEP-BY-STEP INSTRUCTIONS; VECTOR DISTRIBUTION; VECTOR-BORNE DISEASE;

GEOGRAPHIC INFORMATION SYSTEMS;

EID: 84996596944     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b18433     Document Type: Book

References (361)

1. Church, J.A., White, N.J. (2006). A 20th century acceleration in global sea level rise. Geophysical Research Letters, 33(1): L01602. doi:10.1029/2005GL024826
2. Eisen, L., Eisen, R.J. (2011). Using geographic information systems and decision support systems for the prediction, preven-tion, and control of vector-borne diseases. Annual Review Entomology, 56: 41-61.
3. European Centre for Disease Prevention and Control. (2014). Vector-diseases. Retrieved from http://www.ecdc.europa.eu/en/healthtopics/climate_change/health_effects/pages/vector_borne_diseases.aspx
4. Howe, G.M. (1989). Historical evolution of disease mapping in gen-eral and specifically of cancer mapping. Recent Results Cancer Research, 114: 1-21.
5. Kalluri, S., Gilruth, P., Rogers, D., Szczur, M. (2007). Surveillance of arthropod vector-borne infectious diseases using remote sens-ing techniques: a review. PLoS Pathogens, 3(10): e116.
6. Khormi, H., Kumar, L. (2014). Climate change and the potential global distribution of Aedes aegypti: spatial modelling using GIS and CLIMEX. Geospatial Health, 8(2): 405-415.
7. Khormi, H., Kumar, L. (2012). Assessing the risk for dengue fever based on socioeconomic and environmental variables in a GIS environment. Geospatial Health, 6(2): 171-176.
8. Khormi, H., Kumar, L., Elzahrany, R. (2011). Describing and analys-ing the association between meteorological variables and adult Aedes Aegypti mosquitoes. Journal of Food, Agriculture and Environment, 9: 954-959.
9. Lawson, A.B., Biggeri, A.B., Boehning, D., Lesaffre, E., Viel, J.F., Clark, A., Schlattmann, P., Divino, F. (2000). Disease map-ping models: an empirical evaluation. Disease Mapping Collaborative Group. Statistics in Medicine, 19: 2217-2241.
10. Lawson, A.B., Böhning, D., Biggeri, A., Lesaffre, E., Viel, J.F. (1999). Disease mapping and its uses. In: Lawson, A., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J.F., Bertollini, R. (Eds.), Disease Mapping and Risk Assessment for Public Health. West Sussex, UK: Wiley; 3-13.
11. Lemon, S.M., Sparling, P.F., Hamburg, M.A., Relman, D.A., Choffnes, E.R., Mack, A. (2008). Vector Borne Diseases: Understanding the Environmental, Human Health and Ecological Connections, Washington, DC: National Academies Press.
12. Ogden, N.H., Lindsay, L.R., Hanincová, K., Barker, I.K., Bigras-Poulin, M., Charron, D.F., Heagy, A., Francis, C.M., O’Callaghan, C.J., Schwartz, I., Thompson, R.A. (2008). The role of migratory birds in introduction and range expansion of Ixodes scapularis ticks, and Borrelia burgdorferi and Anaplasma phagocyto-philum in Canada. Applied Environmental Microbiology, 74: 1780-1790.
13. Patz, J.A., Olson, S.H. (2008). Climate change and health: global to local influences on disease risk. In: Lemon, S.M., Sparling, P.F., Hamburg, M.A., Relman, D.A., Choffnes, E.R., Mack, A. (Eds.), Vector-Borne Diseases: Understanding the Environmental, Human Health, and Ecological Connections. Washington, DC: National Academies Press; 88-103.
14. Patz, J.A., Strzepek, K., Lele, S., Hedden, M., Greene, S., Noden, B., Hay, S.I., Kalkstein, L., Beier, J.C. (1998). Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya. Tropical Medicine and International Health, 3(10): 818-827.
15. Rytkonen, M.J.P. (2004). Not all maps are equal: GIS and spatial analysis in epidemiology. International Journal of Circumpolar Health, 63(1): 9-24.
16. Smith, K.R., Woodward, A., Campbell-Lendrum, D., Chadee, D.D., Honda, Y., Liu, Q., Olwoch, J.M., Revich, B., Sauerborn, R. (2014). Human health: impacts, adaptation, and co-benefits. In: Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., White, L.L. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 709-754.
17. Snow, J. (1855). On the Mode of Communication of Cholera. 2nd ed. London: Churchill.
18. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2): 234-240.
19. World Health Organisation (WHO). (2014). Vector-borne disease. Retrieved September 9, 2014, from the Health and Environment Linkages Initiative (HELI), http:// www.who.int/heli/risks/vectors/vector/en/
20. Abdullahi, F.B. (2013). A comparative analysis of geographical infor-mation system (GIS) and electronic health (E-Health) in deci-sion making. International Journal of Research in Engineering, IT and Social Sciences, 3: 61-71.
21. Andreo, V., Neteler, M., Rocchini, D., Provensal, C., Levis, S., Porcasi, X., Rizzoli, A., Lanfri, M., Scavuzzo, M., Pini, N., Enria, D., Polop, J. (2014). Estimating hantavirus risk in Southern Argentina: a GIS-based approach combining human cases and host distribution. Viruses, 6: 201-222.
22. Bailey, T. (1994). A review of statistical spatial analysis in geographi-cal information systems. In Fotheringham, S., Rogerson, P. (Eds.), Spatial Analysis and GIS. London: Taylor and Francis.
23. Barbu, C.M., Hong, A., Manne, J.M., Small, D.S., Quintanilla Calderón, J.E., Sethuraman, K., Quispe-Machaca, V., Ancca-Juárez, J., Cornejo del Carpio, J.G., Málaga Chavez, F.S., Náquira, C., Levy, M.Z. (2013). The effects of city streets on an urban disease vector. PLoS Computational Biology, 9(1): e1002801. doi:10.1371/journal.pcbi.100280.
24. Bhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., Drake, J.M., Brownstein, J.S., Hoen, A.G., Sankoh, O., Myers, M.F., George, D.B., Jaenisch, T., Wint, G.R.W., Simmons, C.P., Scott, T.W., Farrar, J.J., Hay, S.I. (2013). The global distribution and burden of dengue. Nature, 496: 504-507.
25. Bhunia, G.S., Kesari, S., Jeyaram, A., Kumar, V., Das, P. (2010). Influence of topography on the endemicity of Kala-azar: a study based on remote sensing and geographical information. Geospatial Health, 4(2): 155-165.
26. Booman, M., Durrheim, D.N., La Grange, K., Martin, C., Mabuza, A.M., Zitha, A., Mbokazi, F.M., Fraser, C., Sharp, B.L. (2000). Using a geographical information system to plan a malaria con-trol programme in South Africa. Bulletin of the World Health Organization, 78: 1438-1444.
27. Bynum, W.F. (2013). On the mode of communication of cholera: W.F. Bynum reassesses the work of John Snow, the Victorian ‘cholera cartographer’. Nature, 495(7440): 169-170.
28. Chang, A.Y., Parrales, M. E., Jimenez, J., Sobieszczyk, M.E., Hammer, S.M., Copenhaver, D.J., Kulkarni, R.P. (2009). Combining Google Earth and GIS mapping technolo-gies in a dengue surveillance system for developing coun-tries. International Journal of Health Geographics, 8: 49. doi:10.1186/1476-072X-8-49
29. Chaput, E.K., Meek, J.I., Heimer, R. (2002). Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerging Infectious Diseases, 8: 943-948.
30. Chu, H.J., Chan, T.C., Jao, F.J. (2013). GIS-aided planning of insecticide spraying to control dengue transmis-sion. International Journal of Health Geographics, 12: 42. doi:10.1186/1476-072X-12-42
31. Clarke, K.C., McLafferty, S.L., Tempalski, B.J. (1996). On epidemiol-ogy and geographic information systems: a review and dis-cussion of future directions. Emerging Infectious Diseases, 2: 85-92.
32. Coleman, M., Coleman, M., Mabuza, A.M., Kok, G., Coetzee, M., Durrheim, D.N. (2009). Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malaria Journal, 8: 68. doi:10.1186/1475-2875-8-68
33. Coleman, M., Sharp, B., Seocharan, I., Hemingway, J. (2006). Developing an evidence-based decision support system for rational insecticide choice in the control of African malaria vec-tors. Journal of Medical Entomology, 43: 663-668.
34. Cooke, W.H., III, Grala, K., Wallis, R.C. (2006). Avian GIS mod-els signal human risk for West Nile virus in Mississippi. International Journal of Health Geographics, 5: 36. doi:10.1186/1476-072X-5-36
35. Daash, A., Srivastava, A., Nagpal, B.N., Saxena, R. Gupta, S.K. (2009). Geographical information system (GIS) in decision support to control malaria-a case study of Koraput district in Orissa, India. Journal of Vector Borne Diseases, 46: 72-74.
36. de Oliveira, E.D., dos Santos, E.S., Zeilhofer, P., Souza-Santos, R., Atanaka-M. (2013). Geographic information systems and logistic regression for high-resolution malaria risk map-ping in a rural settlement of the southern Brazilian Amazon. Malaria Journal, 12(1): 420. doi:10.1186/1475-2875-12-420
37. Ducheyne, E., De Deken, R., Bécu, S., Codina, B., Nomikou, K., Mangana-Vougiaki, O., Georgiev, G., Purse, B.V., Hendrickx, G. (2007). Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospatial Health, 1(2): 177-189.
