Advertisement

Introduction to Geoinformatics in Public Health

  • Gouri Sankar Bhunia
  • Pravat Kumar Shit
Chapter

Abstract

Medical geography or health geography is a branch of human geography that focuses on the terrestrial aspect in the study of health prominence and the banquet of diseases. Additionally, it provides an idea of the location of individual health as well as its geographical distribution and its association with environmental factors. The concept of medical geography was first introduced by Hippocrates (5th–4th Century BCE). Today’s public health information is an embryonic field which emphasizes on the solicitation of information science and technology to public health rehearsal and investigation. The examination of public health data usually comprises the concepts and tools of epidemiology. With growing interest in “Medical Geography”, the epidemiological method, assumed in the field of geography of disease relied increasingly on the statistical modeling of the geographical dissemination of diseases and their distribution in time and space. Earth observation satellite allows us to quantify physical, chemical and biological factors when considering the association between climate and vector borne diseases, the succeeding associations among the distribution and life cycle of the vector, outbreaks of the disease. Conversely, the application of geographic information systems (GIS) to public health exercise has prodigious prospective for enlightening our understanding of the ecology and reasons of complex health problems, and for managing the policy and appraisal of effective population based programs and policies.

References

  1. Achu DF (2008) Application of gis in temporal and spatial analyses of dengue fever outbreak: case of Rio De Janeiro, Brazil. Linköpings Universitet Linköping, Sweden. Available at: https://www.diva-portal.org/smash/get/diva2:210116/FULLTEXT01.pdf
  2. Ali M, Emch M, Donnay JP, Yunus M, Sack RB (2002) Identifying environmental risk factors for endemic cholera: a raster GIS approach. Health & Place 8(3):201–210CrossRefGoogle Scholar
  3. Ali M, Rasool S, Park JK, Saeed S, Ochiai R, Nizami Q, Acosta CJ, Bhutta Z (2004) Use of satellite imagery in constructing a household GIS database for health studies in Karachi, Pakistan. Int J Health Geogr 3(1):20CrossRefGoogle Scholar
  4. Anno S, Takagi M, Tsuda Y, Yotopranoto S, Dachlan YP, Bendryman SS et al (2000) Analysis of relationship between Anopheles subpictus larval densities and environmental parameters using Remote Sensing (RS), Global Positioning Systems (GPS) and a Geographic Information System (GIS). Kobe J Med Sci 46:231–243Google Scholar
  5. Aparício C, Dantas-Bittencourt M (2003) Análise especial da leishmaniose tegumentar americana. In: Anais do XI Simpósio Brasileiro de Sensoriamento Remoto; Abr 5–10; Minas Gerais, Brasil. Belo Horizonte: Instituto Brasileiro de Pesquisas EspaciaisGoogle Scholar
  6. Beck LR, Lobitz BM, Wood BL (2000) Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 6(3):217–227CrossRefGoogle Scholar
  7. Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Washino RK, Roberts DR (1997) Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg 56:99–106CrossRefGoogle Scholar
  8. Bhunia GS, Chatterjee N, Kumar V, Siddiqui NA, Mandal R, Das P (2012a) Delimitation of Kala-azar risk areas in the district of Vaishali in Bihar (India) using a geo-environmental approach. Memórias do Instituto Oswaldo Cruz 107(5):609–620CrossRefGoogle Scholar
