Present and Future Distribution Model using MaxEnt: A Risk Map for Dengue Haemorrhagic Fever based on Aedes aegypti Mosquitoes Distribution in Malang Region, East Java, Indonesia

  • Zulfaidah Penata Gama Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia https://orcid.org/0000-0001-7065-5756
  • Bagyo Yanuwiadi Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia
  • Puji Rahayu Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia
  • Rafi Jauhar Khalil Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia
  • Miftah Farid Assiddiqy Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia
  • Muhammad Asyraf Rijalullah Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia https://orcid.org/0000-0002-2136-2698
  • Nia Kurniawan Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran, Malang City, East Java, 65145, Indonesia
Keywords: Aedes aegypti, Dengue Haemorrhagic Fever, Species Distribution Model, Malang Region

Abstract

The prevalence of Dengue Haemorrhagic Fever (DHF), a disease prevalent in countries with tropical and sub-tropical climates, including Indonesia, has exhibited a notable increase over the past two decades. A study case of a region experiencing this surge is Malang Region, which situated in East Java. The transmission of DHF within individual human is facilitated by the existence of Ae. aegypti, which serves as one of the intermediate vector mosquitoes. MaxEnt modelling was employed to analyse the niche and distribution of Ae. aegypti. The results of this study demonstrated that the integration of environmental and anthropogenic variables in a combination model provided  more comprehensive approach for comprehending the niche and distribution patterns of Ae. aegypti compared to relying only regarding a climatic model. Areas characterised by higher temperatures, high population density, and limited vegetation cover possess the inherent capacity to serve as suitable habitats for Ae. aegypti. According to the modelling results, the distribution of Ae. aegypti in Malang region currently encompasses approximately 14.5 % (545.5 km2) of the total area. It is projected that this distribution can potentially expand to 15.5 % (568.9 km2) by the year 2040. Several sub-districts, namely Klojen, Blimbing, Sukun, Lowokwaru, Kedungkandang, Pakisaji, and Kepanjen, have been classified as high-risk areas that require special concern. The combination model of environmental variables and anthropogenic variables provide more comprehensive approach to understand the niche and the distribution patterns of Ae. aegypti in Malang Region compared to relying solely on climate models.

References

Agustin, I., Tarwotjo, U. & Rahadian, R., 2017. Perilaku bertelur dan siklus hidup Aedes aegypti pada berbagai media air. Jurnal Biologi, 6(4), pp.71-81

Alto, B.W., Bettinardi, D.J. & Ortiz, S., 2015. Interspecific larval competition differentially impacts adult survival in dengue vectors. J. Med. Entomol. 52, pp.163–70. doi: 10.1093/jme /tju062.

Arana-Guardia, R. et al., 2014. Stormwater drains and catch basins as sources for production of Aedes aegypti and Culex quinquefasciatus. Acta Trop., 134, pp.33–42. doi: 10.1016/j.actatropica 2014.01.011.

Banerjee, S., Aditya, G. & Saha, G.K., 2015. Household Wastes as Larval Habitats of Dengue Vectors: Comparison between Urban and Rural Areas of Kolkata, India. PLoS One, 10(10), e0138082. doi: 10.1371/journal.pone.0138082.

Bara, J. et al., 2015. Effect of larval competition on extrinsic incubation period and vectorial capacity of Aedes albopictus for dengue virus. PLoS One, 10, e0126703. doi: 10.1371/journal.pone.0126703.

Barrera, R. et al., 2008. Unusual productivity of Aedes aegypti in septic tanks and its implications for dengue control. Med. Vet. Entomol., 22, pp.62–69. doi: 10.1111/ j.13652915.2008.00720.x.

Becker, N. et al., 2020. Mosquitoes: Identification, Ecology, Control (Third Edition), Switzerland: Springer. doi: 10.1007/978-3-030-11623-1

BPS-Statistics Indonesia Malang Regency, 2021, ‘Penduduk, Laju Pertumbuhan Penduduk, Distribusi Persentase Penduduk Kepadatan Penduduk, Rasio Jenis Kelamin Penduduk Menurut Kecamatan di Kabupaten Malang, 2020’ in Badan Pusat Statistik Kabupaten Malang, viewed from https://malangkab.bps.go.id.