38. Eisen, L., Eisen, R.J. (2011). Using geographic information systems and decision support systems for the prediction, preven-tion, and control of vector-borne diseases. Annual Review of Entomology, 56: 41-61.
39. Eisen, L., Lozano-Fuentes, S. (2009). Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Neglected Tropical Diseases, 3(4): e411. doi:10.1371/journal.pntd.0000411
40. Esri. (2013). ArcMap 10.2. Redlands, CA: Esri.
41. Foody, G.M. (2006). GIS: health applications. Progress in Physical Geography, 30(5): 691-695.
42. Gajović, V., Todorović, B. (2013). Spatial and temporal analy-sis of fires in Serbia for period 2000-2013. Journal of the Geographical Institute Jovan Cvijic, SASA, 63: 297-312.
43. Gatrell, A.C., Bailey, T.C. (1996). Interactive spatial data analysis in medical geography. Social Science and Medicine, 42: 843-855.
44. Gettinby, G., Revie, C., Forsyth, A.J. (1992). Modelling: a review of systems and approaches for vector-transmitted and other parasitic diseases in developing countries. Proceedings of a workshop organised jointly by the International Laboratory for Research on Animal Diseases and the Food and Agriculture Organisation of the United Nations, Nairobi, Kenya.
45. Guerra, M., Walker, E., Jones, C., Paskewitz, S., Cortinas, M.R., Stancil, A., Beck, L., Bobo, M., Kitron, U. (2002). Predicting the risk of Lyme disease: habitat suitability for Ixodes scapularis in the North Central United States. Emerging Infectious Diseases, 8(3): 289-297.
46. Hemingway, J., Beaty, B.J., Rowland, M., Scott, T.W., Sharp, B.L. (2006). The innovative vector control consortium: improved control of mosquito-borne diseases. Trends in Parasitology, 22: 308-312.
47. Hendrickx, G. (1999). Georeferenced Decision Support Methodology Towards Trypanosomosis Management in West Africa. PhD the-sis, University of Gent.
48. Hendrickx, G., de La Rocque, S., Reid, R., Wint, W. (2001). Spatial trypanosomosis management: from data-layers to decision making. Trends in Parasitology, 17: 35-41.
49. Hernández-Avila, J.E., Rodríguez, M.H., Betanzos-Reyes, A.F., Danis-Lozano, R., Méndez-Galván, J.F., Velázquez-Monroy, O.J., Tapia-Conyer, R. (2006). Determinant factors for malaria transmission on the coast of Oaxaca State, the main residual transmission focus in Mexico. Salud Pública de México, 48: 405-417.
50. Huang, Z., Das, A., Qiu, Y., Tatem, A.J. (2012). Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool. International Journal of Health Geographics, 11: 1-14.
51. Jacob, B.G., Burkett-Cadena, N.D., Luvall, J.C., Parcak, S.H., McClure, C.J.W., Estep, L.K., Hill, G.E., Cupp, E.W., Novak, R.J., Unnasch, T.R. (2010). Developing GIS-based east-ern equine encephalitis vector-host models in Tuskegee, Alabama. International Journal of Health Geographics, 9: 12. doi:10.1186/1476-072X-9-12
52. Jeganathan, C., Khan, S.A., Chandra, R., Singh, H., Srivastava, V., Raju, P.L.N. (2001). Characterisation of malaria vector habitats using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 29(1): 31-36.
53. Kalluri, S., Gilruth, P., Rogers, D., Szczur, M. (2007). Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathogens, 3(10): e116. doi:0.1371/journal.ppat.0030116
54. Kenu, E., Ganu, V., Calys-Tagoe, B.N.L., Yiran, G.A.B., Lartey, M., Adanu, R. (2014). Application of geographical information sys-tem (GIS) technology in the control of Buruli ulcer in Ghana. BMC Public Health, 14: 724. doi:10.1186/1471-2458-14-724
55. Khormi, H.M., Kumar, L. (2011a). Identifying and visualizing spatial patterns and hot spots of clinically-confirmed dengue fever cases and female Aedes aegypti mosquitoes in Jeddah, Saudi Arabia. Dengue Bulletin, 35: 15-34.
56. Khormi, H.M., Kumar, L. (2011b). Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Science of the Total Environment, 409: 4713-4719.
57. Khormi, H.M., Kumar, L. (2012). Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geo-graphical information system environment. Geospatial Health, 6: 171-176.
58. Kolivras, K.N. (2006). Mosquito habitat and dengue risk potential in Hawaii: a conceptual framework and GIS application. The Professional Geographer, 58(2): 139-154.
59. Kopp, S., Shuchman, R., Strecher, V., Gueye, M., Ledlow, J., Philip, T., Grodzinski, A. (2002). Chapter 3: public health applications. Telemedicine Journal and e-Health, 8: 35-48.
60. Lambert, R.C., Kolivras, K.N., Resler, L.M., Brewster, C.C., Paulson, S.L. (2008). The potential for emergence of Chagas disease in the United States. Geospatial Health, 2(2): 227-239.
61. Lawson, A.B., Böhning, D., Biggeri, A., Lesaffre, E., Viel, J.F. (1999). Disease mapping and its uses. In: Lawson, A., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J.F., Bertollini, R. (Eds.), Disease Mapping and Risk Assessment for Public Health. Chichester, UK: Wiley; 3-13.
62. Legendre, P. (1993). Spatial autocorrelation-trouble or new para-digm? Ecology, 74: 1659-1673.
63. Leighton, P.A., Koffi, J.K., Pelcat, Y., Lindsay, L.R., Ogden, N.H. (2012). Predicting the speed of tick invasion: an empirical model of range expansion for the Lyme disease vector Ixodes scapularis in Canada. Journal of Applied Ecology, 49: 457-464.
64. Marechal, F., Ribeiro, N., Lafaye, M., Güell, A. (2008). Satellite imag-ing and vector-borne diseases: the approach of the French National Space Agency (CNES). Geospatial Health, 3(1): 1-5.
65. Martin, C., Curtis, B., Fraser, C., Sharp, B. (2002). The use of a GIS-based malaria information system for malaria research and control in South Africa. Health and Place, 8: 227-236.
66. Mitchell, A. (2012). The Esri Guide to GIS Analysis. Redlands, CA: Esri Press.
67. Mollié, A. (1999). Bayesian and empirical Bayes approaches to disease mapping. In: Lawson, A., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J.F., Bertollini, R. (Eds.), Disease Mapping and Risk Assessment for Public Health. Chichester, UK: Wiley; 15-29.
68. Moran, P.A.P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37: 17-23.
69. Mortarino, M., Musella, V., Costa, V., Genchi, C., Cringoli, G., Rinaldi, L. (2008). GIS modeling for canine dirofilariosis risk assessment in central Italy. Geospatial Health, 2(2): 253-261.
70. Nmor, J.C., Sunahara, T., Goto, K., Futami, K., Sonye, G., Akweywa, P., Dida, G., Minakawa, N. (2013). Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers. Parasites and Vectors, 6: 14. doi:10.1186/1756-3305-6-14
71. Palaniyandi, M. (2014). GIS for disease surveillance and health information management in India. Geospatial Today, 13(5): 44-46.
72. Palaniyandi, M., Mariappan, T. (2013) Containing the vector borne diseases, (GIS for master plan for mosquito control in the met-ropolitan cities in India). Journal of Geospatial Today, 12(8): 28-30.
73. Pathirana, S., Kawabata, M., Goonetilake, R. (2009). Study of poten-tial risk of dengue disease outbreak in Sri Lanka using GIS and statistical modelling. Journal of Rural and Tropical Public Health, 8: 8-17.
74. Porcasi, X., Rotela, C.H., Introini, M.V., Frutos, N., Lanfri, S., Peralta, G., De Elia, E.A., Lanfri, M.A., Scavuzzo, C.M. (2012). An opera-tive dengue risk stratification system in Argentina based on geospatial technology. Geospatial Health, 6(3): S31-S42.
75. Ra, P.K., Nathawat, M.S., Onagh, M. (2012). Application of multiple linear regression model through GIS and remote sensing for malaria mapping in Varanasi District, India. Health Science Journal, 6(4): 731-749.
76. Racloz, V., Ramsey, R., Tong, S., Hu, W. (2012). Surveillance of den-gue fever virus: a review of epidemiological models and early warning systems. PLoS Neglected Tropical Diseases, 6(5): e1648. doi:10.1371/journal.pntd.0001648
77. Robinson, T., Rogers, D.J., Williams, B. (1997a). Mapping tsetse hab-itat suitability in the common fly belt of southern Africa using multivariate analysis of climate and remotely sensed vegetation data. Medical and Veterinary Entomology, 11: 235-245.
78. Robinson, T.P., Rogers, D.J., Williams, B. (1997b). Univariate analy-sis of tsetse habitat in the common fly belt of southern Africa using climate and remotely sensed vegetation data. Medical and Veterinary Entomology, 11: 223-234.
79. Rogers, D.J., Hay, S.I., Packer, M.J. (1996). Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology, 90: 225-241.
80. Rogers, D.J., Randolph, S.E. (1993). Distribution of tsetse and ticks in Africa: past, present, and future. Parasitology Today, 9: 266-271.
81. Rogers, D.J., Williams, B.G. (1993). Monitoring trypanosomiasis in space and time. Parasitology, 106: 77-92.
82. Rogers, D.J., Williams, B.G. (1994). Tsetse distribution in Africa: seeing the wood and the trees. In: Edwards, P.J., May, R.M., Webb, N. (Eds.), Large-Scale Ecology and Conservation Biology. Oxford, UK: Blackwell Scientific; 247-271.
