Modeling Spatio-temporal Malaria Risk Using Remote Sensing and Environmental Factors

  • Muhammad Haris MAZHER Institute of Geographic Information Systems, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
  • Javed IQBAL Institute of Geographic Information Systems, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
  • Muhammad Ahsan MAHBOOB Institute of Geographic Information Systems, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
  • Iqra ATIF Institute of Geographic Information Systems, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
Keywords: Malaria, Climatic and environmental variables, Remote sensing, Malaria risk modeling, Pakistan

Abstract

AbstractBackground: Remote sensing have been intensively used across many disciplines, however, such information was limited in spatial epidemiology.Methods: Two years (2009 & 2010) Landsat TM satellite data was used to develop vegetation, water bodies, air temperature and humidity criterion maps to model malaria risk and its spatiotemporal seasonal variation. The criterion maps were used in weighted overlay analysis to generate final categorized malaria risk map.Results: Overall, 25%, 68%, 18% and 16% of the total area of Rawalpindi region was categorized as danger zone for Jun 2009, Oct 2009, Jan 2010 and Jun 2010, respectively. The malaria risk reached at its peak during the monsoon season whereas air temperature and relative humidity were the main contributing factors in seasonal variation.Conclusion: Malaria risk maps could be used for prioritizing areas for malaria control measures.  

References

Allen RG, Tasumi M, Morse A, et al (2005). A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrig Drain,19(3-4):251-68.

Asare EO, Tompkins AM, Amekudzi LK, et al (2016). Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa. Geospat Health,11(1 Suppl):391.

Bastiaanssen W, Noordman E, Pelgrum H, et al (2005). SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. J Irrig Drain E-Asce, 131(1):85-93.

Boegh E, Soegaard H, Thomsen A (2002). Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance. Remote Sens Environ, 79(2-3):329-43.

Borgogno-Mondino E, Lessio A, Gomarasca MA (2016). A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis. Eur J Remote Sens, 49(1):137-56.

Haghdoost A (2004). Assessment of seasonal and climatic effects on the incidence and species composition of malaria by using GIS methods: University of London, 2004.

Kabaria CW, Molteni F, Mandike R, et al (2016). Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam. Int J Health Geogr, 15:26.

Kaya S, Pultz T, Mbogo C, et al (2002). The use of radar remote sensing for identifying environmental factors associated with malaria risk in coastal Kenya. International Geoscience and Remote Sensing Symposium, Toronto, 2002

Ifatimehin OO (2009). Malaria risk mapping in Lokoja, Nigeria using Geoinformatic technology.

Keiser J, Utzinger J, De Castro MC, et al (2004). Urbanization in sub-saharan Africa and implication for malaria control. Am J Trop Med Hyg, 71(2 Suppl):118-27.

Khadim M (2002). Malaria a menace at Zhob Garrison. Pak Armed Forces Med J,52(2):203-7.

Kleinschmidt I, Omumbo J, Briet O, et al (2001). An empirical malaria distribution map for West Africa. Trop Med Int Health, 6(10):779-86.

Kotwal RS, Wenzel RB, Sterling RA, et al (2005). An outbreak of malaria in US Army Rangers returning from Afghanistan. JAMA, 293(2):212-6.

Kouyate B, Sie A, Ye M, et al (2007). The great failure of malaria control in Africa: a district perspective from Burkina Faso. PLoS Med,4(6):e127.

Leder K, Black J, O'brien D, et al (2004). Malaria in travelers: a review of the GeoSentinel surveillance network. Clin Infect Dis, 39(8):1104-12.

Kleinschmidt I, Bagayoko M, Clarke G, et al (2000). A spatial statistical approach to malaria mapping. Int J Epidemiol, 29(2):355-61.

Levine RS, Peterson AT, Benedict MQ (2004). Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm. Am J Trop Med Hyg, 70(2):105-9.

Smith SJ, Wigley T (2006). Multi-gas forcing stabilization with Minicam. Energy J, 27:373-91.

Lindsay S, Parson L, Thomas C (1998). Mapping the range and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proc Biol Sci, 265(1399):847-54.

Organization WH (2000). The African Summit on Roll Back Malaria, Abuja, Nigeria. Geneva: WHO. http://apps.who.int/iris/handle/10665/67815

Malvy D, Pistone T, Rezvani A, et al (2006). Risk of malaria among French adult travellers. Travel Med Infect Dis, 4(5):259-69.

Marj AA, Mobasheri MR, Valadanzouje MJ, et al (2008). Using satellite images in determination of malaria outbreaks potential region. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5BF2DD11CF23EA89E6F46366DCCBD736?doi=10.1.1.672.6710&rep=rep1&type=pdf

Murtaza G, Memon IA, Memon AR, et al (2009). Malaria morbidity in Sindh and the plasmodium species distribution. Pak J Med Sci, 25(4):646-9.

Nath RK, Deb SK (2010). Water-body area extraction from high resolution satellite images-an introduction, review, and comparison. Int J Image Proc, 3(6):353-72.

Nihei N, Hashida Y, Kobayashi M, et al (2002). Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing. Jpn J Infect Dis, 55(5):160-6.

Carpenter CC, Pearson GW, Mitchell VS, et al (1991). Malaria: obstacles and opportunities. National Academies Press.

Omumbo J, Hay S, Snow R, et al (2005). Modelling malaria risk in East Africa at high‐spatial resolution. Trop Med Int Health,10(6):557-66.

Craig MH, Snow R, le Sueur D (1999). A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today,15(3):105-11.

Organization WH (2010). Global tuberculosis control: WHO report 2010: World Health Organization. http://apps.who.int/iris/handle/10665/44425

Moss WJ, Hamapumbu H, Kobayashi T, et al (2011). Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey. Malar J,10:163.

Adeola AM, Olwoch JM, Botai JO, et al (2017). Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa. S Afr Geogr J, 99(1):14-28.

Adde A, Roux E, Mangeas M, et al (2016). Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data. PLoS One,11(10):e0164685.

Roiz D, Ruiz S, Soriguer R, et al (2015). Landscape Effects on the Presence, Abundance and Diversity of Mosquitoes in Mediterranean Wetlands. PLoS One,10(6):e0128112.

Published
2018-08-29
How to Cite
1.
MAZHER MH, IQBAL J, MAHBOOB MA, ATIF I. Modeling Spatio-temporal Malaria Risk Using Remote Sensing and Environmental Factors. IJPH. 47(9):1280-9.
Section
Original Article(s)