Comparison of Dengue Transmission in Lowland and Highland Area: Case Study in Semarang and Malang, Indonesia

Ilham Saiful Fauzi, Muhammad Fakhruddin, Nuning Nuraini, Karunia Putra Wijaya


Dengue is a potentially lethal mosquito-borne disease, regarded as the most dangerous disease in the world. It is also a major health issue in tropical and subtropical countries. Environmental characteristics and sociocultural are factors which play a role in the spread of dengue. Different landscape structure such as lowland and highland areas are possible to give different infection rate on dengue transmission. Semarang and Malang are densely populated areas in Java, which are selected to be our study areas. A mathematical model (SIR-UV) is adapted to describe dengue transmission. Spiral dynamic optimization is applied to convert monthly data to weekly in Malang and estimate the infection rate that minimized the deviation between dengue data and simulation. This method produces a good fitting to the data. We compare the pattern of dengue cases from the simulation in both cities. Furthermore, we identify seasonal variations of the cases via Fourier series of the infection rate. We also investigate the correlation between humidity, infection rate, and dengue cases in Semarang and Malang. It reveals that humidity influences infection rate in 1-3 weeks later and the infection rate produces dengue cases in the next four weeks.


Dengue, infection rate, comparison, lowland, highland, host-vector model, spiral optimization.

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Aguiar, M., Ballesteros, S., Kooi, B.W. and Stollenwerk, N., 2011. The role of seasonality and import in a minimalistic multi-strain dengue model capturing differences between primary and secondary infections: complex dynamics and its implications for data analysis. Journal of theoretical biology, 289, pp.181-196.

Aguiar, M., Paul, R., Sakuntabhai, A. and Stollenwerk, N., 2014. Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand. Epidemiology & Infection, 142(11), pp.2447-2459.

Andraud, M., Hens, N. and Beutels, P., 2013. A simple periodic-forced model for dengue fitted to incidence data in Singapore. Mathematical biosciences, 244(1), pp.22-28.

BMKG. 2019. DB-klim : Peringatan Dini Demam Berdarah. Jakarta.

Ch´avez, J.P., G¨otz, T., Siegmund, S. and Wijaya, K.P., 2017. An SIR-Dengue transmission model with seasonal effects and impulsive control. Mathematical biosciences, 289, pp.29-39.

Chen, S.C., Liao, C.M., Chio, C.P., Chou, H.H., You, S.H. and Cheng, Y.H., 2010. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Science of the total environment, 408(19), pp.4069-4075.

Climate data contributors, Semarang city, accessed 10 November 2018,

Climate data contributors, Malang city, accessed 10 November 2018,

Dengue data provided by the Health Office (Dinas Kesehatan) of the City of Semarang and Malang. Indonesia.

Ebi, K.L. and Nealon, J., 2016. Dengue in a changing climate. Environmental research, 151, pp.115-123.

Esteva, L. and Vargas, C., 1998. Analysis of a dengue disease transmission model. Mathematical biosciences, 150(2), pp.131-151.

Fakhruddin, M., Nuraini, N. and Indratno, S.W., 2019, March. Mathematical model of dengue transmission based on daily data in Bandung. In AIP Conference Proceedings (Vol. 2084, No. 1, p. 020013). AIP Publishing.

Grassly, N.C. and Fraser, C., 2006. Seasonal infectious disease epidemiology. Proceedings of the Royal Society B: Biological Sciences, 273(1600), pp.2541-2550.

G¨otz, T., Altmeier, N., Bock, W., Rockenfeller, R. and Wijaya, K.P., 2017. Modeling dengue data from Semarang, Indonesia. Ecological complexity, 30, pp.57-62.

Halstead, S.B., 2017. Dengue. The Lancet, 370, pp.1644-1652.

Hartley, L.M., Donnelly, C.A. and Garnett, G.P., 2002. The seasonal pattern of dengue in endemic areas: mathematical models of mechanisms. Transactions of the royal society of tropical medicine and hygiene, 96(4), pp.387-397.

Hopp, M.J. and Foley, J.A., 2001. Global-scale relationships between climate and the dengue fever vector, Aedes aegypti. Climatic change, 48(2-3), pp.441-463.

Huang, C.C., Tam, T., Chern, Y.R., Lung, S.C., Chen, N.T. and Wu, C.D., 2018. Spatial clustering of dengue fever incidence and its association with surrounding greenness. International journal of environmental research and public health, 15(9), p.1869.

Izzah, L.N., Majid, Z., Ariff, M.A.M. and Fook, C.K., 2016. Geospatial analysis of urban land use pattern analysis for haemorrhagic fever risk: a review. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(W1), pp.37-53.

Johansson, M.A., Dominici, F. and Glass, G.E., 2009. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS neglected tropical diseases, 3(2), p.e382.

Kyle, J.L. and Harris, E., 2008. Global spread and persistence of dengue. Annu. Rev. Microbiol., 62, pp.71-92.

Lee, K.S., Lai, Y.L., Lo, S., Barkham, T., Aw, P., Ooi, P.L., Tai, J.C., Hibberd, M., Johansson, P., Khoo, S.P. and Ng, L.C., 2010. Dengue virus surveillance for early warning, Singapore. Emerging infectious diseases, 16(5), p.847.

McLennan-Smith, T.A. and Mercer, G.N., 2014. Complex behaviour in a dengue model with a seasonally varying vector population. Mathematical biosciences, 248, pp.22-30.

Ooi, E.E., Hart, T.J., Tan, H.C. and Chan, S.H., 2001. Dengue seroepidemiology in Singapore. The Lancet, 357(9257), pp.685-686.

Sang, S., Yin, W., Bi, P., Zhang, H., Wang, C., Liu, X., Chen, B., Yang, W. and Liu, Q., 2014. Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability. PloS one, 9(7), p.e102755.

Sharmin, S., Glass, K., Viennet, E. and Harley, D., 2015. Interaction of mean temperature and daily fluctuation influences dengue incidence in Dhaka, Bangladesh. PLoS neglected tropical diseases, 9(7), p.e0003901.

Tamura, K. and Yasuda, K., 2011. Spiral dynamics inspired optimization. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(8), pp.1116-1122.

YU¨ ZGE, U. and Tufan, I˙.N.A.C¸ ., 2016. Adaptive Spiral Optimization Algorithm for Benchmark Problems. Bilecik S¸eyh Edebali U¨ niversitesi Fen Bilimleri Dergisi, 3(1).

Wikipedia contributors, Semarang city, accessed 28 October 2018,

Wikipedia contributors, Malang city, accessed 28 October 2018,

Yang, H.M. and Ferreira, C.P., 2008. Assessing the effects of vector control on dengue transmission. Applied Mathematics and Computation, 198(1), pp.401-413.



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This journal published by: Indonesian Bio-Mathematical Society, Pusat Pemodelan Matematika dan Simulasi, Jalan Ganesa No. 10 Bandung 40116