83. Rossi, E., Rinaldi, L., Musella, V., Veneziano, V., Carbone, S., Gradoni, L., Cringoli, G., Maroli, M. (2007). Mapping the main Leishmania phlebotomine vector in the endemic focus of the Mt Vesuvius in southern Italy. Geospatial Health, 2: 191-198.
84. Rytkönen, M. (2004). Not all maps are equal: GIS and spatial analy-sis in epidemiology. International Journal of Circumpolar Health, 63: 9-24.
85. Sabel, C.E., Löytönen, M. (2004). Clustering of disease. In: Maheswaran, R., Craglia, M. (Eds.), GIS in Public Health Practice. London: CRC Press; 51-67.
86. Schröder, W., Schmidt, G., Bast, H., Pesch, R. Kiel, E. (2007). Pilot-study on GIS-based risk modelling of a climate warming induced tertian malaria outbreak in Lower Saxony (Germany). Environmental Monitoring and Assessment, 133: 483-493.
87. Sipe, N.G., Dale, P. (2003) Challenges in using geographic infor-mation systems (GIS) to understand and control malaria in Indonesia. Malaria Journal, 2: 36. doi:10.1186/1475-2875-2-36
88. Symeonakis, E., Robinson, T., Drake, N. (2007). GIS and multiple-criteria evaluation for the optimisation of tsetse fly eradication programmes. Environmental Monitoring and Assessment, 124: 89-103.
89. Tack, W., Madder, M., Baeten, L., Vanhellemont, M., Gruwez, R., Verheyen, K. (2012). Local habitat and landscape affect Ixodes ricinus tick abundances in forests on poor, sandy soils. Forest Ecology Management, 265: 30-36.
90. Tatem, A.J., Huang, Z., Das, A., Qi, Q., Roth, J., Qiu, Y., (2012). Air travel and vector-borne disease movement. Parasitology, 139(14): 1816-1830. doi:10.1017/S0031182012000352
91. Thomas, R. (1990). Geomedical Systems: Intervention and Control. New York: Routledge; 314.
92. Valiakos, G., Papaspyropoulos, K., Giannakopoulos, A., Birtsas, P., Tsiodras, S., Hutchings, M.R., Spyrou, V., Pervanidou, D., Athanasiou, L.V., Papadopoulos, N., Tsokana, C., Baka, A., Manolakou, K., Chatzopoulos, D., Artois, M., Yon, L., Hannant, D., Petrovska, L., Hadjichristodoulou, C., Billinis, C. (2014). Use of wild bird surveillance, human case data and GIS spa-tial analysis for predicting spatial distribution of West Nile Virus in Greece. Plos One, 9(5): e96935. doi:10.1371/journal.pone.0096935
93. Vescio, F.M., Busani, L., Mughini-Gras, L., Khoury, C., Avellis, L., Taseva, E., Rezza, G., Christova, I. (2012). Environmental correlates of Crimean-Congo haemorrhagic fever inci-dence in Bulgaria. BMC Public Health. 12: 1116. doi:10.1186/1471-2458-12-1116
94. Vignolles, C., Lacaux, J.-P., Tourre, Y.M., Bigeard, G., Ndione, J.-A., Lafaye, M. (2009). Rift Valley fever in a zone potentially occu-pied by Aedes vexans in Senegal: dynamics and risk mapping. Geospatial Health, 3(2): 211-220.
95. Wimberly, M., Baer, A., Yabsley, M. (2008). Enhanced spatial mod-els for predicting the geographic distributions of tick-borne pathogens. International Journal of Health Geographics, 7: 15. doi:10.1186/1476-072X-7-15
96. Winters, A.M., Eisen, R.J., Delorey, M.J., Fischer, M., Nasci, R.S., Zielinski-Gutierrez, E., Moore, C.G., Pape, W.J., Eisen, L. (2010). Spatial risk assessments based on vector-disease epide-miologic data: importance of scale for West Nile virus disease in Colorado. American Journal of Tropical Medicine and Hygiene, 82: 945-953.
97. World Health Organisation (WHO). (2014). Policy Brief: IVM-The Power of Integrated Health and Environment Action. Geneva, Switzerland: WHO.
98. Yang, G.-J., Gao, Q., Zhou, S.-S., Malone, J.B., McCarroll, J.C., Tanner, M., Vounatsou, P., Bergquist, R., Utzinger, J., Zhou, X.-N. (2010). Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models. Geospatial Health, 5(1): 11-22.
99. Young, S.G. Tullis, J.A. Cothren, J. (2013). A remote sensing and GIS assisted landscape epidemiology approach to West Nile virus. Applied Geography. 45: 241-249.
100. Zou, L., Miller, S.N., Schmidtmann, E.T. (2007). A GIS tool to esti-mate West Nile Virus risk based on a degree-day model. Environmental Monitoring and Assessment, 129: 413-420.
101. Achu, D.F. (2008). Application of GIS in Temporal and Spatial Analyses of Dengue Fever Outbreak: Case of Rio de Janeiro, Brazil. Master’s thesis, Linköpings University.
102. Barrera, R., Delgado, N., Jimenez, M., Villalobos, I., Romero, I. (2000). Stratification of a hyperendemic city in hemorrhagic dengue. Revista Panamericana de Salud Publica, 8(4): 225-233.
103. Bogh, C., Lindsay, S.W., Clarke, S.E., Dean, A., Jawara, M., Pinder, M., Thomas, C.J. (2007). High spatial resolution mapping of malaria transmission risk in the Gambia, West Africa, using Landsat TM satellite imagery. American Journal of Tropical Medicine and Hygiene, 76(5): 875-881.
104. Capinha, C., Gomes, E., Reis, E., Rocha, J., Sousa, C. A., do Rosario, V.E., Almeida, A.P. (2009). Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal. Geospatial Health, 3(2): 177-187.
105. Chaikoolvatana, A., Singhasivanon, P., Haddawy, P. (2007). Utilization of a geographical information system for surveil-lance of Aedes aegypti and dengue haemorrhagic fever in north-eastern Thailand. Dengue Bulletin, 31: 75-82.
106. Chansang, C., Kittayapong, P. (2007). Application of mosquito sam-pling count and geospatial methods to improve dengue vec-tor surveillance. American Journal of Tropical Medicine and Hygiene, 77: 897-902.
107. Eisen, L., Lozano-Fuentes, S. (2009). Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Neglected Tropical Diseases, 3(4). doi:10.1371/journal.pntd.0000411
108. Guisan, A., Thuiller, W. (2005). Predicting species distribution: offer-ing more than simple habitat models. Ecology Letters, 10: 19.
109. Hakre, S., Masuoka, P., Vanzie, E., Roberts, D.R. (2004). Spatial cor-relations of mapped malaria rates with environmental factors in Belize, Central America. International Journal of Health Geographics, 3(6). doi:10.1186/1476-072X-3-6
110. Hay, S.I., Lennon, J.J. (1999). Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of cli-mate. Tropical Medicine and International Health, 4(1): 58-71.
111. Hay, S.I., Omumbo, J.A., Craig, M.H., Snow, R.W. (2000). Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Advances in Parasitology, 47: 173-215.
112. Hu, H., Singhasivanon, P., Salazar, N.P., Thimasarn, K., Li, X., Wu, Y., … Looarecsuwan, S. (1998). Factors influencing malaria ende-micity in Yunnan Province, PR China (analysis of spatial pat-tern by GIS). Geographical Information System. Southeast Asian Journal of Tropical Medicine and Public Health, 29(2): 191-200.
113. Indaratna, K., Hutubessy, R., Chupraphawan, S., Sukapurana, C., Tao, J., Chunsutthiwat, S., … Crissman, L. (1998). Application of geographical information systems to co-analysis of disease and economic resources: dengue and malaria in Thailand. Southeast Asian Journal of Tropical Medicine and Public Health, 29: 669-684.
114. Khormi, H., Kumar, L. (2011). Examples of using spatial informa-tion technologies for mapping and modelling mosquito-borne diseases based on environmental, climatic, socio-economic factors and different spatial statistics, temporal risk indices and spatial analysis: a review. Journal of Food, Agriculture and Environment, 9(2), 41-49.
115. Khormi, H.M., Kumar, L. (2012). Assessing dengue fever risk based on socioeconomic and environmental variables in a GIS envi-ronment. Geospatial Health, 6(2), 171-176.
116. MARA. (1998). Towards an Atlas of Malaria Risk in Africa. First Technical Report of the MARA Collaboration. Durban, South Africa: MARA.
117. Morrison, A.C., Getis, A., Santiago, M., Rigau-Perez, J.G., Reiter, P. (1998). Exploratory space-time analysis of reported dengue cases during an outbreak in Florida, Puerto Rico, 1991-1992. American Journal of Tropical Medicine and Hygiene, 58(3), 287-298.
118. Nakhapakorn, K., Tripathi, N.K. (2005). An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International Journal of Health Geographic, 4: 13-15.
119. Napier, M. (2001). Application of GIS and modeling of dengue risk areas in the Hawaiian islands. Retrieved from http://www.pdc.org/PDCNewsWebArticles/2003ISRSE/ISRSE_Napier_TS49.3.pdf
120. Omumbo, J., Ouma, J., Rapuoda, B., Craig, M.H., le Sueur, D., Snow, R.W. (1998). Mapping malaria transmission intensity using geographical information systems (GIS): an example from Kenya. Annals of Tropical Medicine and Parasitology, 92(1), 7-21.
121. Pratt, M. (2003). Down-to-earth approach jumpstarts GIS for dengue outbreak. Magazine for Esri Software Users, 6: 2.