  9. Bhunia GS, Kesari S, Chatterjee N, Kumar V, Das P (2012b) Localization of Kala-azar in the endemic region of Bihar, India based on land use/land cover assessment at different scales. Geospatial Health 6(2):177–193CrossRefGoogle Scholar
  10. Bhunia GS, Kesari S, Chatterjee N, Kumar V, Das P (2012c) Seasonal relationship between normalized difference vegetation index and abundance of the Kala-azar vector in an endemic focus in Bihar, India. Geospatial Health 7(1):51–62CrossRefGoogle Scholar
  11. Bhunia GS, Kesari S, Chatterjee N, Kumar V, Das P (2013) The burden of visceral leishmaniasis in India: challenges in using remote sensing and GIS to understand and control. ISRN Infect Dis 1–14CrossRefGoogle Scholar
  12. Bhunia GS, Kesari S, Chatterjee N, Pal DK, Kumar V, Ranjan A, Das P (2011) Incidence of visceral leishmaniasis in the Vaishali district of Bihar, India: spatial patterns and role of inland water bodies. Geospatial Health 5(2):205–215CrossRefGoogle Scholar
  13. Bhunia GS, Kesari S, Jeyaram A, Kumar V, Das P (2010a) Influence of topography on the endemicity of Kala-azar: a study based on remote sensing and geographical information system. Geospatial Health 4(2):155–165CrossRefGoogle Scholar
  14. Bhunia GS, Kumar V, Kumar AJ, Das P, Kesari S (2010b) The use of remote sensing in the identification of the eco-environmental factors associated with the risk of human visceral leishmaniasis (Kala-azar) on the Gangetic plain, in north-eastern India. Ann Trop Med Parasitol 104(1):35–53CrossRefGoogle Scholar
  15. Bill R (1999) Grundlagen der Geo-Informationssysteme, 4th ed. Band 1 (Hardware, Software und Daten). 2. Aufl. Wichmann Verlag, Heidelberg pp 454Google Scholar
  16. Booman M, Durrheim DN, La Grange K, Martin C, Mabuza AM, Zitha A, Mbokazi FM, Fraser C, Sharp BL (2000) Using a geographical information system to plan a malaria control programme in South Africa. Bull World Health Organ 78(12):1438–1444Google Scholar
  17. Brooker S (2002) Schistosomes, snails, and satellites. Acta Trop 82:207–214CrossRefGoogle Scholar
  18. Brooker S, Clarke S, Njagi JK, Polack S, Mugo B, Estambale B, Muchiri E, Magnussen P, Cox J (2004) Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health 9(7):757–766CrossRefGoogle Scholar
  19. Brooker S, Rowlands M, Haller L, Savioli L, Bundy DAP (2000) Towards an atlas of human helminth infection in sub-Saharan Africa: the use of geographic information systems (GIS). Parasitol Today 16(7):303–307CrossRefGoogle Scholar
  20. Bunnell JE, Price SD, Das A, Shields TM, Glass GE (2003) Geographic information systems and spatial analysis of adult Ixodes scapularis (Acari: Ixodidae) in the Middle Atlantic Region of the USA. J Med Entomol 40:570–576CrossRefGoogle Scholar
  21. Carabin H, Escalona M, Marshall C, Vivas-Martinez SV, Botto C, Joseph L, Basáñez M-G (2003) Prediction of community prevalenceof human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach. Bull World Health Organ 81:482–490Google Scholar
  22. Chen D, Wong H, Belanger P, Moore K, Peterson M, Cunningham J (2015) Analyzing the correlation between deer habitat and the component of the risk for lyme disease in Eastern Ontario, Canada: a GIS-based approach. ISPRS Int J Geo-Inf 4:105–123CrossRefGoogle Scholar
  23. Clarke K, McLafferty S, Tempalski B (1996) On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis 2:85–92CrossRefGoogle Scholar
  24. Clements ACA, Lwambo NJS, Blair L, Nyandindi U, Kaatano G, Kinung’hi S, Webster JP, Fenwick A, Brooker S (2006) Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania. Trop Med Int Health 11(4):490–503CrossRefGoogle Scholar
  25. Connor SJ, Thompson MC, Flasse SP, Perryman AH (1998) Environmental information systems in Malaria: risk mapping and epidemic forecasting. Disasters 22:39–56CrossRefGoogle Scholar
