Development of Web-Based GIS Alert System for Informing Environmental Risk of Dengue Infections in Major Cities of Pakistan
Abstract
Dengue is one of the emerging major public health problems, and its incidence varies with climate conditions. It affects millions of people's lives owing to unusual socioeconomic conditions and epidemiological factors. This study was designed to build a web-based GIS alert system for dengue data management and analysis which would centralize information and make it accessible to all relevant stakeholders before, during, and after crises. Three geographical regions were selected in this study. The user interface of the dengue alert system was developed based upon MapGuide. Results indicate that risk level was mainly associated with Breteau Index. Karachi and Lahore were at their highest risk, i.e., level 4. Islamabad and Chakwal were also at the highest risk, i.e., level 4. Attock had high risk, i.e., level 3 followed by Haripur with minimal level 1. The high Breteau Index showed a direct relationship to high potential transmission of dengue outbreaks, a more significant peak of dengue was the result of monsoons, while smaller peaks were observed due to domestic water storage. Hence, it was concluded that monsoon is the best suitable season for the development of dengue. Web-Based GIS Alert System for dengue data management and analysis was developed, centralizing information and making it accessible to all relevant stakeholders before, during & after a crisis. This program creation will provide a more analytical forum for advising multiple levels of risk and an experimental method for measuring the effect of different factors on risk level distribution by adjusting the component's weighting.
Keywords : Dengue; GIS analysis; GUI; Alert system; Breteau index; Weighted overlay
Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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Adak, S., & Jana, S. (2021). A study on stegomyia indices in dengue control: a fuzzy approach. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 25(1), 699–709. https://doi.org/10.1007/s00500-020-05179-x.
Asif, M., Tripathi, N. K., & Ahmed, S. (2013). Towards Near Real Time Public Health Surveillance (A Decision Support System for Public Health Surveillance). International Journal of Computer Applications, 61(21), 45–50.
Attaway, D. F., Jacobsen, K. H., Falconer, A., Manca, G., & Waters, N. M. (2016). Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools). Acta tropica, 158, 248-257. https://doi.org/10.1016/j.actatropica.2016.02.018.
Bajwala, V. R., John, D., Rajasekar, D., Eapen, A., & Murhekar, M. V. (2020). Burden of Dengue with Related Entomological and Climatic Characteristics in Surat City, Gujarat, India, 2011–2016: An Analysis of Surveillance Data. The American journal of tropical medicine and hygiene, 103(1), 142-148. https://doi.org/10.4269/ajtmh.19-0967.
Bowman, L. R., Runge-Ranzinger, S., & McCall, P. J. (2014). Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence. PLoS Neglected Tropical Diseases, 8(5). https://doi.org/10.1371/journal.pntd.0002848.
Bozdogan Sert, E., Kaya, E., Adiguzel, F., Cetin, M., Gungor, S., Zeren Cetin, I., & Dinc, Y. (2021). Effect of the surface temperature of surface materials on thermal comfort: a case study of Iskenderun (Hatay, Turkey). Theoretical and Applied Climatology, 1–11. https://doi.org/10.1007/s00704-021-03524-0.
Cetin, M. (2019). The effect of urban planning on urban formations determining bioclimatic comfort area’s effect using satellitia imagines on air quality: a case study of Bursa city. Air Quality, Atmosphere & Health, 12(10), 1237-1249. https://doi.org/10.1007/s11869-019-00742-4.
Cetin, M., Adiguzel, F., Gungor, S., Kaya, E., & Sancar, M. C. (2019). Evaluation of thermal climatic region areas in terms of building density in urban management and planning for Burdur, Turkey. Air Quality, Atmosphere & Health, 12(9), 1103-1112. https://doi.org/10.1007/s11869-019-00727-3.
Cetin, M. (2015). Using GIS analysis to assess urban green space in terms of accessibility: case study in Kutahya. International Journal of Sustainable Development & World Ecology, 22(5), 420-424. https://doi.org/10.1080/13504509.2015.1061066.
Chang, A. Y., Parrales, M. E., Jimenez, J., Sobieszczyk, M. E., Hammer, S. M., Copenhaver, D. J., & Kulkarni, R. P. (2009). Combining google earth and GIS mapping technologies in a dengue surveillance system for developing countries. International Journal of Health Geographics, 8(1), 1–11. https://doi.org/10.1186/1476-072X-8-49.
Gubler, D. J. (2006). Dengue/dengue haemorrhagic fever: history and current status. In Novartis foundation symposium (Vol. 277, p. 3). Chichester; New York; John Wiley.
Gubler, D. J., Ooi, E. E., Vasudevan, S., & Farrar, J. (2014). Dengue and dengue hemorrhagic fever. Oxfordshire : CABI.
