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Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach

  • Sk Ajim AliEmail author
  • Ateeque Ahmad
Article
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Abstract

Mosquito-borne diseases are those which transmitted through the bite of an infected mosquito. Stagnant water bodies are often preferable for breeding sites of mosquitos. But from breeding eggs to final stage, there are many factors responsible for its incubation, maturation and growth enough to bite and transmit diseases. The main aim of present study is to focus on associated environmental factors that provide suitable breeding sites and susceptibility mapping of mosquito-borne-diseases through geospatial technique and decision making approach. Analytic hierarchy process as a decision making approach was integrated with geographic information system to map of mosquito-borne diseases in Kolkata Municipal Corporation. Choice based various ranking was used to decide the weights of selected factors through establishing pairwise comparison matrix. Initially, 10 relevant environmental factors were chosen to determine their weight through pairwise comparison matrix. Concomitantly, weight of each causative factor was used as geo-database to support overlay analysis. Consistency ratio was calculated to check the decision process and significance measurement. The consistency ratio of decision factors was calculated as 0.0470, which is < 0.1 and considered as consistent and acceptable. The study analysis shows that proximity to water bodies is a major responsible factor and subsequently moisture content, water index, availability of shadow area and presence of vegetation are also influential factors in prevalence of mosquito-borne diseases. The present result shows the high applicability of satellite data and spatial technique in epidemic diseases zonation.

Keywords

Mosquito-borne diseases KMC Environmental factors GIS AHP Detection of vulnerable zones 

Notes

Acknowledgements

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

Supplementary material

41324_2019_242_MOESM1_ESM.tif (1000 kb)
Supplementary material 1 (TIFF 999 kb)