  26. Courtin F, Jamonneau V, Oké E, Coulibaly B, Oswald Y, Dupont S, Cuny G, Doumenge J-P, Solano P (2005) Towards understanding the presence/absence of human African trypanosomosis in a focus of Côte d’Ivoire: a spatial analysis of the pathogenic system. Int J Health Geogr 4(1):27CrossRefGoogle Scholar
  27. Craig W, Tepfer M, Degrassi G, Ripandelli D (2008) An overview of general features of risk assessments of genetically modified crops. Euphytica 164:853–880. https://doi.org/10.1007/s10681-007-9643-8CrossRefGoogle Scholar
  28. Cross ER, Newcomb WW, Tucker CJ (1996) Use of weather data and remote sensing to predict the geographic and seasonal distribution of Phlebotomus papatasi in Southwest Asia. Am J Trop Med Hyg 54:330–332CrossRefGoogle Scholar
  29. Daash A, Srivastava A, Nagpal BN, Saxena R, Gupta SK (2009) Geographical information system (GIS) in decision support to control malaria—a case study of Koraput district in Orissa, India. J Vector Borne Dis 46(1):72–74Google Scholar
  30. Dale PER, Morris CD (1996) Culex annulirostris breeding sites in urban areas: using remote sensing and digital image analysis to develop a rapid predictor of potential breeding areas. J Am Mosq Control Assoc 12:316–320Google Scholar
  31. De La Rocque S, Michael JF, De Wispelaere G, Cuisance D (2001) New tools for the study of animal trypanosomiasis in the Sudan: model-building of dangerous epidemiological passage by remote sensing geographic information systems. Parasite 8:171–195CrossRefGoogle Scholar
  32. Dister SW, Fish D, Bros SM, Frank DH, Wood BL (1997) Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg 57:687–692CrossRefGoogle Scholar
  33. Diuk-Wasser MA, Vourc’h G, Cislo P, Hoen AG, Melton F, Hamer SA, Rowland M, Cortinas R, Hickling GJ, Tsao JI (2010) Field and climate-based model for predicting the density of host-seeking nymphal Ixodes scapularis, an important vector of tick-borne disease agents in the eastern United States. Global Ecol Biogeogr 19:504–514Google Scholar
  34. Dwolatzky B, Trengove E, Struthers H, McIntyre J, Martinson N (2006) Linking the global positioning system (GPS) to a personal digital assistant (PDA) to support tuberculosis control in South Africa: a pilot study. Int J Health Geogr 5(1):34CrossRefGoogle Scholar
  35. Eisen L, Eisen RJ (2010) Using Geographic Information Systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. Ann Rev Entomol 56:41–61CrossRefGoogle Scholar
  36. Elnaiem DA, Schorscher J, Bendall A, Obsomer V, Osman ME, Mekkawi AM, Connor SJ, Ashford RW, Thomson MC (2003) Risk mapping of visceral leishmaniasis: the role of local variation in rainfall and altitude on the presence and incidence of Kala-azar in eastern Sudan. Am J Trop Med Hyg 68(1):10–17CrossRefGoogle Scholar
  37. English D (1992) Geographical epidemiology and ecological studies. In: Elliot P, Cuzick J, English D, Stern R (eds) Geographical and environmental epidemiology: methods for small-area studies. Oxford Press, Oxford, pp 3–13Google Scholar
  38. Estrada-Peña A (1998) Geostatistics and remote sensing as predictive tools of tick distribution: a cokriging system to estimate Ixodes scapularis (Acari: Ixodidae) habitat suitability in United States and Canada from advanced very high resolution radiometer satellite imagery. J Med Entomol 35:989–995CrossRefGoogle Scholar
  39. Foody GM (2006) GIS: health applications. Prog Phys Geogr 30(5):691–695CrossRefGoogle Scholar
  40. Glass GE, Cheek JE, Patz JA, Shields TM, Doyle TJ, Thoroughman DA, Hunt DK, Enscore RE, Gage KL, Irland C, Peters CJ, Bryan R (2000) Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerg Infect Dis 6(3):238CrossRefGoogle Scholar