BPS-Statistics Indonesia Batu Municipality, 2022, ‘Jumlah Penduduk Menurut Jenis Kelamin dan Kecamatan di Kota Batu’ in Badan Pusat Statistik Kota Batu, viewed from https://batukota.bps.go.id.

BPS-Statistics Indonesia Malang Municipality, 2023, ‘Jumlah Penduduk Menurut Kecamatan dan Jenis Kelamin di Kota Malang, 2000-2023’ in Badan Pusat Statistik Kota Malang, viewed from https://malangkota.bps.go.id.

Capinha, C., Rocha, J. & Sousa, C.A. 2014. Macroclimate determines the global range limit of Aedes aegypti. Ecohealth, 11(3), pp.420-428. doi: 10.1007/s10393-014-0918-y.

Dickens, B.L. et al., 2018. Determining environmental and anthropogenic factors which explain the global distribution of Aedes aegypti and Ae. albopictus. BMJ Glob. Health., 3(4), e000801. doi: 10.1136/bmjgh-2018-000801

East Java Provincial Health Service, 2021, ‘Profil Kesehatan Provinsi Jawa Timur tahun 2020’ in Dinas Kesehatan Pemerintah Provinsi Jawa Timur, viewed from https://dinkes.jatimprov.go.id.

Eisen, L. & Moore, C.G., 2013. Aedes (Stegomyia) aegypti in the continental United States: a vector at the cool margin of its geographic range. J Med Entomol., 50, pp.467–78. doi: 10.1603/ME12245.

Elith, J. et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, pp.129–151. doi: 10.1111/j.2006.09067590.04596.x.

Elith, J. et al., 2010. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57. doi: 10.1111/j.1472-4642.2010.00725.x

Fick, S.E. & Hijmans, R.J., 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), pp.4302-4315. doi: 10.1002/joc.5086.

Früh, L. et al., 2018. Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. Ecol Model., 388, pp.136–144. doi: 10.1016/j.ecolmodel.2018.08.011.

Gama, Z.P. & Salsabila, R.J., 2021. Spatial distribution of dengue vector mosquitoes in Malang City, East Java, Indonesia (case study: Jatimulyo Village, Lowokwaru Sub-district). IOP Conference Series Earth and Environmental Science, 743, 012064. doi: 10.1088/1755-1315/743/1/012064

Gama, Z.P. et al., 2013. Toxicity studies for indigenous Bacillus thuringiensis isolates from Malang city, East Java on Aedes aegypti larvae. Asian Pac. J. Trop. Biomed., 3(2), pp.111-117. doi: 10.1016/S2221-1691(13)60034-9

Gomes, A.C. et al., 2005. Anthropophilic activity of Aedes aegypti and of Aedes albopictus in area under control and surveillance. Rev. Saude Publica., 39(2), pp.206-210. doi: 10.1590/S0034-89102005000200010.

Hansen, M.C. et al., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), pp.850-853. doi: 10.1126/science.1244693.

Harapan, H. et al., 2019. Dengue viruses circulating in Indonesia: a systematic review and phylogenetic analysis of data from five decades. Reviews In Medical Virology, 29(4), e2037. doi: 10.1002/rmv.2037.

Hastie, T., Tibshirani, R. & Friedman, J., 2009. Unsupervised learning. In The Elements of Statistical Learning. New York: Springer, pp. 485–585.

Hijmans, R.J., 2023. raster: Geographic Data Analysis and Modeling. R package version 3.6-23.

Hribar, L. et al., 2004. Mosquito larvae (Culicidae) and other Diptera associated with containers, storm drains, and sewage treatment plants in the Florida Keys, Monroe County, Florida. Fla. Entomol, 87, pp.199–203. doi: 10.1653/0015-4040(2004)087[0199:MLCAOD]2.0.CO;2.

IPCC, 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

Iwamura, T., Guzman-Holst, A. & Murray, K.A., 2020. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nature Communications, 11(1), 2130. doi: 10.1038/s41467-020-16010-4.

Jarvis, A. et al., 2008, ‘Hole-filled SRTM for the globe version 3, CGIAR-CSI SRTM 90m database’ in CGIAR CSI, viewed 10 January 2023, from https://srtm.csi.cgiar.org

Khan, A.M. et al., 2022. MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia. Forest, 13(5), pp.715. doi: 10.3390/f13050715.