References

  1. 1.
    Ahmad, F., Goparaju, L., & Qayum, A. (2017). Studying malaria epidemic for vulnerability zones: Multi-criteria approach of geospatial tools. Journal of Geoscience and Environment Protection, 5, 30–53.  https://doi.org/10.4236/gep.2017.55003.CrossRefGoogle Scholar
  2. 2.
    Sharma, S. N., Ghosh, D., Srivastava, P. K., Sonal, G. S., & Dhariwal, A. C. (2014). Vector borne diseases in Kolkata Municipal Corporation KMC: Achievements and challenges. Journal of Communicative Disease, 46(2), 68–76.Google Scholar
  3. 3.
    Wondim, Y. K., Alemayehu, E. B., & Abebe, W. B. (2017). Malaria hazard and risk mapping using GIS based spatial multicriteria evaluation technique (SMCET) in Tekeze Basin Development Corridor, Amhara Region, Ethiopia. Journal of Environment and Earth Science, 7(5), 76–87.Google Scholar
  4. 4.
    Scott, F. L. (2007). Anti-protozoals. Department of Pharmaceutical Sciences, College of Pharmacy, Oklahoma State University, Weatherford. http://faculty.swosu.edu/scott.long/phcl/antiprot.htm.2007. Accessed 13 Sept 2018.
  5. 5.
    Morin, C. W., Comrie, A. C., & Ernst, K. (2013). Climate and dengue transmission: Evidence and implications. Environmental Health Perspectives, 121, 1264–1272.CrossRefGoogle Scholar
  6. 6.
    Hii, Y. L., Zaki, R. A., Aghamohammadi, N., & Rocklov, J. (2016). Research on climate and dengue in Malaysia: A systematic review. Current Environmental Health Reports, 3(1), 81–90.CrossRefGoogle Scholar
  7. 7.
    Rattanarithikul, R., et al. (1995). Larval habitats of malaria vectors and other Anopheles mosquitoes around a transmission focus in Northwestern Thailand. Journal of the American Mosquito Control Association, 11, 428–433.Google Scholar
  8. 8.
    Mushinzimana, E., Munga, S., Minakawa, N., Li, L., Feng, C. C., Bian, L., et al. (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
  9. 9.
    Ayele, D. G., Temesgen, T. Z., & Henry, G. M. (2012). Prevalence and risk factor of malaria in Ethiopia. Malaria Journal, 11, 195.CrossRefGoogle Scholar
  10. 10.
    WHO. (2012). World Malaria Report. Geneva: World Health Organization. www.who.int/malaria/publications/world_malaria_report_2012/en/. Accessed 7 Sept 2018.
  11. 11.
    WHO. (2017). World Malaria Report. Geneva: World Health Organization. www.who.int/malaria/publications/world-malaria-report-2017/report/en/. Accessed 7 Sept 2018.
  12. 12.
    Ali, S. A., & Ahmad, A. (2018). Using analytic hierarchy process with GIS for dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India. Spatial Information Research, 26, 449.  https://doi.org/10.1007/s41324-018-0187-x.CrossRefGoogle Scholar
  13. 13.
    Nazri, C. D., Ahmad, A. H., Latif, Z. A., Ismail, R., & Pradhan, B. (2012). Coupling of remote sensing data and environmental related parameters for dengue transmission risk assessment in Subang Jaya, Malaysia. Geocarto International, 28(3), 1–15.  https://doi.org/10.1080/10106049.2012.696726.Google Scholar
  14. 14.
    Sarfraz, M. S., Tripathi, N. K., Faruque, F. S., Bajwa, U. I., 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. Geospatial Health, 8(3), S685–S697.CrossRefGoogle Scholar
  15. 15.
    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. Japanese Journal of Infectious Diseases, 67(6), 417–422.CrossRefGoogle Scholar
  16. 16.
    Dongus, S., et al. (2009). Urban agriculture and Anopheles habitats in Dar es Salaam, Tanzania. Geospatial Health, 3, 189–210.  https://doi.org/10.4081/gh.2009.220.CrossRefGoogle Scholar
  17. 17.
    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. Geospatial Health, 5, 169–176.  https://doi.org/10.4081/gh.2011.168.CrossRefGoogle Scholar
  18. 18.
    Sharma, V. P., et al. (1996). Study on feasibility of delivering mosquitogenic conditions in and around Delhi using IRS data. Indian Journal of Malariology, 33, 107–125.Google Scholar
  19. 19.
    Robert, V., Macintyre, K., Keating, J., Trape, J. F., Duchemin, J. B., & Warren, M. (2003). Malaria transmission in urban sub-Saharan Africa. The American Journal of Tropical Medicine and Hygieneg, 68, 169–176.CrossRefGoogle Scholar
  20. 20.
    Palaniyandi, M. (2013). GIS mapping of vector breeding habitats. Geospatial World Weekly, 9, 1–4.Google Scholar
  21. 21.
    Wood, B. L., Beck, L. R., Washino, R. K., Hibbard, K. A., & Salute, J. S. (1992). Estimating high mosquito-producing rice fields using spectral and spatial data. International Journal of Remote Sensing, 13, 2813–2826.  https://doi.org/10.1080/01431169208904083.CrossRefGoogle Scholar
  22. 22.
    Qayum, A., Arya, R., Kumar, P., & Lynn, A. M. (2015). Socio-economic, epidemiological and geographic features based on GIS integrated mapping to identify malarial hotspots. Malaria Journal, 14, 192.  https://doi.org/10.1186/s12936-015-0685-4.CrossRefGoogle Scholar
  23. 23.
    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 Geographics, 4, 1–13.CrossRefGoogle Scholar
  24. 24.
    Rochon, G. L., Quansah, J. E., Fall, S., Araya, B., Biehl, L. L., Thiam, T., et al. (2010). Remote sensing, public health and disaster mitigation. Geospatial Technologies in Environmental Management, 3, 187–209.CrossRefGoogle Scholar
  25. 25.
    Hongoh, V., Anne, H., Gatewood Hoen, A., Cécile, W., Jean-Philippe, B., et al. (2011). Spatially explicit multi-criteria decision analysis for managing vector-borne diseases. International Journal of Health Geographics, 10, 70. http://www.ij-healthgeographics.com/content/10/1/70. Accessed 17 Sept 2018.
  26. 26.
    Khormi, H. M., & Kumar, L. (2011). Modelling 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.CrossRefGoogle Scholar