122. Robinson, A. (1982). Early Thematic Mapping in the History of Cartography. Chicago: University of Chicago Press.
123. Rochlin, I., Iwanejko, T., Dempsey, M. E., Ninivaggi, D. V. (2009). Geostatistical evaluation of integrated marsh management impact on mosquito vectors using before-after-control-impact (BACI) design. International Journal of Health Geographics, 8: 1-20. doi:10.1186/1476-072x-8-35
124. Rogers, D.J., Randolph, S.E., Snow, R.W., Hay, S.I. (2002). Satellite imagery in the study and forecast of malaria. Nature, 415(6872): 710-715.
125. Schafer, M.L., Lundstrom, J.O. (2009). The present distribution and predicted geographic expansion of the floodwater mosquito Aedes sticticus in Sweden. Journal of Vector Ecology, 34(1): 141-147. doi:10.1111/j.1948-7134.2009.00017.x
126. Shirayama, Y., Phompida, S., Shibuya, K. (2009). Geographic infor-mation system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province, Laos. Malaria Journal, 8: 217.
127. Siqueira, J.B., Maciel, I.J., Barcellos, C., Souza, W.V., Carvalho, M.S., Nascimento, N.E., … Martelli, C.M.T. (2008). Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health, 8. doi:10.1186/1471-2458-8-361
128. Snow, R., Gouws, E., Omumbo, J., Rapuoda, B., Craig, M., Tanser, F., Sueur, D., Ouma, J. (1998). Models to predict the intensity of Plasmodium falciparum transmission: applications to the burden of disease in Kenya. Transactions, 92: 601-606.
129. Srivastava, A., Nagpal, B.N., Joshi, P.L., Paliwal, J.C., Dash, A.P. 2009. Identification of malaria hot spots for focused intervention in tribal state of India: a GIS based approach. International Journal of Health Geographics, 8: 30.
130. Strickman, D., Kittayapong, P. (2002). Dengue and its vectors in Thailand: introduction to the study and seasonal distribution of Aedes larvae. American Journal of Tropical Medicine and Hygiene, 67: 247-259.
131. Tan, A., Song, R. (2000). The use of GIS in ovitrap monitoring for dengue control in Singapore. Dengue Bulletin, 24: 110-116.
132. Tan, B.T. (2001). New initiatives in dengue control in Singapore. Dengue Bulletin, 25: 1-6.
133. Takumi, K., Scholte, E.J., Braks, M., Reusken, C., Avenell, D., Medlock, J.M. (2009). Introduction, scenarios for establishment and seasonal activity of Aedes albopictus in the Netherlands. Vector-Borne and Zoonotic Diseases, 9(2): 191-196. doi:10.1089/vbz.2008.0038
134. Tran, A., Deparis, X., Dussart, P., Morvan, J., Rabarison, P., Remy, F., … Gardon, J. (2004). Dengue spatial and temporal patterns, French Guiana, 2001. Emerging Infectious Diseases, 10(4): 615-621.
135. World Health Organisation (WHO). (2004). Roll Back Malaria. Retrieved from http://who.int/int-fs/en
136. World Health Organisation (WHO). (2002). Dengue and dengue haemorrhagic fever. Retrieved from http://www.who.int/media-centre/factsheets/fs117/en/index.html
137. Achee, N.L., Grieco, J.P., Masuoka, P., Andre, R.G., Roberts, D.R., Thomas, J., Briceno, I., King, R., Rejmankova, E. (2006). Use of remote sensing and geographic information systems to predict locations of Anopheles darlingi-positive breeding sites within the Sibun River in Belize Central America. Journal of Medical Entomology, 43: 382-392.
138. Altibase. (2014). Altibase Application Development Spatial SQL Reference Release 6.3.1. Seoul, Korea: Altibase Corporation.
139. Anyamba, A., Linthicum, K.J., Tucker, C.J. (2001). Climate-disease connections: Rift Valley fever in Kenya. Cadernos de Saude Publica, 17: 133-140.
140. Aronoff, S. (1989). Geographic Information Systems: A Management Perspective. Ottawa, Canada: WDL.
141. Barnes, C.M., Cibula, C.G. (1979). Some implications of remote sensing technology in insect control programs including mos-quitoes. Mosquito News, 39: 271-282.
142. Beale, L., Abellan, J.J., Hodgson, S., Jarup, L. (2008). Methodologic issues and approaches to spatial epidemiology. Environmental Health Perspectives, 116, 1105-1110.
143. Beck, L.R., Rodriguez, M.H., Dister, S.W., Rodriguez, A.D., Rejmankova, E., Ulloa, A., Meza, R.A., Roberts, D.R., Paris, J.F., Spanner, M.A., Washino, R.K., Hacker, C., Legters, L.J. (1994). Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. American Journal of Tropical Medicine and Hygiene, 51: 271-280.
144. Bernhardsen, T. (2002). Geographic Information Systems: An Introduction. Arendal, Norway: Wiley.
145. Blan, L., Butler, R. (1999). Comparing effects of aggregation meth-ods on statistical and spatial properties of simulated spatial data. American Society for Photogrammetry and Remote Sensing, 65, 73-84.
146. Buckley, D.J. (1997). The GIS Primer: An Introduction to GIS. Fort Collins, CO: Pacific Meridian Resource.
147. Burrough, P.A. (1986). Principles of Geographic Information Systems for Land Resource Assessment. Oxford, UK: Oxford University Press.
148. Chang, K.-T. (2002). Introduction to Geographic Information Systems. Boston: McGraw-Hill.
149. Chrisman, N.R. (1997). Exploring Geographic Information Systems. New York: Wiley.
150. Cross, E.R., Newcomb, W.W., Tucker, C.J. (1996). Use of weather data and remote sensing to predict the seasonal distribution of Phlebotomus papatasi in southwestern Asia. American Journal of Tropical Medicine and Hygiene, 54: 530-536.
151. Cui, T. (2001). Characteristic of spatial data and the design of data model. Paper presented at the 20th International Cartographic Conference, Beijing, China, August 6-10.
152. Davis, B.E. (2001). GIS: A Visual Approach. Albany, NY: Onword Press Thomson Learning.
153. Dent, B.D. (1999). Cartography: Thematic Map Design. New York: McGraw-Hill.
154. Glass, G.E., Schwartz, B.S., Morgan, J.M., III, Johnson, D.T., Noy, P.M., Israel, E. (1995). Environmental risk factors for Lyme dis-ease identified with geographic information systems. American Journal of Public Health, 85: 944-948.
155. Green, R.M., Hay, S.I. (2002). The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications. Remote Sensing of Environment, 79: 166-175.
156. Hay, S.I., Omumbo, J.A., Kraig, M.H., Snow, R.W. (2000). Earth observation geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Advances in Parasitology, 47: 174-206.
157. Heywood, I., Cornelius, S., Carver, S. (2002). An Introduction to Geographical Information Systems. Harlow, UK: Pearson Education.
158. Hugh-Jones, M. (1991a). Landsat-TM identification of the habitats of the cattle tick Amblyomma variegatum in Guadeloupe French Windward Islands. Preventive Veterinary Medicine, 11: 355-356.
159. Hugh-Jones, M. (1991b). The remote sensing of tick habitats. Journal of Agricultural Entomology, 8: 309-315.
160. Johnson, V. (2011). I object to your projections. In: Herrman, J., Buchanan, M. (Eds.), The Awl. http://www.theawl.com/2011/09/i-object-to-your-projections
161. Jeganathan, C., Khan, S.A., Chandra, R., Singh, H., Srivastava, V., Raju, P.L.N. (2001). Characterisation of malaria vector habitats using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 29(1): 31-36.
162. Kalluri, S., Gilruth, P., Rogers, D., Szczur, M. (2007). Surveillance of arthropod vector-borne infectious diseases using remote sens-ing techniques: a review. PLoS Pathogens, 3(10): e116.
163. Kemp, K. (2008). Encyclopedia of Geographic Information Science. Thousand Oaks, CA: Sage.
164. Khormi, H.M., Kumar, L. (2011). Identifying and visualizing spatial patterns and hot spots of clinically-confirmed dengue fever cases and female Aedes aegypti mosquitoes in Jeddah, Saudi Arabia. Dengue Bulletin, 35: 15-34.
165. Khormi, H.M., Kumar, L. (2012). Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geo-graphical information system environment. Geospatial Health, 6: 171-176.
166. Leighton, P.A., Koffi, J.K., Pelcat, Y., Lindsay, L.R., Ogden, N.H. (2012). Predicting the speed of tick invasion: an empirical model of range expansion for the Lyme disease vector Ixodes scapularis in Canada. Journal of Applied Ecology, 49: 457-464.
167. Linthicum, K.J., Anyamba, A., Tucker, C.J., Kelley, P.W., Myers, M.F., Peters, C.J. (1999). Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science, 285: 397-4000.
168. Linthicum, K.J., Bailey, C.L., Tucker, C.J., Angleberger, D.R., Cannon, T., Logan, T.M., Gibbs, P.H., Nickeson, J. (1991). Towards real-time prediction of Rift-Valley fever epidemics in Africa. Preventive Veterinary Medicine, 11: 325-334.
169. Longley, P.A., Batty, M. (1996). Spatial Analysis: Modelling in a GIS Environment. New York: Wiley.
170. Marechal, F., Ribeiro, N., Lafaye, M., Güell, A. (2008). Satellite imag-ing and vector-borne diseases: the approach of the French National Space Agency (CNES). Geospatial Health, 3(1): 1-5.