  41. Glass GE, Schwartz BS, Morgan JM, Johnson DT, Noy PM, Israel E (1995) Environmental risk factors for Lyme disease identified with geographic information systems. Am J Public Health 85(7):944–948CrossRefGoogle Scholar
  42. Guerra M, Walker E, Jones C, Paskewitz S, Cortinas MR, Stancil A et al (2002) Predicting the risk of lyme disease: habitat suitability for Ixodes scapularis in the North Central United States. Emerg Infect Dis 8:289–295CrossRefGoogle Scholar
  43. Guo-Jing G, Chen H, Lin D, Hu G, Wu X, Li D et al (2002) A method of rapid identification snail habitat in marshland of Poyang Lake region by remote sensing. Chin J Parasit Dis 15:291–296Google Scholar
  44. Hay SI, Snow RW, Rogers DJ (1998) Predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives. Parasitol Today 14:306–313CrossRefGoogle Scholar
  45. Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ (2006) Global environmental data for mapping infectious disease distribution. Adv Parasitol 62:37–77CrossRefGoogle Scholar
  46. Hendricks G, LaRocque S, Reid R, Wint W (2001) Spatial trypanosomiasis management: from data-layers to decision making. Trends Parasitol 17(1):35–41CrossRefGoogle Scholar
  47. Hendrickx G, Nepala A, Rogers D, Bastiaensen P, Slingenbergh J (1999) Can remotely sensed meteorological data significantly contribute to reduce costs of tsetse surveys? Mem Inst Oswaldo Cruz 94:273–276CrossRefGoogle Scholar
  48. Hennekens CH, Buring JE (1987) Epidemiology in medicine. Lippincott Williams & WilkinsGoogle Scholar
  49. Hollingsworth TD, Pulliam JRC, Funk S, Truscott JE, Isham V, Lloyd AL (2015) Seven challenges for modelling indirect transmission: vector-borne diseases, macroparasites and neglected tropical diseases. Epidemics 10:16–20.  https://doi.org/10.1016/j.epidem.2014.08.007CrossRefGoogle Scholar
  50. Honório NA, Codeço CT, Alves FC, Magalhães MAFM, Lourençode-Oliveira R (2009) Temporal distribution of Aedes aegypti in different districts of Rio de Janeiro, Brazil, measured by two types of traps. J Med Entomol 46:1001–1014CrossRefGoogle Scholar
  51. Hu W, Tong S, Mengersen K, Oldenburg B, Dale P (2005) Spatial and temporal patterns of Ross River virus in Brisbane, Australia. Arbovirus Res Aust 9:128–136Google Scholar
  52. Huang Z, Das A, Qiu Y, Tatem AJ (2012) Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool. Int J Health Geogr 11:33CrossRefGoogle Scholar
  53. Jeganathan C, Khan SA, Chandra R et al (2001) Characterisation of malaria vector habitats using remote sensing and GIS. J Indian Soc Remote Sens 29:31.  https://doi.org/10.1007/BF02989911CrossRefGoogle Scholar
  54. Kabatereine NB, Brooker S, Tukahebwa EM, Kazibwe F, Onapa AW (2004) Epidemiology and geography of Schistosoma mansoni in Uganda: implications for planning control. Tropical Med Int Health 9(3):372–380CrossRefGoogle Scholar
  55. Kalluri S, Gilruth P, Rogers D, Szczur M (2007) Surveillence of arthropod vector borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 3(10):1361–1371CrossRefGoogle Scholar
  56. Kaya S, Pultz TJ, Mbogo CM, Beier JC, Mushinzimana E (2002) The use of radar remote sensing for identifying environmental factors associated with malaria risk in Coastal Kenya. In: International geoscience and remote sensing symposium (IGARSS’02), TorontoGoogle Scholar
  57. Keegan L, Dushoff J (2014) Analytic calculation of finite-population reproductive numbers for direct- and vector-transmitted diseases with homogeneous mixing. Bull Math Biol 76(5):1143–1154.  https://doi.org/10.1007/s11538-014-9950-xCrossRefGoogle Scholar