  27. 27.
    Yadav, K., Nath, M. J., Talukdar, P. K., Saikia, P. K., Baruah, I., & Singh, L. (2012). Malaria risk areas of Udalguri District of Assam, India: A GIS-based study. International Journal of Geographical Information Science, 26, 123–131.  https://doi.org/10.1080/13658816.2011.576678.CrossRefGoogle Scholar
  28. 28.
    Walker, M., et al. (2013). Temporal and micro spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan Coast. Parasites and Vectors, 6, 311.  https://doi.org/10.1186/1756-3305-6-311.CrossRefGoogle Scholar
  29. 29.
    Nazri, C. D., Ahmad, A. H., Latif, Z. A., & Ismail, R. (2016). Application of GIS-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific Journal of Tropical Disease, 6(12), 930–937.Google Scholar
  30. 30.
    Sheela, A. M., Ghermandi, A., Vineetha, P., Sheeja, R. V., 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
  31. 31.
    Norris, D. E. (2004). Mosquito-borne diseases as a consequence of land use change. EcoHealth, 1, 19–24.  https://doi.org/10.1007/s10393-004-0008-7.CrossRefGoogle Scholar
  32. 32.
    Kleinschmidt, I., Bagayoko, M., Clarke, G. P. Y., Craig, M., & Sueur, D. L. (2000). A spatial statistical approach to malaria mapping. International Journal of Epidemiology, 29, 355–361.CrossRefGoogle Scholar
  33. 33.
    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 and Zoonotic Diseases, 8, 197–206.CrossRefGoogle Scholar
  34. 34.
    Lourenço, P. M., Sousa, C. A., Seixas, J., Lopes, P., Novo, M. T., & Almeida, A. P. G. (2011). Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal. Journal of Vector Ecology, 36, 279–291.CrossRefGoogle Scholar
  35. 35.
    Gemperli, A., Sogoba, N., Fondjo, E., Mabaso, M., Bagayoko, M., Briët, O. J. T., et al. (2006). Mapping malaria transmission in West and Central Africa. Tropical Medicine & International Health, 11(7), 10321046.CrossRefGoogle Scholar
  36. 36.
    Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the third earth resources technology satellite1 symposium (pp. 301–317). Greenbelt: NASA SP351.Google Scholar
  37. 37.
    Sandru, M. I. V. (2014). Promoting spatial data synthesis for vector-borne disease assessment in Romania. Romanian Review of Regional Studies, X(2), 75–86.Google Scholar
  38. 38.
    Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6, 9829–9852.  https://doi.org/10.3390/rs6109829.CrossRefGoogle Scholar
  39. 39.
    Epstein, P. R. (2001). Climate change and emerging infectious diseases. Microbes and Infection, 3, 747–754.CrossRefGoogle Scholar
  40. 40.
    McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.CrossRefGoogle Scholar
  41. 41.
    Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033.CrossRefGoogle Scholar
  42. 42.
    Atieli, H., et al. (2011). Topography as a modifier of breeding habitats and concurrent vulnerability to malaria risk in the western Kenya highlands. Parasites and Vectors, 4, 241.  https://doi.org/10.1186/1756-3305-4-241.CrossRefGoogle Scholar
  43. 43.
    Sergo, P. (2007). Dengue fever warming up to human habits. Retrieved from http://www.scienceline.org. Accessed 22 Sept 2018
  44. 44.
    Ahmed, A. (2014). GIS and remote sensing for malaria risk mapping, Ethiopia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-8, 155–161.CrossRefGoogle Scholar
  45. 45.
    Mu, E., & Rojas, M. P. (2017). Understanding the analytical hierarchy process. In Springer briefs in operational research.  https://doi.org/10.1007/978-3-319-33861-3_2.
  46. 46.
    Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48, 9–26.CrossRefGoogle Scholar
  47. 47.
    Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts and applications of the analytic hierarchy process. Norwell: Kluwer Academic Publishers.  https://doi.org/10.1007/978-1-4615-1665-1.CrossRefGoogle Scholar
  48. 48.
    Saaty, T. L. (2012). Decision making for leaders: The analytic hierarchy process for decisions in a complex world. Pittsburgh: RWS Publication.Google Scholar
  49. 49.
    Jeefoo, P., Tripathi, N. K., & Hara, S. (2008). Analytical hierarchy process modeling for malaria risk zonation in Kanchanaburi, Thailand. In International symposium on geoinformatics for spatial infrastructure development in earth and allied sciences. Accessed from www.gisws.media.osaka-cu.ac.jp/gisideas08/viewpaper.php?id=284. Accessed 23 Sept 2018.
  50. 50.
    Bhatt, B., & Joshi, J. P. (2014). Analytical hierarchy process modeling for malaria risk zones in Vadodara district, Gujarat. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-8. Accessed from https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/171/2014/isprsarchives-XL-8171201. Accessed 24 Sept 2018.
  51. 51.
    Saaty, T. L., & Vargas, L. G. (2012). Models, methods, concepts and applications of the analytic hierarchy process. Dordrecht: Kluwer.CrossRefGoogle Scholar
  52. 52.
    Barrera, R., Amador, M., & MacKay, A. J. (2011). Population dynamics of aedes aegypti and dengue as influenced by weather and human behaviour in San Juan, Puerto Rico. PLoS Neglected Tropical Diseases, 5, e1378.  https://doi.org/10.1371/journal.pntd.0001378.CrossRefGoogle Scholar
  53. 53.
    Cheong, Y. L., Leitao, P. J., & Lakes, T. (2014). Assessment of land use factors associated with dengue cases in Malaysia using boosted regression trees. Spatial and Spatio-temporal Epidemiology, 10, 75–84.CrossRefGoogle Scholar

Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Department of Geography, Faculty of ScienceAligarh Muslim University (AMU)AligarhIndia

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