171. McDougall, K., Rajabifard, A., Williamson, I.P. (2007). A mixed method approach for evaluating spatial data sharing partner-ships for Spatial Data Infrastructure development. In Onsrud, H. (Ed.), Research and Theory in Advancing Spatial Data Infrastructure Concepts. Redlands, CA: ESRI Press.
172. Montgomery, G.E., Schuch, H.C. (1993). GIS Data Conversion Handbook. Fort Collins, CO: GIS World and UGC Consulting.
173. Nmor, J.C., Sunahara, T., Goto, K., Futami, K., Sonye, G., Akweywa, P., Dida, G. Minakawa, N. (2013). Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers. Parasites and Vectors, 6: 14. doi:10.1186/1756-3305-6-14
174. Onsrud, H.J., Rushton, G. (1995). Sharing geographic information: an introduction. In: Onsrud, H.J., Rushton, G. (Eds.), Sharing Geographic Information. New Brunswick, NJ: Centre for Urban Policy Research.
175. Palaniyandi, M. (2014). GIS for disease surveillance and health infor-mation management in India. Geospatial Today, 13(5): 44-46.
176. Pope, K.O., Sheffner, E.J., Linthicum, K.J., Bailey, C.L., Logan, T.M., Kasischke, E.S., Birney, K., Njogu, A.R., Roberts, C.R. (1992). Identification of central Kenyan Rift Valley fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar. Remote Sensing of Environment, 40: 185-196.
177. Ra, P.K., Nathawat, M.S., Onagh, M. (2012). Application of multiple linear regression model through GIS and remote sensing for malaria mapping in Varanasi District, India. Health Science Journal, 6(4): 731-749.
178. Randolph, S.E. (1994). Population dynamics and density-dependent seasonal mortality indices of the tick Rhipicephalus appendicu-latus in eastern and southern Africa. Medical and Veterinary Entomology, 8: 351-368.
179. Randolph, S.E. (2000). Ticks and tick-borne disease systems in space and from space. Advances in Parasitology, 47: 217-240.
180. Randolph, S.E., Rogers, D.J. (2006). Tick-bone disease systems: mapping geographic and phylogenetic space. Advances in Parasitology, 62: 263-291.
181. Rao, A.S. (2012). What do you mean by GIS aggregation? http://www.publishyourarticles.net/knowledge-hub/geography/what-do-you-mean-by-gis-aggregation.html
182. Rejmankova, E., Savage, H.M., Rodriguez, M.H., Roberts, D.R., Rejmanek, M. (1992). Aquatic vegetation as a basis for classifi-cation of Anopheles albimanus Wiedemann (Diptera: Culcidae) larval habitats. Environmental Entomology, 21: 598-603.
183. Roberts, D.R., Paris, J.F., Manguin, S., Harbach, R.E., Woodruff, R., Rejmankova, E., Polanco, J., Wullschleger, B., Legters, L.J. (1996). Predictions of malaria vector distribution in Belize based on multispectral satellite data. American Journal of Tropical Medicine and Hygiene, 57: 304-308.
184. Robinson, T., Rogers, D.J., Williams, B. (1997a). Mapping tsetse hab-itat suitability in the common fly belt of southern Africa using multivariate analysis of climate and remotely sensed vegetation data. Medical and Veterinary Entomology, 11: 235-245.
185. Robinson, T.P., Rogers, D.J., Williams, B. (1997b). Univariate analy-sis of tsetse habitat in the common fly belt of southern Africa using climate and remotely sensed vegetation data. Medical and Veterinary Entomology, 11: 223-234.
186. Rogers, D. (1979). Tsetse population dynamics and distribution: a new analytical approach. Journal of Animal Ecology, 48: 825-849.
187. Rogers, D.J. (2000). Satellites space time and the African trypanoso-miasis. Advances in Parasitology, 47: 129-171.
188. Rogers, D.J., Hay, S.I., Packer, M.J. (1996). Predicting the distri-bution of tsetse flies in West Africa using Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology, 90: 225-241.
189. Rogers, D.J., Myers, M.F., Tucker, C.J., Smith, P.F., White, D.J., et al. (2002a). Predicting the distribution of West Nile fever in North America using satellite sensor data. Photogrammetric Engineering and Remote Sensing, 68: 112-114.
190. Rogers, D.L., Randolph, S.E., Snow, R.W., Hay, S.I. (2002b). Satellite imagery in the study and forecast of malaria. Nature. 415: 710-715.
191. Rogers, D.J., Wilson, A.J., Hay, S.I., Graham, A.J. (2006). The global distribution of yellow fever and dengue. Advances in Parasitology, 62: 181-220.
192. Sahu, K.C. (2007). Textbook of Remote Sensing and Geographical Information Systems. New Delhi, India: Atlantic.
193. Thomson, M.C., Connor, S.J., Milligan, P.J.M., Flasse, S.P. (1996). The ecology of malaria seen by earth-observation satellites. Annals of Tropical Medicine and Parasitology, 90: 243-264.
194. Thomson, M.C., Obsomer, V., Dunne, M., Connor, S.J., Molyneux, D.H. (2000). Satellite mapping of Loa loa prevalence in rela-tion to ivermectin use in west and central Africa. Lancet, 356: 1077-1078.
195. Wagner, V.E., Hill-Rowley, R., Newson, H.D. (1979). Remote sensing: a rapid and accurate method of data acquisition for a newly formed mosquito control district. Mosquito News, 39: 271-282.
196. Williamson, I.P., Rajabifard, A., Feeney, M.-E.F. (2003). Developing Spatial Data Infrastructures: From Concept to Reality. London: Taylor and Francis.
197. Wood, B.L., Beck, L.R., Washino, R.K., Palchick, S.M., Sebesta, D. (1991a). Spectral and spatial characterization of rice field mosquito habitat. International Journal of Remote Sensing, 12: 621-626.
198. Wood, B., Washino, R., Beck, L., Hibbard, K., Pitcairn, M., Roberts, D., Rejmankova, E., Paris, J., Hacker, C., Salute, J., Sebesta, P., Legters, L. (1991b). Distinguishing high and low Anopheline-producing rice fields using remote sensing and GIS technolo-gies. Preventive Veterinary Medicine, 11: 277-288.
199. Young, S.G., Tullis, J.A., Cothren, J. (2013). A remote sensing and GIS assisted landscape epidemiology approach to West Nile virus. Applied Geography, 45: 241-249.
200. Zhang, J., Atkinson, P., Goodchild, M.F. (2014). Scale in Spatial Information and Analysis. Boca Raton, FL: Taylor & Francis.
201. Baker, R.D. (1996). Testing for space-time clusters of unknown size. Journal of Applied Statistics, 23: 543-554.
202. Bartlett, M.S. (1947). Multivariate analysis. Journal of the Royal Statistical Society Series B, 9: 176-197.
203. Bithell, J.F. (1999). Disease Mapping Using the Relative Risk Function Estimated from Areal Data. Disease Mapping and Risk Assessment for Public Health. New York: Wiley; 247-255.
204. Boots, B., Getis, A. (1988). Point Pattern Analysis. Sage University Paper Series on Quantitative Applications in the Social Sciences, series no. 07-001. Thousand Oaks, CA: Sage.
205. Diggle, P.J., Chetwynd, A.G., Haggkvist, R., Morris, S.E. (1995). Second order analysis of space-time clustering. Statistical Methods in Medical Research, 4: 124-136.
206. Dixon, P.M. (2006). Ripley’s K function. In: El-Shaarawi, A.-H., Piegorsch, W.W. (Eds.), Encyclopedia of Environmetrics. Vol. 3. Chichester, UK: Wiley; 1796-1803.
207. Earnest A., Chen M.I., Ng D., Sin L.Y. (2005). Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS out-break in a tertiary hospital in Singapore. BMC Health Service Research, 5: 36.
208. Getis, A., Ord, J.K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(19): 189-206.
209. Giesecke, J. (2001). Modern Infectious Disease Epidemiology. London: Arnold; 280.
210. Jacquez, G. (1996). A K nearest neighbor test for space-time interac-tion. Statistics in Medicine, 15: 1935-1949.
211. Khormi, H.M., Kumar, L. (2011a). Examples of using spatial informa-tion technologies for mapping and modelling mosquito-borne diseases based on environmental, climatic, socio-economic factors and different spatial statistics, temporal risk indices and spatial analysis: a review. Journal of Food, Agriculture and Environment, 9(2): 41-49.
212. Khormi, H.M., Kumar, L. (2011b). Identifying and vizualising spatial patterns and hotspots of dengue fever: clinically-confirmed cases and female Aedes aegypti mosquitoes in Jeddah, Saudi Arabia. Dengue Bulletin, 35: 15-34.
213. Khormi, H., Kumar, L., Elzahrany, R. (2011). Modeling spatio-temporal risk changes in dengue fever incidence in Saudi Arabia: a GIS case study. Geospatial Health, 6: 77-84.
214. Kitron, U., Kazmierczak, J.J. (1997). Spatial analysis of the dis-tribution of Lyme disease. Wisconsin American Journal of Epidemiology, 145(6): 558-566.
215. Knox, G., Bartlett, M.S. (1964). The detection of space-time interac-tions. Applied Statistics, 13, 25-29.
216. Kulldorff, M., Hjalmars, U. (1999). The Knox method and other tests for space-time interaction. Biometrics, 55: 544-552.
217. Lagrotta, M., Fernandes, T., da Costa Silva, W., and Souza-Santos, R. (2008). Identification of key areas for Aedes aegypti control through geoprocessing in Nova Iguaçu, Rio de Janeiro State, Brazil. Cadernos de Saúde Pública, 24(1): 70-80.
