Advertisement

Spatial susceptibility analysis of vector-borne diseases in KMC using geospatial technique and MCDM approach

  • Sk Ajim AliEmail author
  • Ateeque Ahmad
Original Article
  • 26 Downloads

Abstract

The prevalence of vector-borne diseases (VBDs) like malaria and dengue claims many parts of the capital city Kolkata. Although the frequency of affects has been declining, still several cases are still reported from different parts of Kolkata Municipal Corporation. The present study aimed to apply multi-criteria decision making (MCDM) approach along with geospatial technique to map susceptible areas of vector-borne diseases. For growing vectors and transmitting diseases, there are always many factors responsible instead of a single factor. Hence, the present work was carried out in multiple stages. Initially different susceptible factors to vector-borne diseases like environment, demography, epidemic and related to suitable breeding sites were selected. Analytic hierarchy process as a technique of MCDM was considered and pair-wise comparison matrix (PCM) was established for each selected factor. Synergistically, weight-based single layer of susceptible zonation was developed and finally, GIS integration was performed for susceptible map of VBDs. The decision-making process was judged by consistency measurement and result shows that the consistency ratio of each selected factor ranged between 0.02 and 0.07, i.e. < 0.1 which is acceptable. Geospatial technique offers space to apply statistical method and analytical technique to acquire information. With the help of remote sensing data and spatial information, GIS tool was utilised to analyse spatial susceptibility of vector-borne diseases. The study revealed that spatial location of water bodies is the most responsible factor with highest weight among all selected factors and concomitantly, moisture content, surface temperature, proximity to waste storage bins, and reported dengue and malaria cases also share influential contributions in prevalence of vector-borne diseases. The present result shows the high applicability of geospatial technique in epidemic diseases’ zonation which may considered helpful for applying in different fields of research.

Keywords

Vector-borne diseases Kolkata Municipal Corporation Multi-criteria decision-making approach Geospatial analysis Susceptibility analysis 

Notes

Acknowledgements

We thankfully acknowledge the anonymous reviewers and the editor for their valuable time, productive comments and suggestions for improving the overall quality of the manuscript.

