Euclidean Distance Modeling of Musi River in Controlling the Dengue Epidemic Transmission in Palembang City

Authors

  • Cipta Estri Sekarrini Department of Geography Education, Faculty of Social Science, Universitas Negeri Malang, Malang, East Java, Indonesia https://orcid.org/0000-0002-9204-1314
  • Sumarmi Sumarmi Department of Geography Education, Faculty of Social Science, Universitas Negeri Malang, Malang, East Java, Indonesia
  • Syamsul Bachri Department of Geography Education
  • Didik Taryana Department of Geography Education, Faculty of Social Science, Universitas Negeri Malang, Malang, East Java, Indonesia
  • Eggy Arya Giofandi Department of Geography, Faculty of Social Science, Universitas Negeri Padang, Padang, Indonesia https://orcid.org/0000-0002-9204-1314

DOI:

https://doi.org/10.3889/oamjms.2022.9125

Keywords:

Ecology, Dengue outbreak, Euclidean distance, Mosquito

Abstract

BACKGROUND: Various attempts have been made to control the population of Aedes aegypti with the help of chemicals or by engineering Wolbachia pipentis, an obligate intracellular bacterium that is passed down through DENV and arbovirus infections to manipulate the monthly average reproductive yield. This study reviews the phenomenon of the river border area which is one of the habitats for the Aedes aegypti mosquito in the Musi River, Palembang City.

AIM: The application of the euclidean distance method in this study was carried out to determine the environmental exposure of settlements along the river basin area.

METHODS: The research methodology was carried out objectively related to data on dengue incidence in 2019. It was carried out by taking location coordinates through the application of geographic information systems and the use of satellite imagery for data acquisition of existing buildings. This stage is followed by bivariate statistical calculations using the application of WoE where the probability value of the measurement is described using the Area Under Curve. Processing and accumulation carried out with existing buildings will result in a calculation of the estimated size of the exposure area.

RESULTS: The results obtained provide information, where the natural breaks jeanks value of 0.007-0.016 range results in 1465ha of heavily exposed building area. The value of the temporary bivariate statistical calculation will produce an AUC probability number of 0.44 which describes the relationship between the Musi river and the findings of dengue symptoms in the sub-districts around the Musi river border area, Palembang City. Swamp soil conditions are vulnerable to being a habitat where Aedes aegypti larvae are found.

CONCLUSIONS: Based on the analysis that we obtained from the population of dengue incidence and the condition of the river basin area showed a significant structure with the distribution of dengue incidence, it is known that the presence of buildings on the river Musi banks has a greater risk of infectious diseases transmissions and natural disasters ranging from sanitation, hygiene, flooding to river erosion.

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References

Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504-7. http://doi.10.10.1038/nature12060 PMid:23563266 DOI: https://doi.org/10.1038/nature12060

Laureano-Rosario A, Duncan A, Mendez-Lazaro P, Garcia- Rejon J, Gomez-Carro S, Farfan-Ale J, et al. Application of artificial neural networks for dengue fever outbreak predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico. Trop Med Infect Dis. 2018;3(1):1-16. http://doi.10.10.3390/tropicalmed3010005 PMid:30274404 DOI: https://doi.org/10.3390/tropicalmed3010005

Bhoomiboonchoo P, Gibbons RV, Huang A, Yoon IK, Buddhari D, Nisalak A, et al. The spatial dynamics of dengue virus in Kamphaeng Phet, Thailand. PLoS Negl Trop Dis. 2014;8(9):e3138. http://doi.10.10.1371/journal.pntd.0003138 PMid:25211127 DOI: https://doi.org/10.1371/journal.pntd.0003138

Purwanto P, Utaya S, Handoyo B, Bachri S, Astuti IS, Sastro K, et al. Spatiotemporal analysis of COVID-19 spread with emerging hotspot analysis and space-time cube models in East Java, Indonesia. ISPRS Int J Geo Inform. 2021;10(3):1-22. DOI: https://doi.org/10.3390/ijgi10030133

Deb S, Milagros C, Acebedo L, Dhanapal G, Matthew C, Heng C. An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions. J Health Soc Sci. 2017;23:257-72.