218. Lay, J.G., Lin, Z.H., Yap, K.H., Wu, P.C., Su, H.J. (2006). Temperature variability and spatial hotspots of dengue fever occurrence in Taiwan. Epidemiology, 17: S485-S485.
219. Mitchell A. (2005). The ESRI Guide to GIS Analysis. ESRI, Press 2.
220. Nakhapakorn, K., Jirakajohnkool, S. (2006). Temporal and spatial autocorrelation statistics of dengue fever. Dengue Bulletin, 30: 177-183.
221. Pfeiffer, D., Robinson, T., Stevenson, M., Stevens, K., Rogers, D., Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford, UK: Oxford University Press.
222. Ripley, B.D. (1976). The second order analysis of stationary point processes. Journal of Applied Probability, 13: 255-266.
223. Ripley, B.D. (1977). Modelling spatial patterns (with discussion). Journal of Royal Society Series B 39: 172-212.
224. Sharma, K.D., Mahabir, R.S., Curtin, K.M., Sutherland, J.M., Agard, J.B., Chadee, D.D. (2014). Exploratory space-time analysis of dengue incidence in Trinidad: a retrospective study using travel hubs as dispersal points, 1998-2004. Parasites and Vectors 7: 341.
225. Songchitruksa, P., and Zeng, X. (2010). Getis-Ord spatial statistics to identify hot spots by using incident management data. Transportation Research Record: Journal of the Transportation Research Board, 2165(1): 42-51.
226. Tipayamongkholgul, M., and Lisakulruk, S. (2011). Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis. Geospatial Health, 5(2): 191-198.
227. Upton, G., Fingleton, B. (1985). Spatial Data Analysis by Example (Point Pattern and Quantitative Data). New York: Wiley.
228. Wen, T.H., Lin, N.H., Lin, C.H., King, C.C., Su, M.D. (2006). Spatial mapping of temporal risk characteristics to improve environ-mental health risk identification: a case study of a dengue epidemic in Taiwan. Science of the Total Environment, 367: 631-640.
229. Wu, P.C., Lay, J.G., Guo, H.R., Lin, C.Y., Lung, S.C., Su, H.J. (2009). Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. Science of the Total Environment, 407: 2224-2233.
230. Zeger, S.L., Irizarry, R., Peng, R.D. (2006). On time series analysis of public health and biomedical data. Annual Review of Public Health, 27: 57-79.
231. Bithell, J.F. (2000). A classification of disease mapping methods. Statistics in Medicine, 19(17-18): 2203-2215.
232. Berke, O., von Keyserlingk, M., Broll, S., Kreienbrock. L. 2002. Zum Vorkommen von Echinococcus multilocularis bei Rotfüchsen in Niedersachsen: Identifikation eines Hochrisikogebietes mit Methoden der räumlichen epidemiologischen Clusteranalyse. Berl Munch Tierarztl Wochenschr. 115:428-434.
233. Cumming, G.S. (2002). Comparing climate and vegetation as limiting factors for species ranges of African ticks. Ecology, 82(1): 255-268.
234. Diggle, P.J. (2000). Overview of statistical methods for disease map-ping and its relationship to cluster detection. In: Elliott, P. et al. (Eds.), Spatial Epidemiology: Methods and Applications, Oxford, UK: Oxford University Press, 87-103.
235. Dormann, C.F. (2007). Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16, 129-138.
236. Duchateau, L., Kruska, R.L., Perry, R.D. (1997). Reducing a spatial database to its effective dimensionality for logistic-regression analysis of incidence of livestock disease. Preventive Veterinary Medicine, 32(34): 207-218.
237. Elliot, P., Wakefield, J.C., Best, N.G., Briggs, D.J. (Eds) (2000). Spatial Epidemiology: Methods and Applications. Oxford, UK: Oxford University Press.
238. Hales, S., de Wet, N., Maindonald, J., Woodward, A. (2002). Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet, 360(9336): 830-834.
239. Holdridge, L.R. (1971). Forest Environments in Tropical Life Zones: A Pilot Study. New York: Pergamon Press.
240. Kissling, W.D., Gudrun, C. (2007). Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography. doi:10.1111/j.1466-8238.2007.00334.x
241. Lawson, A.B. (2001) Tutorial in biostatistics: disease map recon-struction. Statistics in Medicine, 20: 2183-2203.
242. Lindsay, S.W., Martens, P. (1998). Malaria in the African highlands: past, present and future. Bulletin of the World Health Organisation, 76: 33-45.
243. Lucey, B.T., Russell, C.A., Smith, D., Wilson, M.L., Long, A., Waller, L.A., et al. (2002). Spatiotemporal analysis of epizootic raccoon rabies, propagation in Connecticut, 1991-1995. Vector Borne Zoonotic Diseases, 2: 77-86.
244. Maidana, N.A., Yang, H.M. (2009). Spatial spreading of West Nile Virus described by traveling waves. Journal of Theoretical Biology, 258: 403-417. doi:10.1016/j.jtbi.2008.12.032
245. Martens, P. (1998). Health and Climate Change: Modelling the Impacts of Global Warming and Ozone Depletion. London: Earthscan.
246. Martens, W.J., Niessen, L.W., Rottmans, J., Jetten, T.H., McMichael, A.J. (1995). Potential impact of global climate change on malaria risk. Environmental Health Perspectives, 103(5): 458-464.
247. Mevik, B.-H., and Cederkvist, H.R. (2004). Mean squared error of prediction (MSEP) estimates for principal component regres-sion (PCR) and partial least squares regression (PLSR). Journal of Chemometrics, 18(9): 422-429.
248. Moore, D.A. (1999). Spatial diffusion of raccoon rabies in Pennsylvania, USA. Preventive Veterinary Medicine, 40: 19-32. doi:10.1016/S0167-5877(99)00005-7
249. Moore, D.A., Carpenter, T.E. (1999). Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiology Review, 21: 143-161.
250. Patz, J.A., Epstein, P.R., et al. (1996). Global climate change and emerging infectious diseases. JAMA, 275(3): 217-223.
251. Reeves, W.C., Hardy, J.L., et al. (1994). Potential effect of global warming on mosquito-borne arboviruses. Journal of Medical Entomology, 31(3): 323-332.
252. Robinson, T., Rogers, D., et al. (1997). Mapping tsetse habitat suit-ability in the common fly belt of southern Africa using multi-variate analysis of climate and remotely sensed vegetation data. Medical and Veterinary Entomology, 11(3): 235-245.
253. Rogers, D.J., Hay, S.I., et al. (1996). Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology, 90(3): 225-241.
254. Shope, R. (1991). Global climate change and infectious diseases. Environmental Health Perspectives, 96: 171-174.
255. Tanser, F.C., Sharp, B., et al. (2003). Potential effect of climate change on malaria transmission in Africa. Lancet, 362(9398): 1792-1798.
256. Unwin, D.J. (1975). An Introduction to Trend Surface Analysis. Concepts and Techniques in Modern Geography. Norwich, UK: Geo Abstracts.
257. Voltz, M., Webster, R. (1990). A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Journal of Soil Science, 41(3): 473-490.
258. Aburas, H.M. (2007). ABURAS Index: A statistically developed index for dengue-transmitting vector population prediction. Proceedings of World Academy of Science, Engineering and Technology, 23:151-154.
259. Beck, L.R., Lobitz, B.M., Wood, B.L. (2000). Remote sensing and human health: new sensors and new opportunities. Emerging Infectious Diseases, 6(3): 217-227.
260. Boots, B. (2003). Geographically weighted regression: the analy-sis of spatially varying relationships. International Journal of Geographical Information Science, 17(7): 717-719.
261. Cromley, E.K., and McLafferty, S.L. (2012). GIS and Public Health. 2nd ed. New York: Guilford Publications.
262. Day, J.F., and Curtis, G.A. (1989). Influence of rainfall on Culex nigripalpus (Diptera, Culicidae) blood-feeding behaviour in Indian River County, Florida. Annals of the Entomological Society of America, 82(1): 32-37.
263. Dye, C., Reiter, P. (2000). Climate chance and malaria-temperatures without fevers? Science, 289(5485): 1697-1698.
264. Epstein, P.R. (2000). Is global warming harmful to health? Scientific American, 283(2): 50-57.
265. Friis, R.H., Sellers, T.A. (2009). Epidemiology for Public Health Practice. 4th ed. Sudbury, MA: Jones and Bartlett.
266. Githeko, A.K., Lindsay, S.W., Confalonieri, U.E., Patz, J.A. (2000). Climate change and vector-borne diseases: a regional analysis. Bulletin of the World Health Organization, 78(9): 1136-1147.
267. Hakre, S., Masuoka, P., Vanzie, E., Roberts, D.R. (2004). Spatial cor-relations of mapped malaria rates with environmental factors in Belize, Central America. International Journal of Health Geographics, 3(6). doi:10.1186/1476-072X-3-6
268. Hales, S., de Wet, N., Maindonald, J., Woodward, A. (2002). Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet, 360(9336): 830-834.
269. Khormi, H., Kumar, L. (2011a). Examples of using spatial informa-tion technologies for mapping and modelling mosquito-borne diseases based on environmental, climatic, socio-economic factors and different spatial statistics, temporal risk indices and spatial analysis: a review. Journal of Food, Agriculture and Environment, 9(2): 41-49.
270. Khormi, H., Kumar, L. (2011b). Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Science of the Total Environment, 409(22): 4713-4719.