References

  1. Abdellah AM, Balla QI (2013) Domestic solid waste management and its impacts on human health and the environment in Sharg El Neel locality, Khartoum State, Sudan. Pak J Biol Sci 16:1538–1544.  https://doi.org/10.3923/pjbs.2013.1538.1544 CrossRefGoogle Scholar
  2. Ahmad F, Goparaju L, Qayum A (2017) Studying malaria epidemic for susceptibility zones: multi-criteria approach of geospatial tools. J Geosci Environ Prot 5:30–53.  https://doi.org/10.4236/gep.2017.55003 Google Scholar
  3. Ali SA, Ahmad A (2018) Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India. Spat Inf Res 26(4):449–469.  https://doi.org/10.1007/s41324-018-0187-x CrossRefGoogle Scholar
  4. Ali SA, Ahmad A (2019) Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach. Spat Inf Res.  https://doi.org/10.1007/s41324-019-00242-8 Google Scholar
  5. Ambrasaite I, Barfod M, Salling K (2011) MCDA and risk analysis in transport infrastructure appraisals: The Rail Baltica case. Proc Soc Behav Sci 20:944–953CrossRefGoogle Scholar
  6. Ayele DG, Temesgen TZ GM, Henry (2012) Prevalence and risk factor of malaria in Ethiopia. Malar J 11:195CrossRefGoogle Scholar
  7. Barreto ML, Teixeira MG (2008) Dengue fever: a call for local, national, and international action. Lancet 372:200–205CrossRefGoogle Scholar
  8. Braga C, Luna CF, Martelli CM, de Souza WV, Cordeiro MT et al (2010) Seroprevalence and risk factors for dengue infection in socio-economically distinct areas of Recife, Brazil. Acta Trop 113:234–240CrossRefGoogle Scholar
  9. Brown H, Diuk-Wasser M, Andreadis T, Fish D (2008) Remotely-sensed vegetation indices identify mosquito clusters of west Nile virus vectors in an urban landscape in the Northeastern United States. Vector Borne Zoonotic Dis 8:197–206CrossRefGoogle Scholar
  10. Carter R, Mendis KN, Roberts D (2000) Spatial targeting of interventions against malaria. Bull World Health Organ 78(12):1401–1411Google Scholar
  11. Carver SJ (1991) Integrating Multi-Criteria Evaluation with Geo-graphical Information Systems. Int J Geogr Inf Syst 5(3):321–339CrossRefGoogle Scholar
  12. Chang HC, Dimlich DN, Yokokura T, Mukherjee A, Kankel MW, Sen A, Sridhar V, Fulga TA, Hart AC, Van Vactor D, Artavanis-Tsakonas S (2008) Modeling spinal muscular atrophy in Drosophila. PLoS ONE 3(9):e3209.  https://doi.org/10.1371/journal.pone.0003209 CrossRefGoogle Scholar
  13. Chatterjee S (2017) Budget statement. Kolkata Municipal Corporation 2017–2018. https://www.kmcgov.in/KMCPortal/downloads/KMC_Budget_English_2017_2018.pdf. 18 Sep 2018
  14. Chen Y, Yua J, Khan S (2010) Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environ Model Softw 25:1582–1591CrossRefGoogle Scholar
  15. Cointreau S (2006) Occupational and Environmental Health Issues of Solid Waste Management. Washington DC World Bank Jul. Available from http://siteresources.worldbank.org/INTUSWM/Resources/up-2.pdf. 13 Oct 2018
  16. Delgado L, Camardiel A, Aguilar V, Martinez N, Codova K, Ramos S (2011) Geospatial tools for the identification of a Malaria Corridor in Estado Sucre, a Venezuelan North Eastern State. Geosp Health 5:169–176.  https://doi.org/10.4081/gh.2011.168 CrossRefGoogle Scholar
  17. Dongus S et al (2009) Urban agriculture and anopheles habitats in Dar es Sa-laam, Tanzania. Geosp Health 3:189–210.  https://doi.org/10.4081/gh.2009.220 CrossRefGoogle Scholar
  18. Eldin N, Sui D (2003) A COM-based spatial decision support system for industrial site selection. J Geogr Inf Decis Anal 7(2):72–92Google Scholar
  19. Eskandari M, Homaee M, Falamaki A (2016) Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility. Environ Sci Pollut Res 23(12):12423–12434.  https://doi.org/10.1007/s11356-016-6459-x CrossRefGoogle Scholar
  20. Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M, Briët OJT, Anderegg D, Liebe J, Smith T, Vounatsou P (2006) Mapping malaria transmission in West and Central Africa. Trop Med Int Health 11(7):1032–1046CrossRefGoogle Scholar
  21. Girvan MS, Bullimore J, Pretty JN, Osborn AM, Ball AS (2003) Soil type is the primary determinant of the composition of the total and active bacterial communities in arable soils. Appl Environ Microbiol 69:1800–1809CrossRefGoogle Scholar
  22. Gorsevski PV, Donevska KR, Mitrovski CD, Frizado JP (2012) Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. Waste Manag 32:287–296.  https://doi.org/10.1016/j.wasman.2011.09.023 CrossRefGoogle Scholar
  23. Gubler DJ (2004) Cities spawn epidemic dengue viruses. Nat Med 10:129–130CrossRefGoogle Scholar
  24. Guler D, Omralıoglu T (2017) Alternative suitable landfill site selection using analytic hierarchy process and geographic information systems: a case study in Istanbul. Environ Earth Sci 76(20):678.  https://doi.org/10.1007/s12665-017-7039-1 CrossRefGoogle Scholar
