Developing a Molecular Mathematical Model for the Dissemination of Dengue Fever Incorporating Human Mobility Variables
DOI:
https://doi.org/10.31004/joecy.v5i2.3453Keywords:
Dengue fever, molecular mode, mathematical modelling, human mobility, spatial heterogeneityAbstract
In 2009, the World Health Organisation disclosed that Indonesia has the highest frequency of dengue fever in Southeast Asia. In 2014, the Ministry of Health of the Republic of Indonesia publicly disclosed that a substantial number of dengue fever cases were recorded across all regions of Indonesia. Spatial heterogeneity and human mobility are the principal variables that complicate dengue transmission. This work centres on developing a genetic model for dengue disease transmission, incorporating human movement between two distinct clusters. The molecular model addresses both human mobility and spatial heterogeneity. In this specific molecular model, each individual person and female Aedes aegypti mosquito is designated as a particle, but the collective population of humans and mosquitoes is termed a particle system. This molecular model consists of three different phases. Their coverage includes factors such as population growth dynamics, alterations in regional distribution, and fluctuations in infection status among humans and mosquitoes. The dynamics influence the interactions between humans and mosquitoes. Simulations have been carried out with a variety of contact radii and human motion patterns taken into consideration. The simulation results indicate that the intensity of dengue transmission escalates with heightened human mobility and an expanded interaction radius during disease propagation.
References
Achee, N. L., Gould, F., Perkins, T. A., Reiner, R. C., Morrison, A. C., Ritchie, S. A., Gubler, D. J., Teyssou, R., & Scott, T. W. (2015). A Critical Assessment of Vector Control for Dengue Prevention. PLOS Neglected Tropical Diseases, 9(5), e0003655. https://doi.org/10.1371/journal.pntd.0003655
Aldila, D., Götz, T., & Soewono, E. (2013). An optimal control problem arising from a dengue disease transmission model. Mathematical Biosciences, 242(1), 9–16. https://doi.org/10.1016/j.mbs.2012.11.014
Arias, J. H., Martinez, H. J., Sepulveda, L. S., & Vasilieva, O. (2015). PREDATOR-PREY MODEL FOR ANALYSIS OF AEDES AEGYPTI POPULATION DYNAMICS IN CALI, COLOMBIA. International Journal of Pure and Apllied Mathematics, 105(4). https://doi.org/10.12732/ijpam.v105i4.2
Esler, D. (2009). Dengue—Clinical and public health ramifications. Australian Family Physician, 38(11), 876–879.
Esteva, L., & Vargas, C. (1998). Analysis of a dengue disease transmission model. Mathematical Biosciences, 150(2), 131–151. https://doi.org/10.1016/S0025-5564(98)10003-2
Esteva, L., & Vargas, C. (1999). A model for dengue disease with variable human population. Journal of Mathematical Biology, 38(3), 220–240. https://doi.org/10.1007/s002850050147
Favier, C., Schmit, D., Müller-Graf, C. D. M., Cazelles, B., Degallier, N., Mondet, B., & Dubois, M. A. (2005). Influence of spatial heterogeneity on an emerging infectious disease: The case of dengue epidemics. Proceedings of the Royal Society B: Biological Sciences, 272(1568), 1171–1177. https://doi.org/10.1098/rspb.2004.3020
Kularatne, S. A., & Dalugama, C. (2022). Dengue infection: Global importance, immunopathology and management. Clinical Medicine, 22(1), 9–13. https://doi.org/10.7861/clinmed.2021-0791
Ligsay, A. D., Tambio, K. J. M., Aytona, M. J. M., Alejandro, G. J. D., Regencia, Z. J. G., Baja, E. S., & Paul, R. E. L. (2022). Assessing Entomological and Epidemiological Efficacy of Pyriproxyfen-Treated Ovitraps in the Reduction of Aedes Species: A Quasi-Experiment on Dengue Infection Using Saliva Samples. International Journal of Environmental Research and Public Health, 19(5), 3026. https://doi.org/10.3390/ijerph19053026
Maciel-de-Freitas, R., Avendanho, F. C., Santos, R., Sylvestre, G., Araújo, S. C., Lima, J. B. P., Martins, A. J., Coelho, G. E., & Valle, D. (2014). Undesirable Consequences of Insecticide Resistance following Aedes aegypti Control Activities Due to a Dengue Outbreak. PLOS ONE, 9(3), e92424. https://doi.org/10.1371/journal.pone.0092424
Ndii, M. Z., Mage, A. R., Messakh, J. J., & Djahi, B. S. (2020). Optimal vaccination strategy for dengue transmission in Kupang city, Indonesia. Heliyon, 6(11), e05345. https://doi.org/10.1016/j.heliyon.2020.e05345
Nuraini, N., Soewono, E., & Sidarto, K. A. (2007). Mathematical model of dengue disease transmission with severe DHF compartment. Bulletin of the Malaysian Mathematical Sciences Society. Second Series, 30(2), 143–157.
Nuraini, N., & Tasman, H. (2012). Simulation Model for Dengue Infection. International Journal of Basic & Applied Sciences IJBAS-IJENS, 12(1), 26–30.
Porter, K. R., Beckett, C. G., Kosasih, H., Tan, R. I., Alisjahbana, B., Rudiman, P. I. F., Widjaja, S., Listiyaningsih, E., Ma’Roef, C. N., McArdle, J. L., Parwati, I., Sudjana, P., Jusuf, H., Yuwono, D., & Wuryadi, S. (2005). Epidemiology of dengue and dengue hemorrhagic fever in a cohort of adults living in Bandung, West Java, Indonesia. The American Journal of Tropical Medicine and Hygiene, 72(1), 60–66.
Shepard, D. S., Suaya, J. A., Halstead, S. B., Nathan, M. B., Gubler, D. J., Mahoney, R. T., Wang, D. N. C., & Meltzer, M. I. (2004). Cost-effectiveness of a pediatric dengue vaccine. Vaccine, 22(9–10), 1275–1280. https://doi.org/10.1016/j.vaccine.2003.09.019
Yaacob, Y., Yeak, S. H., Lim, R. S., & Soewono, E. (2015). A delay differential equation model for dengue transmission with regular visits to a mosquito breeding site. 153–160. https://doi.org/10.1063/1.4914447
Zevika, M., Soewono, E., & Tse, O. (2017). A particle system model for dengue transmission. Communication in Biomathematical Sciences, 1(1), 32–45. https://doi.org/10.5614/cbms.2017.1.1.3
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