271. Khormi, H.M., Kumar, L., Elzahrany, R.A. (2013). Regression model for predicting adult female Aedes aegypti based on meteorological variables: a case study of Jeddah, Saudi Arabia. Journal of Earth Science Climate Change, 5: 168. doi:10.4172/2157-7617.1000168
272. Koopman, J.S., Prevots, D.R., Vaca Marin, M.A., Gomez Dantes, H., Zarate Aquino, M.L., Longini, I.M. Jr., Sepulveda Amor, J. (1991). Determinants and predictors of dengue infection in Mexico. American Journal of Epidemiology, 3(11): 1168-1178.
273. Lindsay, S.W., Birley, M.H. (1996). Climate change and malaria trans-mission. Annals of Tropical Medicine and Parasitology, 90(6): 573-588.
274. Molyneux, D.H. (1997). Patterns of change in vector-borne diseases. Annals of Tropical Medicine and Parasitology, 91(7): 827-839.
275. Monath, T.P., Tsai, T.P. (1987). St-Louis encephalitis-lessons from the last decade. American Journal of Tropical Medicine and Hygiene, 37(3): 40-59.
276. Moore, C.G. (1985). Predicting Aedes aegypti abundance from cli-matologic data. In: Lounibos, L.P., Rey, J.R., Frank, J.H. (Eds.), Ecology of Mosquitoes: Proceedings of a Workshop. Vero Beach, FL: Vero Beach Medical Enomology Laboratory; 223-235.
277. Morrison, A.C., Zielinski-Gutierrez, E., Scott, T.W., Rosenberg, R. (2008). Defining challenges and proposing solutions for con-trol of the virus vector Aedes aegypti. PLoS Medicine, 5(3): e68. doi:10.1371/journal.pmed.0050068
278. Nakaya, T., Fotheringham, A.S., Brunsdon, C., Charlton, M. (2005). Geographically weighted Poisson regression for disease asso-ciation mapping. Statistics in Medicine, 24(17): 2695-2717. doi:10.1002/sim.2129
279. Openshaw, S., Taylor, P. (1979). A Million or So Correlation Coefficients: Three Experiments on the Modifiable of a Real Unit Problem. London: Pion.
280. O’Sullivan, D. (2003). Geographically weighted regression: the anal-ysis of spatially varying relationships. Geographical Analysis, 35(3): 272-275.
281. Rakotomanana, F., Ratovonjato, J., Randremanana, R.V., et al. (2010). Geographical and environmental approaches to urban malaria in Antananarivo (Madagascar). BMC Infectious Diseases, 10: 173.
282. Scott, T.W., Morrison, A.C. (2003). Aedes aegypti Density and the Risk of Dengue-Virus Transmission. Davis: Department of Entomology, University of California, Davis.
283. Sithiprasasna, R., Ugsang, D.M., Honda, K., Jones, J.W., Singhasivanon, P. (2005). IKONOS-derived malaria transmission risk in north-western Thailand. Southeast Asian Journal of Tropical Medicine and Public Health, 36(1): 14-22.
284. Spurna, P. (2008). Geographically weighted regression: method for analysing spatial non-stationarity of geographical phenomenon. Geografie, 113(2): 125-139.
285. Takken, W., Knols, B.G.J. (Eds.). (2007). Emerging Pests and Vector-Borne Diseases in Europe: Ecology and Control of Vector-Diseases. Wageningen, the Netherlands: Wageningen Academic.
286. Troyo, A. (2007). Analysis of Dengue Fever and Aedes aegypti (Diptera: Culicidae) Larval Habitats in a Tropical Urban Environment of Costa Rica Using Geospatial and Mosquito Surveillance Technologies. PhD dissertation, University of Miami.
287. Wegbreit, J. (1997). The Possible Effects of Temperature and Precipitation on Dengue Morbidity in Trinidad and Tobago: A Retrospective Longitudinal Study. PhD dissertation, University of Michigan, Ann Arbor.
288. Andrewartha, H.G., Birch, L.C. (1954). The Distribution and Abundance of Animals. Chicago: University of Chicago Press.
289. Bliss, A.R., Gill, J.M. (1933). The effects of freezing on the larvae of Aedes aegypti. American Journal of Tropical Medicine and Hygiene, 13: 583-588.
290. Center for International Earth Science Information Network (CIESIN). Retrieved from http://www.ciesin.org/docs/001-613/map15.gif (accessed July 2012).
291. Centers for Disease Control and Prevention (CDC). (2012). DengueMap. Retrieved from http://www.healthmap.org/den-gue/index.php (accessed July 2012).
292. Chakravarti, A., Kumaria, R. (2005). Eco-epidemiological analysis of dengue infection during an outbreak of dengue fever, India. Virology Journal, 2(32): 1-7.
293. Chejara, V.K., Kriticos, D.J., Kristiansen, P., Sindel, B.M., Whalley, R.D.B., Nadolny, C. (2010). The current and future potential geographical distribution of Hyparrhenia hirta. Weed Research, 50, 174-184.
294. Chiew, F.H.S., Kirono, D.G.C., et al. (2009). Assessment of rainfall simulations from global climate models and implications for climate change impact on runoff studies. 18th Annual World IMACS/MODSIM Congress, Ciarns, Australia, July 13-17. Available at http://mssanz.org.au/modsim09
295. Christophers, S.R. (1960). Aedes aegypti (L.): The Yellow Fever Mosquito. Its Life History, Bionomics and Structure. Cambridge, UK: Cambridge University Press.
296. Clark, D.V., Mammen, M.P., et al. (2005). Economic impact of den-gue fever/dengue hemorrhagic fever in Thailand at the family and population levels. American Journal of Tropical Medicine and Hygiene, 72(6): 786-791.
297. Connor, M.E. (1924). Suggestions for developing a campaign to control yellow fever. American Journal of Tropical Medicine, 4: 277-307.
298. Corwin, A.L., Larasati, R.P., Bangs, M.J., et al. (2001). Epidemic den-gue transmission in southern Sumatra, Indonesia. Transactions of the Royal Society of Tropical Medicine and Hygiene, 95: 257-265.
299. Gordon, H.B., Rotstayn, L.D., McGregor, J.L., Dix, M.R., Kowalczyk, E.A., O’Farrell, S.P., Waterman, L.J., Hirst, A.C., Wilson, S.G., Collier, M.A., Watterson, I.G., Elliott, T.I. (2002). The CSIRO Mk3 Climate System Model. CSIRO Atmospheric Research technical paper No. 60. Aspendale, Australia: CSIRO Atmospheric Research.
300. Graham, R.R., Juffrie, M., Tan, R., Hayes, C.G., Laksono, I., Ma’roef, C., et al. (1999). A prospective seroepidemiologic study on dengue in children four to nine years of age in Yogyakarta, Indonesia I. Studies in 1995-1996. American Journal of Tropical Medicine and Hygiene, 61(3): 412-419.
301. Gratz, N.G. (2004). Critical review of the vector status of Aedes albopictus. Medical and Veterinary Entomology, 18: 215-227.
302. Gubler, D.J. (1997). Dengue and dengue hemorrhagic fever: its history and resurgence as a global public health prob-lem. In: Gubler, D.J., Kuno, G. (Eds.), Dengue and Dengue Hemorrhagic Fever. Wallingford, UK: CAB International; 1-22.
303. Gubler, D.J. (1998a). Dengue and dengue hemorrhagic fever. Clinical Microbiology Review, 11: 480-496.
304. Gubler, D.J. (1998b). Resurgent vector-borne diseases as a global health problem. Emerging Infectious Diseases, 4: 442-450.
305. Gubler, D.J. (2002). The global emergence/resurgence of arboviral diseases as public health problems. Archives of Medical Research, 33: 330-342.
306. Gubler, D.J. (2003). Aedes albopictus in Africa. Lancet, 3: 751-752.
307. Gubler, D.J. (2004). The changing epidemiology of yellow fever and dengue 1900 to 2003: full circle? Comparative Immunology, Microbiology and Infectious Diseases, 27: 319-330.
308. Gubler, D.J., Clark, G.G. (1995). Dengue/dengue hemorrhagic fever: the emergence of a global health problem. Emerging Infectious Diseases, 1: 55-57.
309. Gubler, D.J., Kuno, G. (1997). Dengue and Dengue Hemorrhagic Fever. Wallingford, UK: CAB International.
310. Gubler, D.J., Trent, D.W. (1994). Emergence of epidemic dengue/dengue hemorrhagic fever as a public health problem in the Americas. Infectious Agents and Disease, 2: 383-393.
311. Haines, A., Kovats, R.S., Campbell-Lendrum, D., Corvalan, C. 2006. Climate change and human health: impacts, vulnerability, and public health. Public Health, 120: 585-596.
312. Hales, S., de Wet, N., Maindonald, J., Woodward, A. (2002). Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet, 360(9336): 830-834.
313. Halstead, S.B. (2006). Dengue in the Americas and Southeast Asia: do they differ? Revista Panamerican de Salud Publica, 6: 407-415.
314. Herrera-Basto, E., Prevots, D.R., Zarate, M.L., Silva, J.L., Sepulveda-Amore, J. (1992). First reported outbreak of classical den-gue fever at 1700 meters above sea level in Guerrero State, Mexico, June 1988. American Journal of Tropical Medicine and Hygiene, 46: 649-635.
315. Hopp, M.J., Foley, J.A. (2001). Global-scale relationships between climate and the dengue fever vector, Aedes aegypti. Climate Change 48: 441-463.
316. Intergovernmental Panel on Climate Change (IPCC). (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.
317. Jetten, T.H., Focks, D.A. (1997). Potential changes in the distribu-tion of dengue transmission under climate warming. American Journal of Tropical Medicine and Hygiene, 57: 285-297.