  25. Hii YL, Zaki RA, Aghamohammadi N, Rocklov J (2016) Research on climate and dengue in Malaysia: a systematic review. Cur Environ Health Rep 3(1):81–90CrossRefGoogle Scholar
  26. Hongoh V, Anne H, Gatewood Hoen A, Cécile W, Jean-Philippe B, Denise M, Pascal (2011) Spatially explicit multi-criteria decision analysis for managing vector-borne diseases. Int J Health Geogr 10:70.CrossRefGoogle Scholar
  27. Khormi HM, Kumar L (2011) Modelling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Sci Total Environ 409(22):4713–4719CrossRefGoogle Scholar
  28. Kleinschmidt I, Bagayoko M, Clarke GPY, Craig M, Sueur DL (2000) A spatial statistical approach to malaria mapping. Int J Epidemiol 29:355–361CrossRefGoogle Scholar
  29. Kobbe R et al (2006) Seasonal variation and high multiplicity of first Plasmodium falciparum infections in children from a holoendemic area in Ghana, West Africa. Trop Med Int Health 11(5):613–619CrossRefGoogle Scholar
  30. Kuria D, Ngari D, Withaka E (2011) Using geographic information systems (GIS) to determine land suitability for rice crop growing in the Tana delta. J Geogr Reg Plan 4(9):525–532Google Scholar
  31. Lootsma FA, Schuijt H (1997) The multiplicative AHP, SMART and ELECTRE in a common context. J Multi Criteria Decis Anal 6:185–196CrossRefGoogle Scholar
  32. Lourenço PM, Sousa CA, Seixas J, Lopes P, Novo MT, Almeida APG (2011) Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal. J Vector Ecol 36:279–291CrossRefGoogle Scholar
  33. Majumder M (2015) Impact of urbanization on water shortage in face of climatic aberrations. Springer Briefs Water Sci Technol 35–47.  https://doi.org/10.1007/978-981-4560-73-3_2
  34. Malczewski J (1999) GIS and multi criteria decision analysis. Wiley, CanadaGoogle Scholar
  35. Malczewski J (2004) GIS-based land-use suitability analysis: a critical overview. Prog Plan 62(1):3–16CrossRefGoogle Scholar
  36. Morin CW, Comrie AC, Ernst K (2013) Climate and dengue transmission: evidence and implications. Environ Health Perspect 121:1264–1272CrossRefGoogle Scholar
  37. Mushinzimana E, Munga S, Minakawa N, Li L, Feng CC, Bian L, Kitron U, Schmidt C, Beck L, Zhou G, Githeko AK, Yan G (2006) Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malaria journal 5:13.  https://doi.org/10.1186/1475-2875-5-13 CrossRefGoogle Scholar
  38. Nakhapakorn K, Tripathi NK (2005) An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. Int J Health Geogr 4:1–13CrossRefGoogle Scholar
  39. Nazri CD, Rodziah I, Hashim A (2009) Distribution pattern of a dengue fever outbreak using GIS. J Environ Health Res 9(2):89–95Google Scholar
  40. Nazri CD, Ahmad AH, Latif ZA, Ismail R, Pradhan B (2012) Coupling of remote sensing data and environmental-related parameters for dengue transmission risk assessment in Subang Jaya, Malaysia. Geocarto Int 1–15Google Scholar
  41. Nazri CD, Ahmad AH, Latif ZA, Ismail R (2016) Application of GIS-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific J Trop Dis 6(12):930–937Google Scholar
  42. Norris DE (2004) Mosquito-borne diseases as a consequence of land use change. EcoHealth.  https://doi.org/10.1007/s10393-004-0008-7 Google Scholar
  43. Palaniyandi M (2004) The impact of national river water projects on regional climatic changes and vector borne disease outbreaks in India. Paper presented at the National Conference on Climate Change and Its Impact on Water Resources in India, School of Earth and Atmospheric SciencesGoogle Scholar
  44. Palaniyandi M (2013) GIS mapping of vector breeding habitats. Geosp World Wkly 9:1–4Google Scholar
  45. Palaniyandi M, Mariappan T (2012) Containing the spread of malaria with geospatial technology—a case study with Vizag City, India. Geospatial World 8:1–9Google Scholar
  46. Parimala M, Lopez D (2012) Decision making in agriculture based on land suitability-spatial data analysis approach. J Theor Appl Inf Technol 46(1):17–23CrossRefGoogle Scholar
  47. Qayum A, Arya R, Kumar P, Lynn AM (2015) Socio-economic, epidemiological and geographic features based on GIS integrated mapping to identify Malarial hotspots. Malar J 14:192.  https://doi.org/10.1186/s12936-015-0685-4 CrossRefGoogle Scholar
  48. Rakotomanana F, Randremanana R, Rabarijaona L, Duchemin J, Ratovonjato J, Ariey F, Jeanne I (2007) Determining areas that require indoor insecticide spraying using Multi Criteria Evaluation, a decision-support tool for malaria vector control programmes in the Central Highlands of Madagascar. Int J Health Geogr 6(1):1–11CrossRefGoogle Scholar
  49. Rasania SK, Bhanot A, Sachdev TR (2002) Awareness and practices regarding Malaria of catchment population of a primary Health Centre in Delhi. J Commun Dis 34:78–84Google Scholar
  50. Rattanarithikul R et al (1995) Larval habitats of malaria vectors and other anopheles mosquitoes around a transmission focus in Northwestern Thailand. J Am Mosq Control Assoc 11:428–433Google Scholar
