Geographical analysis

Department of Geography & GIS

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Evaluating Mosquito-Borne Disease Risk Areas in Muktsar District, India: A Decision-Making Approach Using GIS and AHP
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Geographical analysis

DOI: 10.53989/bu.ga.v14i1.24.197

Year: 2025, Volume: 14, Issue: 1, Pages: 54-66

Original Article

Evaluating Mosquito-Borne Disease Risk Areas in Muktsar District, India: A Decision-Making Approach Using GIS and AHP

Amritpal Singh1, L T S Guite1∗

1Department of Geography, Central University of Punjab, Punjab, India

*Corresponding Author
Email: [email protected]

Received Date:17 October 2024, Accepted Date:10 August 2025

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Mosquito-borne diseases are those that are transmitted by the bite of an infected mosquito. Stagnant bodies of water are frequently preferred as mosquito breeding places. However, from producing eggs to the final stage, several elements contribute to its incubation, maturity, and growth to the point where it is capable of biting and transmitting diseases. The primary goal of this research is to focus on connected environmental determinants that provide optimal breeding locations and vulnerability mapping of mosquito-borne diseases using geospatial techniques and a decision-making approach. The analytical hierarchy process was combined with a geographic information system to create a map of mosquito-borne diseases in Muktsar district of Punjab state. The weights of selected variables were determined using a choice-based varied ranking method, which involved building a pair-wise comparison matrix. Initially, ten important environmental parameters were selected to determine their weight using a pair-wise comparison matrix. At the same time, the weight of each related element was employed as a geo-database to aid with overlay analysis. The consistency ratio was derived to evaluate the decision-making process and significance measurement. The consistency ratio of choice factors was found to be 0.0470, which is less than 0.1 and regarded consistent and acceptable. According to the study's findings, proximity to water bodies is a major influence, followed by moisture content, water index, availability of shade area, and the presence of vegetation in mosquito-borne disease prevalence. The current findings demonstrate the wide range of uses of satellites data and spatial techniques in epidemic diseases zonation.

Keywords: Mosquito-borne diseases, Geospatial analysis, Analytic Hierarchy Process, Public health