318. Johansson, M.A., Dominici, F., Glass, G.E, (2009). Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Neglected Tropical Diseases, 3: e382. doi:10.1371/journal.pntd.0000382
319. Khormi, M.H., Kumar, L. (2011). Examples of using spatial informa-tion technologies for mapping and modelling mosquito-borne diseases based on environmental, climatic, socioeconomic factors and different spatial statistics, temporal risk indices and spatial analysis: a review. Journal of Food, Agriculture and Environment, 9: 41-49.
320. Khormi, H.M., Kumar, L., Elzahrany, R. (2011). Describing and ana-lyzing the association between meteorological variables and adult Aedes aegypti mosquitoes. Journal of Food, Agriculture and Environment, 9: 954-959.
321. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., Scott, J. (2011). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution, 53-64.
322. Lee, H.L, Rohani, A. (2005). Transovarial transmission of dengue virus in Aedes aegypti and Aedes albopictus in relation to den-gue outbreak in an urban area in Malaysia. Dengue Bulletin, 29: 106-111.
323. Li, C.F., Lim, T.W., Han, L.L., Fang, R. (1985) Rainfall, abundance of Aedes aegypti and dengue infection in Selangor, Malaysia. Southeast Asian Journal of Tropical Medicine and Public Health, 16(4): 560-568.
324. Lifson, A.R. (1996). Mosquitoes, models, and dengue. Lancet, 347: 1201-1202.
325. Lounibos, LP. (2002). Invasions by insect vectors of human disease. Annual Review of Entomology, 47: 233-266.
326. Macfie, J.W.S. (1920). Heat and Stegomyia fasciata, short expo-sures to raised temperatures. Annals of Tropical Medicine and Parasitology, 14: 73-82.
327. Malavige, G.N., Fernando, S., Fernando, D.J., Seneviratne, S.L. (2004). Dengue viral infections. Postgraduate Medical Journal, 80: 588-601.
328. Martens, W.J.M., Jetten, T.H., Rotmans, J., Niessen, L.W. (1995). Climate change and vector-borne diseases. Global Environmental Change, 5: 195-209.
329. Medlock, J.M., Snow, K.R., Leach, S. (2005). Potential transmission of West Nile virus in the British Isles: an ecological review of candidate mosquito bridge vectors. Medical and Veterinary Entomology, 19: 2-21.
330. Moore, C.G. (1999). Aedes albopictus in the United States: cur-rent status and prospects for further spread. Journal of the American Mosquito Control Association, 15: 221-227.
331. Moore, C.G., Mitchell, C.J. (1997). Aedes albopictus in the United States: ten-year presence and public health implications. Emerging Infectious Diseases, 3: 329-334.
332. Ooi, E.E., Goh, K.T., Gubler, D.J. (2006) Dengue prevention and 35 years of vector control in Singapore. Emerging Infectious Diseases, 12: 887-893. doi:10.3201/10.3201/eid1206.051210
333. Patz, J.A., Martens, W.J.M., Focks, D.A., Jetten, T.H. (1998). Dengue fever epidemic potential as projected by general circula-tion models of global climate change. Environmental Health Perspectives, 106: 147-153.
334. Poutsma, J., Loomans, A.J.M., Aukema, B., Heijerman, T. (2008). Predicting the potential geographical distribution of the harle-quin ladybird, Harmonia axyridis, using the CLIMEX model. BioControl, 53: 103-125.
335. Rahmstorf, S., Cazenave, A., Church, J.A., Hansen, J.E., Keeling, R.F., Parker, D.E., Somerville, R.C.J. (2007). Recent climate observa-tions compared to projections. Science, 316(5825): 709.
336. Reiter, P. (1988). Weather, vector biology, and arboviral recrudes-cence. In: Monath, T.P. (Ed.), The Arboviruses: Epidemiology and Ecology. Vol. 1. Boca Raton, FL: CRC Press; 245-255.
337. Reiter, P., Gubler, D.J. (1997). Surveillance and control of urban dengue vectors. In: Gubler, D.J., Kuno, G. (Eds.), Dengue and Dengue Hemorrhagic Fever. Wallingford, UK: CAB International; 425-462.
338. Rogers, D.J., Wilson, A.J., Hay, S.I., Graham, A.J. (2006). The global distribution of yellow fever and dengue. Advances in Parasitology, 62: 181-220. doi:10.1016/S0065-308X(05)62006-4
339. Rudnick, A. (1986). Dengue fever epidemiology in Malaysia 1901-1980. In: Rudnick, A., Lim, T.W. (Eds.), Dengue Fever Studies in Malaysia. Bulletin No. 23. Kuala Lumpur, Malaysia: Institute of Medical Research; 9-38.
340. Siler, J.F., Hall, M.W., Hitchens, A.P. (1926). Dengue: its history, epidemiology, mechanisms of transmission, etiology, clinical manifestations, immunity and prevention. Philippine Journal of Science, 29: 1-304.
341. Skae, F.M.T. (1902). Dengue fever in Penang. British Medical Journal, 2: 1581-1582.
342. Smith, C.E.G. (1956). A localized outbreak of dengue fever in Kuala Lumpur: epidemiological and clinical aspects. Medical Journal of Malaya, 10: 289-303.
343. Suarez, M.F., Nelson, M.J. (1981). Registro de Altitud del Aedes aegypti en Colombia [Records of the altitude of Aedes aegypti in Colombia]. Biomedica, 1: 225.
344. Sumarmo, W.H., Jahja, E., Gubler, D.J., Suharyono, W., Sorensen, K. (1983). Clinical observations on virologically confirmed fatal dengue infections in Jakarta, Indonesia. Bulletin of the World Health Organisation, 61: 693-701.
345. Suppiah, R., Hennessy, K.J., Whetton, P.H., McInnes, K., Macadam, I., Bathols, J., Ricketts, J., Page, C.M. (2007). Australian climate change projections derived from simulations performed for the IPCC 4th assessment report. Australian Meteorological Magazine, 56: 131-152.
346. Sutherst, R.W., Bourne, A.S. (2009). Modelling non-equilibrium distributions of invasive species: a tale of two modelling para-digms. Biological Invasions, 11: 1231-1237.
347. Sutherst, R.W., Maywald, G., Kriticos, D.J. (2007). CLIMEX Version 3: User’s Guide. South Yarra, Australia: Hearne Scientific.
348. Takken, W., Knols, B.G.J. (Eds.). (2007). Emerging Pests and Vector-Borne Diseases in Europe: Ecology and Control of Vector-Diseases. Wageningen, the Netherlands: Wageningen Academic.
349. Taylor, S., Kumar, L., Reid, N. (2012a). Impacts of climate change and land-use on the potential distribution of an invasive weed: a case study of Lantana camara in Australia. Weed Research, 52(5): 391-401.
350. Taylor, S., Kumar, L., Reid, N., Kriticos, D.J. (2012b). Climate change and the potential distribution of an invasive shrub, Lantana camara L. PLoS One, 7: e35565. doi:10.1371/journal.pone.0035565
351. United Nations (UN). (2004). World Population to 2300. New York: United Nations.
352. Wallis, R.C. (2005). A GIS Model for Predicting Potential “High Risk” Areas of West Nile Virus by Identifying Ideal Mosquito Breeding Habitats. MSc thesis, Mississippi State University.
353. Wayne, A.R., Graham, C.L. (1968). The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. Journal of Insect Physiology, 14: 1251-1257.
354. Wharton, T.N., Kriticos, D.J. (2004). The fundamental and realized niche of the Monterey Pine aphid, Essigella californica (Essig) (Hemiptera: Aphididae): implications for managing soft-wood plantations in Australia. Diversity and Distributions, 10: 253-262.
355. Booman, M., Durrheim, D.N., La Grange, K., Martin, C., Mabuza, A.M., Zitha, A., Mbokazi, F.M., Fraser, C., Sharp, B.L. (2000). Using a geographical information system to plan a malaria con-trol programme in South Africa. Bulletin of the World Health Organization, 78: 1438-1444.
356. Chang, A.Y., Parrales, M.E., Jimenez, J., Sobieszczyk, M.E., Hammer, S.M., Copenhaver D.J., Kulkarni, R.P. (2009). Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries. International Journal of Health Geographics, 8: 49. doi:10.1186/1476-072X-8-49
357. Haile, D.G. (1989) Computer simulation of the effects of changes in weather patterns on vector-borne disease transmission. In: Smith, J.B., Tirpak, D.A. (Eds.), The Potential Effects of Global Climate Change in the United States. Document 230-05-89-057, Appendix G. Washington, DC: US Environmental Protection Agency.
358. Martin, C., Curtis, B., Fraser, C., Sharp, B. (2002). The use of a GIS-based malaria information system for malaria research and control in South Africa. Health and Place, 8: 227-236.
359. Patz, J.A., Githeko, A.K., McCarty, J.P., Hussein, S., Confalonieri, U., de Wet, N. (2003) Climate change and infectious diseases. In: McMichael, A.J., Campbell-Lendrum, D.H., Corvalan, C.F., Ebi, K.L., Githeko, A., Scheraga, J.D., Woodward, A. (Eds.), Climate Change and Human Health: Risks and Responses. Geneva, Switzerland: World Health Organization; 103-132.
360. Rogers, D.J., Randolph, S.E. (1993). Distribution of tsetse and ticks in Africa: past, present, and future. Parasitology Today, 9: 266-271.
361. Sutherst, R.W. (1998) Implications of global change and climate variability for vector-borne diseases: generic approaches to impact assessments. International Journal for Parasitology, 28: 935-945.