Tsheten T, Clements AC, Gray DJ, Wangdi K. Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan. PLoS Negl Trop Dis. 2021;15(2):e0009021. http://doi.10.10.1371/journal.pntd.0009021 PMid:33566797 DOI: https://doi.org/10.1371/journal.pntd.0009021

Kraemer MU, Perkins TA, Cummings DA, Zakar R, Hay SI, Smith DL, et al. Big city, small world: Density, contact rates, and transmission of dengue across Pakistan. J R Soc Interface. 2015;12(111):20150468. http://doi.10.10.1098/rsif.2015.0468 PMid:26468065 DOI: https://doi.org/10.1098/rsif.2015.0468

Kucharski AJ, Kama M, Watson CH, Aubry M, Funk S, Henderson AD, et al. Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji. Elife. 2018;7:e34848. http://doi.10.10.7554/eLife.34848 PMid:30103854 DOI: https://doi.org/10.7554/eLife.34848

Satoto TB, Pascawati NA, Purwaningsih W, Josef HK, Purwono P, Rumbiwati, et al. Occurrence of natural vertical transmission of “ zika like virus ” in Aedes aegypti Mosquito in Jambi City. Kesmas Natl Public Health J. 2019;13(4):189-94. DOI: https://doi.org/10.21109/kesmas.v13i4.2709

Wijayanti SP, Porphyre T, Chase-Topping M, Rainey SM, McFarlane M, Schnettler E, et al. The importance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indonesia. PLoS Negl Trop Dis. 2016;10(9):e0004964. http://doi.10.10.1371/journal.pntd.0004964 PMid:27603137 DOI: https://doi.org/10.1371/journal.pntd.0004964

Bonham-Carter GF. Geographic Information Systems for Geoscientists : Modelling with GIS. New York: Elsevier Science Inc.; 1994. p. 402.

Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, et al. Landslide susceptibility mapping using different GIS-based bivariate models. Water. 2019;11(7):1-22. DOI: https://doi.org/10.3390/w11071402

Simoonga C, Utzinger J, Brooker S, Vounatsou P, Appleton CC, Stensgaard AS, et al. Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa. Parasitology. 2009;136(13):1683-93. http://doi.10.10.1017/S0031182009006222 PMid:19627627 DOI: https://doi.org/10.1017/S0031182009006222

Rushton G. Public health, GIS, and spatial analytic tools. Annu Rev Public Health. 2003;24:43-56. http://doi.10.10.1146/annurev.publhealth.24.012902.140843 PMid:12471269 DOI: https://doi.org/10.1146/annurev.publhealth.24.012902.140843

Sapri NN, Yaacob WF, Wah YB, Abdul Rahim SS. Spatio-temporal clustering of dengue incidence. Univers J Public Health. 2021;9(3):120-30. DOI: https://doi.org/10.13189/ujph.2021.090303

Morin CW, Comrie AC, Ernst K. Climate and dengue transmission: Evidence and implications. Environ Health Perspect. 2013;121(11-12):1264-72. http://doi.10.10.1289/ehp.1306556 PMid:24058050 DOI: https://doi.org/10.1289/ehp.1306556

Colón-González FJ, Fezzi C, Lake IR, Hunter PR. The effects of weather and climate change on dengue. PLoS Negl Trop Dis. 2013;7(11):e2503. http://doi.10.10.1371/journal.pntd.0002503 PMid:24244765 DOI: https://doi.org/10.1371/journal.pntd.0002503

Pham DN, Nellis S, Sadanand AA, Jamil J, Binti A, Khoo JJ, et al. A Literature Review of Methods for Dengue Outbreak Prediction. In: The Eighth International Conference on Information, Process, and Knowledge Management; 2016. p. 7-13.

Rogers DJ. Dengue: Recent past and future threats. Philos Trans R Soc B Biol Sci. 2015;370:20130562. http://doi.10.10.1098/rstb.2013.0562 PMid:25688021 DOI: https://doi.org/10.1098/rstb.2013.0562

Lee J, Bednarz R. Components of spatial thinking: Evidence from a spatial thinking ability test. J Geog. 2012;111(1):15-26. DOI: https://doi.org/10.1080/00221341.2011.583262

Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 2003;24(3):583-94. DOI: https://doi.org/10.1080/01431160304987

Wilson JP. Geographic Information Science & Technology Body of Knowledge 2.0 Project; 2014.

Guo WP, Li X, Wang XF. Epidemics and immunization on Euclidean distance preferred small-world networks. Phys A Stat Mech Appl. 2007;380(1-2):684-90. DOI: https://doi.org/10.1016/j.physa.2007.03.007

McEvoy CS, Ross-Li D, Norris EA, Ricca RL, Gow KW. From far and wide: Geographic distance to pediatric surgical care across Canada. J Pediatr Surg. 2020;55(5):908-12. http://doi.10.10.1016/j.jpedsurg.2020.01.036 PMid:32063366 DOI: https://doi.org/10.1016/j.jpedsurg.2020.01.036

Shao Z, Ma Q, Liu X, Ma J. An Algorithm for Shortest Raster Distance in Euclidean Space with Obstacles. In: 19th International Conference on Geoinformatics; 2011. p. 1-4. DOI: https://doi.org/10.1109/GeoInformatics.2011.5981080

Latif S, Beck F. Interactive map reports summarizing bivariate geographic data. Vis Informatics. 2019;3(1):27-37. DOI: https://doi.org/10.1016/j.visinf.2019.03.004