References

  1. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005;438(7066):310–317. Available from: https://dx.doi.org/10.1038/nature04188
  2. Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito‐borne disease. Ecology Letters. 2019;22(10):1690–1708. Available from: https://doi.org/10.1111/ele.13335
  3. Lunde TM, Korecha D, Loha E, Sorteberg A, Lindtjørn B. A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. I. Model description and sensitivity analysis. Malaria Journal. 2013;12(1):1–29. Available from: https://doi.org/10.1186/1475-2875-12-28
  5. Cheong YL, Leitão PJ, Lakes T. Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. Spatial and Spatio-temporal Epidemiology. 2014;10:75–84. Available from: https://doi.org/10.1016/j.sste.2014.05.002
  6. Morin CW, Comrie AC, Ernst K. Climate and Dengue Transmission: Evidence and Implications. Environmental Health Perspectives. 2013;121(11-12):1264–1272. Available from: https://dx.doi.org/10.1289/ehp.1306556
  7. Shililu JI, Grueber WB, Mbogo CM, Githure JI, Riddiford LM, &beier JC. Development and survival of Anopheles gambiae eggs in drying soil: influence of the rate of drying, egg age, and soil type. Journal of the American mosquito control association. 2004;20(3):243–247. Available from: https://pubmed.ncbi.nlm.nih.gov/15532921/
  8. Atieli HE, Zhou G, Lee MC, Kweka EJ, Afrane Y, Mwanzo I, et al. Topography as a modifier of breeding habitats and concurrent vulnerability to malaria risk in the western Kenya highlands. Parasites & Vectors. 2011;4(1):1–12. Available from: https://dx.doi.org/10.1186/1756-3305-4-241
  9. Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of geographical information systems and remote sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005;47(1):81–96. Available from: https://pubmed.ncbi.nlm.nih.gov/16044677/
  10. Hii YL, Zaki RA, Aghamohammadi N, Rocklöv J. Research on Climate and Dengue in Malaysia: A Systematic Review. Current Environmental Health Reports. 2016;3(1):81–90. Available from: https://dx.doi.org/10.1007/s40572-016-0078-z
  11. Alobuia WM, Missikpode C, Aung M, Jolly PE. Knowledge, Attitude, and Practices Regarding Vector-borne Diseases in Western Jamaica. Annals of Global Health. 2016;81(5):654–663. Available from: https://dx.doi.org/10.1016/j.aogh.2015.08.013
  14. Grover GS, Takkar J, Kaura T, Devi S, Pervaiz N, Kaur U, et al. Trend Analysis of Three Major Mosquito Borne Diseases in Punjab, India. Journal of Biosciences and Medicines. 2020;08(05):1–11. Available from: https://doi.org/10.4236/jbm.2020.85001
  15. Lata S, Singh G, Dubey S. An Epidemiological Investigation of Dengue Outbreak in Shri Muktsar Sahib District. International Journal of Advance Research. 2017;3(1):152–158. Available from: https://www.ijariit.com/manuscripts/v3i1/V3I1-1162.pdf
  17. Wimberly MC, Beurs KMd, Loboda TV, Pan WK. Satellite Observations and Malaria: New Opportunities for Research and Applications. Trends in Parasitology. 2021;37(6):525–537. Available from: https://dx.doi.org/10.1016/j.pt.2021.03.003
  18. Kazansky Y, Wood D, Sutherlun J. The current and potential role of satellite remote sensing in the campaign against malaria. Acta Astronautica. 2016;121:292–305. Available from: https://doi.org/10.1016/j.actaastro.2015.09.021
  19. Parselia E, Kontoes C, Tsouni A, Hadjichristodoulou C, Kioutsioukis I, Magiorkinis G, et al. Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review. Remote Sensing. 2019;11(16):1862. Available from: https://dx.doi.org/10.3390/rs11161862
  20. Adeola AM, Olwoch JM, Botai JO, Rautenbach CJD, Kalumba AM, Tsela PL, et al. Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa. South African Geographical Journal. 2017;99(1):14–28. Available from: https://doi.org/10.1080/03736245.2015.1117012
  21. Davis JK, Gebrehiwot T, Worku M, Awoke W, Mihretie A, Nekorchuk D, et al. A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model. Environmental Modelling & Software. 2019;119:275–284. Available from: https://dx.doi.org/10.1016/j.envsoft.2019.06.010
  22. Solano-Villarreal E, Valdivia W, Pearcy M, Linard C, Pasapera-Gonzales J, Moreno-Gutierrez D, et al. Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon. Scientific Reports. 2019;9(1):15173. Available from: https://dx.doi.org/10.1038/s41598-019-51564-4
  23. Midekisa A, Senay G, Henebry GM, Semuniguse P, Wimberly MC. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria Journal. 2012;11(1):1–10. Available from: https://doi.org/10.1186/1475-2875-11-165
  24. Machault V, Vignolles C, Borchi F, Vounatsou P, Pages F, Briolant S, et al. The use of remotely sensed environmental data in the study of malaria. Geospatial health. 2011;5(2):151–168. Available from: https://dx.doi.org/10.4081/gh.2011.167
  25. Ebhuoma O, Gebreslasie M. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa. International Journal of Environmental Research and Public Health. 2016;13(6):584. Available from: https://dx.doi.org/10.3390/ijerph13060584
  26. Ali SA, Ahmad A. Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation. Spatial Information Research. 2018;26(4):449–469. Available from: https://doi.org/10.1007/s41324-018-0187-x
  27. Nakhapakorn K, Tripathi NK. An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International journal of health geographics. 2005;4(1):1–13. Available from: https://doi.org/10.1186/1476-072X-4-13
  28. Rochon GL, Quansah JE, Fall S, Araya B, Biehl LL, Thiam T, et al. Remote Sensing, Public Health & Disaster Mitigation. In: Geospatial Technologies in Environmental Management . (pp. 187-209) Springer Netherlands. 2010.
  29. Hongoh V, Hoen AG, Aenishaenslin C, Waaub JPP, Bélanger D, Michel P. Spatially explicit multi-criteria decision analysis for managing vector-borne diseases. International Journal of Health Geographics. 2011;10(1):1–9. Available from: https://doi.org/10.1186/1476-072X-10-70