  58. Kesari S, Bhunia GS, Chatterjee N, Kumar V, Mandal R, Das P (2013) Appraisal of phlebotomus argentipes habitat suitability using a remotely sensed index in the Kala-azar endemic focus of Bihar, India. Memórias do Instituto Oswaldo Cruz 108(2):197–204CrossRefGoogle Scholar
  59. Kesari S, Bhunia GS, Kumar V, Jeyaram A, Ranjan A, Das P (2011) A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology. Memórias do Instituto Oswaldo Cruz 106(5):515–523CrossRefGoogle Scholar
  60. King RJ, Campbell-Lendrum DH, Davies CR (2004) Predicting geographic variation in Cutaneous Leishmaniasis, Colombia. Emerg Infect Dis 10:598–607CrossRefGoogle Scholar
  61. Kirk MD, Pires SM, Black RE, Caipo M, Crump JA, Devleesschauwer B, Döpfer D, Fazil A, Fischer-Walker CL, Hald T, Hall AJ (2015) World Health Organization estimates of the global and regional disease burden of foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis. PLoS Med 12(12):e1001921CrossRefGoogle Scholar
  62. Kiszewski A, Mellinger A, Spielman A, Malaney P, Sachs S (2004) A global index representing the stability of malaria Transmission. Am J Trop Med Hyg 70:486–498CrossRefGoogle Scholar
  63. Kitron U, Kazmierczak JJ (1997) Spatial analysis of the distribution of lyme disease in Wisconsin. Am J Epidemiol 145:558–566CrossRefGoogle Scholar
  64. Kitron U, Otieno LH, Hungerford LL, Odulaja A, Brigham WU, Okello OO et al (1996) Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat Tm satellite imagery and GIS. J Anim Ecol 65:371–380CrossRefGoogle Scholar
  65. Lindsay SW, Thomas CJ (2000) Mapping and estimating the population at risk from lymphatic filariasis in Africa. Trans R Soc Trop Med Hyg 94:37–45CrossRefGoogle Scholar
  66. MacMahon B, Pugh TF (1970) Epidemiology; principles and methods. Little & Brown, BostonGoogle Scholar
  67. Malaviya P, Picado A, Singh SP, Hasker E, Singh RP, Boelaert M, Sundar S (2011) Visceral leishmaniasis in Muzaffarpur district, Bihar, India from 1990 to 2008. PLoS One 6(3): e14751. https://doi.org/10.1371/journal.pone.0014751CrossRefGoogle Scholar
  68. Martin C, Curtis B, Fraser C, Sharp B (2002) The use of GIS-based malaria information system for malaria research and control in South Africa. Health and Place 8(4):227–236CrossRefGoogle Scholar
  69. Masuoka PM, Claborn DM, Andre RG, Nigro J, Gordon SW, Klein TA, Kim H (2003) Use of IKONOS and Landsat for malaria control in the Republic of Korea. US Army Res, Paper, p 336Google Scholar
  70. Miranda C, Massa JL, Marques CA (1996) Occurrence of American Cutaneous Leishmaniasis by remote sensing satellite imagery in an urban area of Southeastern Brazil. Rev Saúde Pública 30:433–437CrossRefGoogle Scholar
  71. Miranda ML, Dolinoy DC (2005) Using GIS-based approaches to support research on neurotoxicants and other children’s environmental health threats. Neurotoxicology 26(2):223–228CrossRefGoogle Scholar
  72. Mohan VR, Naumova EN (2014) Temporal changes in land cover types and the incidence of malaria in Mangalore. India. Int J Biomed Res 5(8):494–498CrossRefGoogle Scholar
  73. Mollalo A, Alimohammadi A, Shahrisvand M, Shirzadi MR, Malek MR (2014) Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran. Asian Pac J Trop Dis 4:176–180CrossRefGoogle Scholar
  74. Muhar A, Dale PER, Thalib L, Arito E (2000) The spatial distribution of Ross River virus infections in Brisbane: significance of residential location and relationships with vegetation types. Environ Health Prev Med 4:184–189CrossRefGoogle Scholar