  51. Richardson DB, Volkow ND, Kwan MP, Kaplan RM, Goodchild MF, Croyle RT (2013) Spatial turn in health research. Science 339(6126):1390–1392CrossRefGoogle Scholar
  52. Rikalovic A, Cosic I, Lazarevic D (2014) GIS based multi-criteria analysis for industrial site selection. Proc Eng 69:1054–1063CrossRefGoogle Scholar
  53. Robert V, Macintyre K, Keating J, Trape JF, Duchemin JB, Warren M (2003) Malaria transmission in urban sub-Saharan Africa. Am J Trop Med Hyg 68:169–176CrossRefGoogle Scholar
  54. Rochon GL, Quansah JE, Fall S, Araya B, Biehl LL, Thiam T, Ghani S (2010) Remote sensing, public health & disaster mitigation. Geosp Technol Environ Manag 3:187–209Google Scholar
  55. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill International, New YorkGoogle Scholar
  56. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26CrossRefGoogle Scholar
  57. Saaty TL (2012) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publication, PittsburghGoogle Scholar
  58. Saaty TL, Vargas LG (2001) Models, methods, concepts and applications of the analytic hierarchy process. Kluwer Academic Publishers, Norwell  https://doi.org/10.1007/978-1-4615-1665-1 CrossRefGoogle Scholar
  59. Saaty TL, Vargas LG (2012) Models, methods, concepts and applications of the analytic hierarchy process. Kluwer, DordrechtCrossRefGoogle Scholar
  60. Sabesan S, Vanamail P, Raju KHK, Raju P (2010) Lymphatic filariasis in India: epidemiology and control measures. J Postgrad Med 56(3):232.  https://doi.org/10.4103/0022-3859.68650 CrossRefGoogle Scholar
  61. Sandru MIV (2014) Promoting spatial data synthesis for vector-borne disease assessment in Romania. Rom Rev Reg Stud (2):75–86Google Scholar
  62. Sarfraz MS, Tripathi NK, Faruque FS, Bajwa UI, Kitamoto A, Souris M (2014) Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters. Geospat Health 8(3):S685–S697CrossRefGoogle Scholar
  63. Schmidt WP, Suzuki M, Thiem VD, White RG, Tsuzuki A, Yoshida LM, Yanai H, Haque U, Tho LH, Anh DD, Ariyoshi K (2011) Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. Interdiscip Res Collect PLOS.  https://doi.org/10.1371/journal.pmed.1001082 Google Scholar
  64. Sharma VP et al (1996) Study on feasibility of delivering mosquito genic conditions in and around Delhi using IRS data. Indian J Malariol 33:107–125Google Scholar
  65. Sharma SN, Ghosh D, Srivastava PK, Sonal GS, Dhariwal AC (2014) Vector-borne diseases in Kolkata Municipal Corporation (KMC): achievements and challenges. J Commun Dis 46(2):68–76Google Scholar
  66. Sheela AM, Ghermandi A, Vineetha P, Sheeja RV, Justus J, Ajayakrishna K (2017) Assessment of relation of land use characteristics with vector-borne diseases in tropical areas. Land Use Policy 63:369–380.  https://doi.org/10.1016/j.landusepol.2017.01.047 CrossRefGoogle Scholar
  67. Syamsuddin I, Hwang J (2009) The application of AHP to evaluate information security policy decision making. J Simul Syst Sci Technol 10(5):33–37Google Scholar
  68. Tavares G, Zsigraiova Z, Semiao V (2011) Multi-criteria GIS-based siting of an incineration plant for municipal solid waste. Waste Manag 31(9–10):1960–1972.  https://doi.org/10.1016/j.wasman.2011.04.013 CrossRefGoogle Scholar
  69. Tu C, Fang Y, Huang Z, Tan R (2014) Application of the analytic hierarchy process to a risk assessment of emerging infectious diseases in Shaoxing city in southern China. Jpn J Infect Dis 67(6):417–422CrossRefGoogle Scholar
  70. Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66Google Scholar
  71. Walker M et al (2013) Temporal and micro spatial heterogeneity in the distribution of anopheles vectors of malaria along the Kenyan Coast. Parasit Vectors 6:311.  https://doi.org/10.1186/1756-3305-6-311 CrossRefGoogle Scholar
  72. WHO (2017) World Malaria report. World Health Organization, Geneva. https://www.who.int/malaria/publications/world-malaria-report-2017/en/. Accessed 23 Sept 2018
  73. Wondim YK, Alemayehu EB, Abebe WB (2017) Malaria hazard and risk mapping using gis based spatial multicriteria evaluation technique (SMCET) in Tekeze Basin Development Corridor, Amhara Region, Ethiopia. J Environ Earth Sci 7(5):76–87Google Scholar
  74. Wood BL, Beck LR, Washino RK, Hibbard KA, Salute JS (1992) Estimating high mosquito-producing rice fields using spectral and spatial data. Int J Remote Sens 13:2813–2826.  https://doi.org/10.1080/01431169208904083 CrossRefGoogle Scholar
  75. Yadav K, Nath MJ, Talukdar PK, Saikia PK, Baruah I, Singh L (2012) Malaria risk areas of Udalguri District of Assam, India: a GIS-based study. Int J Geogr Inf Sci 26:123–131.  https://doi.org/10.1080/13658816.2011.576678 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Geography, Faculty of ScienceAligarh Muslim UniversityAligarhIndia

Personalised recommendations