Pradhan B. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens. 2010;38(2):301-20. DOI: https://doi.org/10.1007/s12524-010-0020-z

Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR. A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards. 2016;83(2):947-87. DOI: https://doi.org/10.1007/s11069-016-2357-2

Patriadi A, Asih R, Soemitro A, Warnana DD, Wardoyo W, Mukunoki T, et al. The influence of sembayat weir on sediment transport rate in the estuary of Bengawan Solo River, Indonesia. Int J GEOMATE. 2021;20(81):35-43. DOI: https://doi.org/10.21660/2021.81.j2072

Savitri YR, Kakimoto R, Anwar N, Wardoyo W, Suryani E. Reliability of 2D hydrodynamic model on flood inundation analysis. Int J GEOMATE. 2021;21(83):65-71. DOI: https://doi.org/10.21660/2021.83.j2073

Rosid MS, Prastama RA, Yusuf M, Daud Y, Riyanto A. Monitoring of Jakarta subsidence applying 4D microgravity survey between 2014 and 2018. Int J GEOMATE. 2021;20(79):132-8. DOI: https://doi.org/10.21660/2021.79.j2031

Ansori MB, Damarnegara AA, Margini NF, Nusantara DA. Flood inundation and dam break analysis for disaster risk mitigation (a Case Study of Way Apu Dam). Int J GEOMATE. 2021;21(84):85-92. DOI: https://doi.org/10.21660/2021.84.j2130

Aribawa TM, Mardjono A, Soegiarto S, Moe IR, Sihombing YI, Rizaldi A, et al. Assessment of flood propagation due to several dams break in banten province. Int J GEOMATE. 2021;20(81):185-90. DOI: https://doi.org/10.21660/2021.81.j2082

Purwanto P, Utaya S, Handoyo B, Bachri S, Yulistiya D, Amin S. The spatial thinking ability students on the character of urban and rural environments in solving population problems. Rev Int Geogr Educ Online. 2021;11(3):636-52. DOI: https://doi.org/10.33403/rigeo.877708

Yanti E, Hermon D, Barlian E, Dewata I, Umar I. Directions for sanitation-based environmental structuring using AHP for the prevention of Diarrhea in Pagar Alam City – Indonesia. Int J Manag Humanit. 2020;4(9):25-9. DOI: https://doi.org/10.35940/ijmh.I0848.054920

Marni L, Barlian E, Hermon D, Dewata I, Umar I. Service policy of puskesmas based on dempo volcano disaster mitigation using AHP in Pagar Alam City – Indonesia. Int J Manag Humanit. 2020;4(9):20-4. DOI: https://doi.org/10.35940/ijmh.I0847.054920

Sumarmi S, Bachri S, Baidowi A, Aliman M. Problem-based service learning’s effect on environmental concern and ability to write scientific papers. Int J Instr. 2020;13(4):161-76. DOI: https://doi.org/10.29333/iji.2020.13411a

Yuniarti E, Hermon D, Dewata I, Barlian E, Iswamdi U. Mapping the high risk populations against coronavirus disease 2019 in Padang West Sumatra Indonesia. Int J Sci High Technol. 2020;20(2):50-8.

Armaita A, Barlian E, Hermon D, Dewata I, Umar I. Policy model of community adaptation using AHP in the malaria endemic region of Lahat Regency-Indonesia. Int J Manag Humanit. 2020;4(9):44-8. DOI: https://doi.org/10.35940/ijmh.I0855.054920

Angriani P, Sumarmi, Ruja IN, Bachri S. River management: The importance of the roles of the public sector and community in river preservation in Banjarmasin (A Case Study of the Kuin River, Banjarmasin, South Kalimantan – Indonesia). Sustain Cities Soc. 2018;43:11-20. DOI: https://doi.org/10.1016/j.scs.2018.08.004

Indika PM, Hermon D, Dewata I, Barlian E, Umar I. Malaria Spatial Pattern as an Outbreak Mitigation Effort in South Bengkulu Regency; June 2020. p. 214-8.

Esu E, Lenhart A, Smith L, Horstick O. Effectiveness of peridomestic space spraying with insecticide on dengue transmission; systematic review. Trop Med Int Health. 2010;15(5):619-31. http://doi.10.10.1111/j.1365-3156.2010.02489.x PMid:2021476 DOI: https://doi.org/10.1111/j.1365-3156.2010.02489.x

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Published

2022-03-26

How to Cite

1.
Sekarrini CE, Sumarmi S, Bachri S, Taryana D, Giofandi EA. Euclidean Distance Modeling of Musi River in Controlling the Dengue Epidemic Transmission in Palembang City. Open Access Maced J Med Sci [Internet]. 2022 Mar. 26 [cited 2024 Mar. 28];10(G):422-9. Available from: https://oamjms.eu/index.php/mjms/article/view/9125

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