  31. Yadav K, Nath MJ, Talukdar PK, Saikia PK, Baruah I, Singh L. Malaria risk areas of Udalguri district of Assam, India: a GIS-based study. International Journal of Geographical Information Science. 2012;26(1):123–131. Available from: https://doi.org/10.1080/13658816.2011.576678
  32. Walker M, Winskill P, Basáñez MGG, Mwangangi JM, Mbogo C, Beier JC, et al. Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast. Parasites & Vectors. 2013;6(1):1–14. Available from: https://doi.org/10.1186/1756-3305-6-311
  33. Dom NC, Ahmad AH, Latif ZA, Ismail R. Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific Journal of Tropical Disease. 2016;6(12):928–935. Available from: https://dx.doi.org/10.1016/s2222-1808(16)61158-1
  34. Delgado-Petrocelli L, Camardiel A, Aguilar VH, Martinez N, Córdova K, Ramos S. Geospatial tools for the identification of a malaria corridor in Estado Sucre, a Venezuelan north-eastern state. Geospatial health. 2011;5(2):169–176. Available from: https://dx.doi.org/10.4081/gh.2011.168
  35. Sharma VP, Dhiman RC, Ansari MA, Nagpal BN, Srivastava A, Manavalan P, et al. Study on the feasibility of delineating mosquitogenic conditions in and around Delhi using Indian Remote Sensing Satellite data. Indian Journal of Malariology. 1996;33(3):107–125. Available from: https://pubmed.ncbi.nlm.nih.gov/9014394/
  36. Robert V, Macintyre K, Keating J, Trape JF, Duchemin JB, Warren M, et al. Malaria transmission in urban sub-Saharan Africa. The American journal of tropical medicine and hygiene. 2003;68(2):169–176. Available from: https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-07/010030692.pdf
  38. Wood BL, Beck LR, Washino RK, Hibbard KA, Salute JS. Estimating high mosquito-producing rice fields using spectral and spatial data. International Journal of Remote Sensing. 1992;13(15):2813–2826. Available from: https://doi.org/10.1080/01431169208904083
  39. Ahmad F, Goparaju L, Qayum A. Studying Malaria Epidemic for Vulnerability Zones: Multi-Criteria Approach of Geospatial Tools. Journal of Geoscience and Environment Protection. 2017;05(05):30–53. Available from: https://dx.doi.org/10.4236/gep.2017.55003
  41. Mu E, Pereyra-Rojas M. Understanding the Analytic Hierarchy Process. In: Practical Decision Making using Super Decisions v3 , SpringerBriefs in Operations Research. (pp. 7-22) 2018.
  43. Chopra RPS, Krishan G. Analysis of aquifer characteristics and groundwater quality in south-west Punjab, India. Journal of earth science and engineering. 2014;4(10):597–604. Available from: https://www.davidpublisher.com/Public/uploads/Contribute/55078aa6510ef.pdf
  45. Saaty TL. How to make a decision: The analytic hierarchy process. European Journal of Operational Research. 1990;48(1):9–26. Available from: https://dx.doi.org/10.1016/0377-2217(90)90057-i
  46. Saaty TL. What is the analytic hierarchy process? In Mathematical models for decision support. (pp. 109-121) Berlin, Heidelberg. Springer. 1988.
  48. Sheela AM, Ghermandi A, Vineetha P, Sheeja RV, Justus J, Ajayakrishna K. Assessment of relation of land use characteristics with vector-borne diseases in tropical areas. Land Use Policy. 2017;63:369–380. Available from: https://dx.doi.org/10.1016/j.landusepol.2017.01.047
  50. Kleinschmidt I, Bagayoko M, Clarke G, Craig M, Sueur DL. A spatial statistical approach to malaria mapping. International Journal of Epidemiology. 2000;29(2):355–361. Available from: https://dx.doi.org/10.1093/ije/29.2.355
  51. Brown H, Diuk-Wasser M, Andreadis T, Fish D. 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. 2008;8(2):197–206. Available from: https://dx.doi.org/10.1089/vbz.2007.0154
  52. Lourenço PM, Sousa CA, Seixas J, Lopes P, Novo MT, Almeida APG. Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal. Journal of Vector Ecology. 2011;36(2):279–291. Available from: https://doi.org/10.1111/j.1948-7134.2011.00168.x
  54. Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M, Briët OJT, et al. Mapping malaria transmission in West and Central Africa. Tropical Medicine & International Health. 2006;11(7):1032–1046. Available from: https://doi.org/10.1111/j.1365-3156.2006.01640.x
  56. Rowley WA, Graham CL. The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. Journal of Insect Physiology. 1968;14(9):1251–1257. Available from: https://dx.doi.org/10.1016/0022-1910(68)90018-8
  57. Epstein PR. West Nile virus and the climate. Journal of Urban Health: Bulletin of the New York Academy of Medicine. 2001;78(2):367–371. Available from: https://doi.org/10.1093/jurban/78.2.367
  58. McFeeters SK. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 1996;17(7):1425–1432. Available from: https://dx.doi.org/10.1080/01431169608948714
  60. Mushinzimana E, Munga S, Minakawa N, Li L, Feng Cc, Bian L, et al. Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malaria Journal. 2006;5(1):1–11. Available from: https://dx.doi.org/10.1186/1475-2875-5-13
  61. Ahmed A. GIS and Remote Sensing for Malaria Risk Mapping, Ethiopia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014;40(8):155–161. Available from: https://doi.org/10.5194/isprsarchives-XL-8-155-2014
  62. Dom NC, Ahmad AH, Latif ZA, Ismail R, Pradhan B. Coupling of remote sensing data and environmental-related parameters for dengue transmission risk assessment in Subang Jaya, Malaysia. Geocarto International. 2013;28(3):258–272. Available from: https://dx.doi.org/10.1080/10106049.2012.696726
  63. Barrera R, Amador M, Mackay AJ. Population Dynamics of Aedes aegypti and Dengue as Influenced by Weather and Human Behavior in San Juan, Puerto Rico. PLoS Neglected Tropical Diseases. 2011;5(12):e1378. Available from: https://doi.org/10.1371/journal.pntd.0001378

Copyright

© 2025 Singh & Guite. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Bangalore University, Bengaluru, Karnataka

  • 18 November 2025

Year: 2025, Volume: 14, Issue: 1

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