Knutson, T.R. & Zeng, F. 2018. Model assessment of observed precipitation trends over land regions: detectable human influences and possible low bias in model trends. Journal of Climate, 31, pp.4617–4637. doi: 10.1175/JCLI-D-17-0672.1.

Kraemer, M.U.G. et al., 2015. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife, 4, e08347. doi: 10.7554/eLife.08347.

Liu, C. & Allan, R.P., 2013. Observed and simulated precipitation responses in wet and dry regions 1850–2100. Environ. Res. Lett., 8, 34002. doi: 10.1088/1748-9326/8/3/034002.

Lozano-Fuentes, S. et al., 2012. The Dengue Virus Mosquito Vector Aedesaegypti at High Elevation in Mexico. The American journal of tropical medicine and hygiene, 87(5), pp.902-909. doi: 10.4269/ajtmh.2012.12-0244.

Martin, E. et al., 2019. Surveillance of Aedes aegypti indoors and outdoors using Autocidal Gravid Ovitraps in South Texas during local transmission of Zika virus, 2016 to 2018. Acta Tropica., 192, pp.129-137. doi: 10.1016/j.actatropica.2019.02.006.

Meinshausen, M. et al., 2020. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev., 13, pp.3571–3605, doi: 10.5194/gmd-13-3571-2020.

Menéndez, R. et al., 2007. Direct and indirect effects of climate and habitat factors on butterfly diversity. Ecology, 88, pp.605–611. doi: 10.1890/06-0539.

Menéndez, R., 2007. How are insects responding to global warming? Tijdschr. Entomol., 150, 355.

Mengko, S. & Tuda, J.S.B., 2016. Deteksi porfirin besi pada pakan darah nyamuk liar antropofilik menggunakan uji benzidine. Jurnal e-Biomedik (eBm), 4(2), pp.1-7. doi: 10.35790/ ebm.v4i2.14660.

Mohammed, A. & Chadee, D.D., 2011. Effects of different temperature regimens on the development of Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes. Acta tropica, 119(1), pp.38-43. doi: 10.1016/j.actatropica.2011.04.004.

Naimi, B. et al., 2014. Where is positional uncertainty a problem for species distribution modelling. Ecography. 37, pp.191-203. doi:10.1111/j.1600-0587.2013.00205.x.

Obenauer, J.F., Joyner, T.A. & Harris, J.B. 2017. The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infection diseases. Trop. Med. Health., 45, 38, doi: 10.1186/s41182-017-0078-1.

Peterson, A.T. et al., 2011. Ecological Niches and Geographic Distributions, Princeton University Press.

Phillips, S. J., Anderson, R.P. & Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), pp.231-259. doi: 10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudik, M. & Schapire, R.E., 2023, ‘Maxent software for modeling species niches and distributions (Version 3.4.1)’ in American Museum of Natural History, viewed from http://biodiversityinformatics.amnh.org/open_source/maxent.

Pradhan, P. & Setyawan, A., 2021. Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context. Asian Journal of Forestry, 5(2), pp.111-122. doi: 10.13057/asianjfor/r050207.

Radosavljevic, A. & Anderson, R.P., 2014. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. Journal of biogeography, 41(4), pp.629-643. doi: 10.1111/jbi.12227.

Ratnasari, A. et al., 2020. The ecology of Aedes aegypti and Aedes albopictus larvae habitat in coastal areas of South Sulawesi, Indonesia. BIODIVERSITAS, 21(10), pp.4648-4654. doi: 10.13057/biodiv/d211025.

Richards, J.D., McMillan, J.M. & Smith, D.L., 2019. Socioeconomic factors influencing mosquito control and prevalence of mosquito-borne diseases. PLoS Neglected Tropical Diseases, 13(10), e0007408. doi: 10.1371/journal.pntd.0007408

Rueda, L.M. & Peña, J., 2022. Effects of urbanization on mosquito populations and vector control programs. Insects, 13(4). 343. doi: 10.3390/insects13040343

Sallam, M.F. et al., 2017. Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping. Int. J. Environ. Res. Public. Health, 14(10), 1230. doi: 10.3390/ijerph14101230.

Samson, D.M. et al., 2015. New baseline environmental assessment of mosquito ecology in northern Haiti during increased urbanization. Journal of Vector Ecology, 40(1), pp.46-58. doi: 10.1111/ jvec.12131.