  75. Muller G, Grebaut P, Gouteux JP (2004) An agent-based model of sleeping sickness: simulation trials of a corest focus in southern Cameroon. CR Biolog 327:1–11CrossRefGoogle Scholar
  76. Nieto P, Malone JB, Bavia ME (2006) Ecological niche modeling for visceral leishmaniasis in the state of Bahia, Brazil, using genetic algorithm for rule-set prediction and growing degree day-water budget analysis. Geospatial Health 1(1):115–126CrossRefGoogle Scholar
  77. Odoi A, Martin SW, Michel P, Holt J, Middleton D, Wilson J (2003) Geographical and temporal distribution of human giardiasis in Ontario, Canada. Int J Health Geogr 2(1):5CrossRefGoogle Scholar
  78. Okorie PN, Marshall JM, Akpa OM, Ademowo OG (2014) Perceptions and recommendations by scientists for a potential release of genetically modified mosquitoes in Nigeria. Malar J 13:154CrossRefGoogle Scholar
  79. Omumbo JA, Hay SJ, Goetz SJ, Snow RW, Rogers DJ (2002) Updating historical maps of malaria transmission intensity in East Africa using remote sensing. Photogram Eng Remote Sens 68:161–166Google Scholar
  80. Palaniyandi M (2014) The red and infrared IRS WiFS satellite data for mapping of malaria and JE vector mosquito breeding habitats. J Geophys Remote Sens 3:126.  https://doi.org/10.4172/2169-0049.1000126CrossRefGoogle Scholar
  81. Palaniyandi M (2012) The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review. J Vector Borne Dis 49(4):197–204Google Scholar
  82. Paul SK, Jeyaram A, Jayaraman V (2006) Application of remote sensing and GIS in identifying and mapping sandfly distribution in endemic and non-endemic Kala-azar foci in Bihar and Jharkhand. In: Proceedings of the 57th AIAA International Astronautical Congress (IAC’06), pp 2372–2387Google Scholar
  83. Pellerin C (2006) United States updates global positioning system technology. http://www.america.gov/st/washfileenglish/2006/February/20060203125928lcnirellep0.5061609.html
  84. Picheral HE (1994) Place, space, and health. Soc Sci Med 39:1589–1590CrossRefGoogle Scholar
  85. Polack S, Brooker S, Kuper H, Mariotti S, Mabey D, Foster A (2005) Mapping the global distribution of trachoma. Bull World Health Organ 83:913–919Google Scholar
  86. Raban MZ, Dandona R, Dandona L (2009) Essential health information available for India in the public domain on the internet. BMC Publ Health 9:208CrossRefGoogle Scholar
  87. Raso G, Matthys B, N’Goran EK, Tanner M, Vounatsou P, Utzinger J (2005) Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d’Ivoire. Parasitology 131:97–108CrossRefGoogle Scholar
  88. Ratmanov P, Mediannikov O, Raoult D (2013) Vector borne diseases in West Africa: geographic distribution and geospatial characteristics. Trans Roy Soc Trop Med Hyg 1–12Google Scholar
  89. Rejmankova E, Pope KO, Roberts DR, Lege MG, Andre R, Greico J et al (1998) Characterization and detection of Anopheles vestitipennis and Anopheles punctimacula (Diptera: Culicidae) larval habitats in Belize with field survey and SPOT satellite imagery. J Vector Ecol 23:74–99Google Scholar
  90. Rincón-Romero ME,  Londoño JE (2009) Mapping malaria risk using environmental and anthropic variables. Rev Bras Epidemiol 12(3):338–354CrossRefGoogle Scholar
  91. Robinson RA, Lawson B, Toms MP, Peck KM, Kirkwood JK, Chantrey J, Clatworthy IR, Evans AD, Hughes LA, Hutchinson OC, John SK, Pennycott TW, Perkins MW, Rowley PS, Simpson VR, Tyler KM, Cunningham AA (2010) Emerging infectious disease leads to rapid population declines of common british birds. PLoS One 5(8):e12215. https://doi.org/10.1371/journal-.pone.0012215
  92. Robinson TP, Rogers D, Williams B (1997) Mapping tsetse habitat suitability in the common fly belt of Southern Africa using multivariate analysis climate and remotely sensed vegetation data. Med Vet Entomol 11:235–245CrossRefGoogle Scholar