Gungor, S., Cetin, M., & Adiguzel, F. (2020). Calculation of comfortable thermal conditions for Mersin urban city planning in Turkey. Air Quality, Atmosphere and Health, 1–8. https://doi.org/10.1007/s11869-020-00955-y.
Kahn, T. C., Cameron, J. T., & Giffen, M. B. (1975). Methods and evaluation in clinical and counseling psychology. Elmsford, NY: Pergamon Press.
Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and Ecological Risk Assessment, 25(6), 1521–1530. https://doi.org/10.1080/10807039.2018.1470896.
Liyanage, P., Rocklöv, J., Tissera, H., Palihawadana, P., Wilder-Smith, A., & Tozan, Y. (2019). Evaluation of intensified dengue control measures with interrupted time series analysis in the Panadura Medical Officer of Health division in Sri Lanka: a case study and cost-effectiveness analysis. The Lancet. Planetary Health, 3(5), e211–e218. https://doi.org/10.1016/S2542-5196(19)30057-9.
Mukhtar, M., Tahir, Z., Baloch, T., Mansoor, F., & Kamran, J. (2011). Entomological investigations of dengue vectors in epidemic-prone districts of Pakistan during 2006–2010. World Health Organization Regional Publications. South East Asia Series.
Novotny, V., Miller, S. E., Hulcr, J., Drew, R. A. I., Basset, Y., Janda, M., … Weiblen, G. D. (2007). Low beta diversity of herbivorous insects in tropical forests. Nature, 448(7154), 692–695. https://doi.org/10.1038/nature06021.
Olubadewo-Joshua, O., & Ugom, K. M. (2019). Application of Geospatial Techniques in the Locational Planning of Health Care Centers in Minna, Nigeria. Geosfera Indonesia, 3(3), 59-72. https://doi.org/10.19184/geosi.v3i3.8754.
Shen, J. C., Luo, L., Li, L., Jing, Q. L., Ou, C. Q., Yang, Z. C., & Chen, X. G. (2015). The impacts of mosquito density and meteorological factors on dengue fever epidemics in Guangzhou, China, 2006-2014: A time-series analysis. Biomedical and Environmental Sciences, 28(5), 321–329. https://doi.org/10.3967/bes2015.046.
Sirisena, P., Noordeen, F., Kurukulasuriya, H., Romesh, T. A., & Fernando, L. K. (2017). Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: A GIS based evaluation for prediction of outbreaks. PloS One, 12(1), e0166806. https://doi.org/10.1371/journal.pone.0166806.
Burney, S. A., Barakzai, M. A. K., & James, S. E. (2020). Forecasting Monthly Maximum Temperature of Karachi City using Time Series Analysis. Pakistan Journal of Engineering, Technology & Science, 7(2). https://doi.org/10.22555/pjets.v7i2.2439.
Thompson, R. N., Gilligan, C. A., & Cunniffe, N. J. (2016). Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks. PLoS Computational Biology, 12(4). https://doi.org/10.1371/journal.pcbi.1004836.
Tran, B. L., Tseng, W. C., Chen, C. C., & Liao, S. Y. (2020). Estimating the threshold effects of climate on dengue: A case study of Taiwan. International Journal of Environmental Research and Public Health, 17(4), 1–17. https://doi.org/10.3390/ijerph17041392.
Udayanga, L., Gunathilaka, N., Iqbal, M. C. M., Najim, M. M. M., Pahalagedara, K., & Abeyewickreme, W. (2018). Empirical optimization of risk thresholds for dengue: An approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka. Parasites and Vectors, 11(1), 368. https://doi.org/10.1186/s13071-018-2961-y.
Wong, N. S., Law, C. Y., Lee, M. K., Lee, S. S., & Lin, H. (2007). An Alert System for Informing Environmental Risk of Dengue Infections. In P. C. Lai & A. S. H. Mak (Eds.), GIS for Health and the Environment: Development in the Asia-Pacific Region With 110 Figures (pp. 171–183). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-71318-0_12.
Zavlavsky, I. (2000). A New Technology for Interactive Online Mapping with Vector Markup and XML. Cartographic Perspectives, 0(37), 65-77–77. https://doi.org/10.14714/CP37.810.
Published
2021-04-25
How to Cite
ZAINAB, Naureen; TARIQ, Aqil; SIDDIQUI, Saima.
Development of Web-Based GIS Alert System for Informing Environmental Risk of Dengue Infections in Major Cities of Pakistan.
Geosfera Indonesia, [S.l.], v. 6, n. 1, p. 77-95, apr. 2021.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/20792>. Date accessed: 24 nov. 2024.
doi: https://doi.org/10.19184/geosi.v6i1.20792.
Section
Original Research Articles
