Model dinamika penyebaran pemilih dengan menggunakan pendekatan epidemiologi
Keywords:
mathematical modeling, population dynamics, epidemic model of SIRAbstract
In a world full of uncertainty, it is necessary to have scientific studies that can be used to predict various things in various fields of life that can help the government and stakeholders in making policies that are right on target. The predictions of this scientific study are built from a mathematical modeling. Mathematical modeling has been widely used in various fields, even in politics. One of the expected achievements of the mathematical model in the political field is that it can be used to see the dynamics of the distribution of voters in a presidential election. In the presidential and vice presidential elections, candidate pairs and their supporting parties play an important role in luring voters to vote for them on election day. Just as in an infectious disease that can spread and infect humans very quickly, presidential and vice presidential candidates can massively promote themselves through various means with the support of political parties to get as many votes as possible from the voters. In this study, a dynamic model of the distribution of voters will be built using an epidemiological approach. The model to be used is the SIR epidemic model. The model will use a system of differential equations involving three classes of voters, namely neutral voters who have not made their choice, voters who gravitate towards certain presidential and vice presidential candidate pairs, and voters who are apathetic. This model is expected to be used to predict the number of voters in a presidential election.
References
epidemiology, volume 1945 of Lecture Notes in Math, pp. 19-79, Berlin:
Springer.
Brauer, F. (2009). Review: Mathematical epidemiology is not an oxymoron, BMC Public Health, 9(Suppl I):S2.
Cai, L., Li, X., Ghosh, M., & Guo, B. (2009). Stability analysis of an HIV/AIDS
epidemic model with treatment, Journal of Computational and Applied Mathematics, 229, pp. 313-323.
Chowell, G., Ammon, C.E., Hengartner, N.W., & Hyman, J.M. (2005).
Transmission dynamics of the great influenza pandemic of 1918 in Geneva,
Switzerland: assessing the effects of hypothetical interventions, Journal of
Theoretical Biology.
Efelin, P., Yong, B., & Owen, L. (2016). Model Penyebaran Penyakit SARS dengan Pengaruh Vaksinasi. Prosiding Seminar Nasional Matematika, 11, pp. 77-85.
Esteva, L. & Vargas, C. (1998). Analysis of a dengue disease transmission model.
Mathematical Biosciences, 150, pp. 131-151.
Feng, Z., Castillo-Chavez, C. & Capurro, A.F. (2000). A model for tuberculosis with
exogenous reinfection, Theoretical Population Biology, 57, pp. 235-247.
Giordano, F.R., Fox, W.P., Horton, S.B., & Weir, M.D. (2008). A first course in
mathematical modeling (4th ed.), Brooks/Cole.
Hethcote, H.W. (1994). A thousand and one epidemic models, Frontiers in
Theoretical Biology, 100, pp. 504-515.
Lekone, E.P. & Finkenstadt, B.F. (2006). Statistical inference in a stochastic epidemic
SEIR model with control intervention: ebola as a case stuy, Biometrics, 62,
1170-1177.
Liu, X., Takeuchi, Y. & Iwami, S. (2008). SVIR epidemic models with vaccination
strategies, Journal of Theoretical Biology, 253, pp. 1-11.
Xia, Z.Q, Zhang, J., Xue, Y.K., Sun, G.Q., & Jin, Z. (2015). Modeling the
transmission of middle east respirator syndrome corona virus in the republic of
Korea, PLoS ONE, 10(12).
Yong, B. (2007). Model Penyebaran HIV dalam Sistem Penjara. Jurnal MIPA: Matematika, Ilmu Pengetahuan Alam, dan Pengajarannya, 36(1), pp. 31-47.
Yong, B. & Owen, L. (2016). Dynamical Transmission Model of MERS-CoV in Two Areas. AIP Conference Proceedings, 1716, http://dx.doi.org/10.1063/1.4942993