  93. Rodriguez AD, Rodriguez MH, Hernandez JE, Dister SW, Beck LR, Rejmankova E et al (1996) Landscape surrounding human settlements and malaria mosquito abundance in Southern Chiapas, Mexico. J Med Entomol 33:39–48CrossRefGoogle Scholar
  94. Rogers DJ (2006) Models for vectors and vector-borne diseases. Adv Parasitol 62:1–35CrossRefGoogle Scholar
  95. Rogers DJ (2000) Satellites, space, time and the African trypanosomiases. Adv Parasitol 47:129–171CrossRefGoogle Scholar
  96. Rogers DJ (1991) Satellite imagery tsetse and trypanosomiasis in Africa. Prev Vet Med 11:201–220CrossRefGoogle Scholar
  97. Rogers DJ, Hay SI, Packer MJ (1996) Predicting the distribution of tsetse flies in West Africa using temporal fourier processed meteorological satellite data. Ann Trop Med Parasitol 90:225–241CrossRefGoogle Scholar
  98. Rogers DJ, Randolph SE, Snow RW, Hay SI (2002) Satellite imagery in the study and forecast of malaria. Nature 415:710–715CrossRefGoogle Scholar
  99. Rutto JJ, Karuga JW (2009) Temporal and spatial epidemiology of sleeping sickness and use of geographical information system (GIS) in Kenya. J Vector Borne Dis 46(1):18–25Google Scholar
  100. Saathoff E, Olsen A, Kvalsvig JD, Appleton CC, Sharp BL, Kleinschmidt I (2005) Ecological covariates of ascaris lubricoides infection in schoolchildren from rural KwaZulu-Natal, South Africa. Trop Med Int Health 10(5):412–422CrossRefGoogle Scholar
  101. Salomon OD, Orellano PW, Quintana MG, Pérez S, Sosa Estani S, Acardi S, Lamfri M (2006) Transmisión de la leishmaniasis tegumentaria en Argentina. Med (B Aires) 66:211–219Google Scholar
  102. Saraiva L, Andrade-Filho JD, Falcão AL, Carvalho DAA, Souza CM, Freitas CM, Lopes CRG, Moreno EC, Melo MN (2011) Phlebotominae fauna (Diptera: Psychodidae) in an urban district of Belo Horizonte, Brazil, endemic for visceral leishmaniasis: Characterization of favored locations as determined by spatial analysis. Acta Tropica 117:137–145CrossRefGoogle Scholar
  103. Shah NH, Gupta J (2013) SEIR model and simulation for vector borne diseases. Appl Math 4:13–17CrossRefGoogle Scholar
  104. Sherchand JB, Obsomer V, Thakur GD, Hommel M (2003) Mapping of lymphatic filariasis in Nepal. Filaria J 2(1):7CrossRefGoogle Scholar
  105. Shimabukuro PHF, da Silva TRR, Fonseca FOR, Baton LA, Galati EAB (2010) Geographical distribution of American cutaneous leishmaniasis and its phlebotomine vectors (Diptera: Psychodidae) in the state of São Paulo, Brazil. Parasites Vectors 3:121CrossRefGoogle Scholar
  106. Shirayama Y, Phompida S, Shibuya K (2009) Geographic information system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province. Laos Malar J 8:217CrossRefGoogle Scholar
  107. Simarro PP, Cecchi G, Paone M, Franco JR, Diarra A, Ruiz JA, Fèvre EM, Courtin F, Mattioli RC, Jannin JG (2010) The Atlas of human African trypanosomiasis: a contribution to global mapping of neglected tropical diseases. Int J Health Geogr 9:57CrossRefGoogle Scholar
  108. Sipe NG, Dale P (2003) Challenges in using geographic information systems (GIS) to understand and control malaria in Indonesia. Malar J 2:36CrossRefGoogle Scholar
  109. Sithiprasasna R, Lee WJ, Ugsang DM, Linthicum KJ (2005) Identification and characterization of larval and adult anopheline mosquito habitats in the Republic of Korea: potential use of remotely sensed data to estimate mosquito distributions. Int J of Health Geogr 4:17CrossRefGoogle Scholar
  110. Strom SR (2002) Charting a course toward global navigation. Retrieved 12 Jul 2008. http://www.aero.org/publications/crosslink/summer2002/01.html