Santos, J. & Meneses, B.M., 2017. An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus. Acta Trop., 168, pp.80-90. doi: 10.1016/j.actatropica.2017.01.015

Schurer, A.P. et al., 2020. Human influence strengthens the contrast between tropical wet and dry regions. Environ. Res. Lett., 15(10), 104026. doi: 10.1088/ 1748-9326/ab83ab.

Souza, R.L. et al., 2023. Density of Aedes aegypti (Diptera: Culicidae) in a low-income Brazilian urban community where dengue, Zika, and chikungunya viruses co-circulate. Parasites & Vectors, 16(1), 159. doi: 10.1186/s13071-023-05766-5.

Steinhoff, D.F. et al., 2016. WHATCH'EM: A weather-driven energy balance model for determining water height and temperature in container habitats for Aedes aegypti. Earth interactions, 20, 24. doi: 10.1175/EI-D-15-0048.1.

Stephenson, C. et al., 2022. Imported Dengue Case Numbers and Local Climatic Patterns Are Associated with Dengue Virus Transmission in Florida, USA. Insect, 13(2), 163. doi: 10.3390/insects13020163.

Sulistyawati, 2020. Dengue prevention and control in Indonesia: A case study in Yogyakarta City. Umea University, Umea.

Susilawaty, A. et al., 2021. Climate factors and dengue fever occurrence in Makassar during period of 2011–2017. Gaceta Sanitaria, 35(2), pp.S408-S412. doi: 10.1016/j.gaceta.2021.10.063.

Swaidatul, M.A.F., Wibowo, R.C.A. & Luthfin, A., 2022. Eksplorasi Sebaran Penyakit Demam Berdarah Dengue (DBD) dan Pneumonia di Kota Malang. JUMANTIK (Jurnal Ilmiah Penelitian Kesehatan), 7(2), pp.134-140. doi: 10.30829/jumantik.v7i2.10402

Thani, S.K.S.O., Mohammad, N.H.N. & Abdullah, S.M.S., 2017. Influence of Urban Landscapes to Microclimatic Variances in a Tropical City. Asian Journal of Behavioural Studies, 2(7), pp.31-41. doi: 10.21834/ajbes.v2i7.40.

The Indonesia Ministry of Health, 2017. Pedoman pencegahan dan pengendalian demam berdarah dengue di Indonesia. Directorate General of Disease Prevention and Control. Jakarta.

The Indonesia Ministry of Health, 2020. Profil Kesehatan Indonesia Tahun 2019. The Indonesia Ministry of Health. Jakarta

Tolinggi, S. & Dengo, M.R. 2019. Prediction Model of Dengue Hemorrhagic Fever Incidence Using Climatic Factors in Kabupaten Gorontalo. Jurnal Kesehatan Lingkungan, 11(4), pp.348-353. doi: 10.20473/jkl.v11i4.2019.348-353

Tuiskunen, A. & Lundkvist, A., 2013. Dengue viruses – an overview. Infect. Ecol. Epidemiol., 3, 19839. doi: 10.3402/iee.v3i0.19839

Upshur, I.F. et al., 2019. Temperature and Sugar Feeding Effects on the Activity of a Laboratory Strain of Aedes aegypti. Insect, 10(10), 347. doi: 10.3390/insects10100347.

Valdez, L.D., Sibona, G.J. & Condat, C.A., 2018. Impact of rainfall on Aedes aegypti populations. Ecological Modelling, 385, pp.96-105. doi: 10.1016/j.ecolmodel.2018.07.003

Wan, H. et al., 2014. Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence. Climate Dynamics, 45, pp.1713–1726. doi: 10.1007/s00382-014-2423-y.

Witten, I.H. et al., 2016. Data Mining Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publisher Inc.

Published
2025-01-24
How to Cite
Gama, Z. P., Bagyo Yanuwiadi, Puji Rahayu, Rafi Jauhar Khalil, Miftah Farid Assiddiqy, Muhammad Asyraf Rijalullah and Nia Kurniawan (2025) “Present and Future Distribution Model using MaxEnt: A Risk Map for Dengue Haemorrhagic Fever based on Aedes aegypti Mosquitoes Distribution in Malang Region, East Java, Indonesia”, Journal of Tropical Biodiversity and Biotechnology, 10(1), p. jtbb12678. doi: 10.22146/jtbb.12678.
Section
Research Articles