  111. Sudhakar S, Srinivas T, Palit A, Kar SK, Battacharya SK (2006) Mapping of risk prone areas of Kala-azar (Visceral leishmaniasis) in parts of Bihar state, India: an RS and GIS approach. J Vect Borne Dis 43:115–122Google Scholar
  112. Symeonakis E, Robinson T, Drake N (2007) GIS and multiple-criteria evaluation for the optimization of tsetse fly eradication programmes. Environ Monit Assess 124:89–103CrossRefGoogle Scholar
  113. Thompson DF, Malone JB, Harb M, Faris R, Huh OK, Buck AA et al (1996a) Bancroftian filariasis distribution and diurnal temperature differences in the Southern Nile Delta. Emerg Infect Dis 2:234–235CrossRefGoogle Scholar
  114. Thompson MC, Connor SJ, Milligan PJ, Flasse SP (1996b) The ecology of malaria as seen from earth-observation satellites. Ann Trop Med Parasitol 90:243–264CrossRefGoogle Scholar
  115. Thompson MC, Elnaiem DA, Ashford RW, Connor SJ (1999) Towards a kala azar risk map for Sudan: map ping the potential distribution of Phlebotomus orientalis using digital data of environmental variables. Trop Med Int Health 4:105–113CrossRefGoogle Scholar
  116. Tobler WR (1969) Geographical filters and their inverses. Geogr Anal 1(3):234–253CrossRefGoogle Scholar
  117. Townshend JRG, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int J Remote Sens 7:1435–1445CrossRefGoogle Scholar
  118. Tran A, Ponçon N, Toty C, Linard C, Guis H, Ferré JB, Lo Seen D, Roger F, de la Rocque S, Fontenille D, Baldet T (2008) Using remote sensing to map larval and adult populations of Anopheles hyrcanus (Diptera: Culicidae) a potential malaria vector in Southern France. Int J Health Geogr 7(1):9CrossRefGoogle Scholar
  119. Troyo A, Fuller DO, Calderón-Arguedas O, Beier JC (2008) A geographical sampling method for surveys of mosquito larvae in an urban area using high-resolution satellite imagery. J Vector Ecol 33(1):1–7CrossRefGoogle Scholar
  120. Tuthill K (2003) John snow and the broad street pump: on the trail of an epidemic. Cricket 31(3):23–31Google Scholar
  121. Upadhyayula SM, Mutheneni SR, Kadiri MR, Kumaraswamy S, Nagalla B (2012) A cohort study of lymphatic filariasis on socio economic conditions in Andhra Pradesh, India. PLoS One 7(3):e33779. https://doi.org/10.1371/journal.pone.0033779CrossRefGoogle Scholar
  122. Vally H, Peel M, Dowse GK, Cameron S, Codde JP, Hanigan I, Lindsay MDA (2012) Geographic Information Systems used to describe the link between the risk of Ross River virus infection and proximity to the Leschenault estuary, WA. Aust N Z J Public 36(3):229–235CrossRefGoogle Scholar
  123. Werneck GL, Maguire JH (2002) Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil. Cad Saúde Pública 18:633–637CrossRefGoogle Scholar
  124. Wijegunawardana NDAD, Silva Gunawardene YIN, Manamperi A, Senarathne H, Abeyewickreme W (2012) Geographic information system (GIS) mapping of lymphatic filariasis endemic areas of Gampaha district, Sri lanka based on epidemiological and entomological screening, Southeast Asian. J Trop Med Public Health 43(3):557–566Google Scholar
  125. World Health Organization (WHO) (1999) Geographical information systems (GIS). Wkly Epidemiol Rec 74:281–285Google Scholar
  126. Zeilhofer P, Santos E, Ribeiro A, Miyazaki R, Santos M (2007) Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil. Int J Health Geogr 6(1):7CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Science and TechnologyBihar Remote Sensing Application CentrePatnaIndia
  2. 2.Department of GeographyRaja Narendra Lal Khan Women’s CollegeMidnaporeIndia

Personalised recommendations