Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal
DOI:
https://doi.org/10.31764/jtam.v5i1.3468Keywords:
COVID-19, finite difference, reproduction number, time dependent.Abstract
The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period  for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas. It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro) i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers are computed as discrete values depending on time. The used research method is  finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.References
Amira, F., Hamzah, B., Lau, C. H., Nazri, H., Ligot, D. V., Lee, G., Liang Tan, C., Khursani Bin, M., Shaib, M., Hasanah, U., Zaidon, B., Abdullah, A. B., Chung, M. H., Ong, C. H., Chew, P. Y., & Salunga, R. E. (2020). Outbreak Data Analysis and Prediction. Bull World Health Organ. E-Pub, March. https://doi.org/10.2471/BLT.20.251561
Carcione, J. M., Santos, J. E., Bagaini, C., & Ba, J. (2020). A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model. Frontiers in Public Health, 8(May). https://doi.org/10.3389/fpubh.2020.00230
Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena, 139(January), 1–14. https://doi.org/10.1016/j.chaos.2020.110057
Dilip Kumar, B., Arati, R., Abhishek, B., & Dulu, P. (2020). Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods. Chaos Solitons Fractals., 2020(140). https://doi.org/10.1016/j.chaos.2020.110154
Godio, A., Pace, F., & Vergnano, A. (2020). SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103535
Gray, A., Greenhalgh, D., Hu, L., Mao, X., & Pan, J. (2009). A Stochastic Differential Equation SIS Epidemic Model. SIAM Journal on Applied Mathematics, 31(5), 876–902. https://doi.org/https://doi.org/10.1137/10081856X
Hasan, A., Susanto, H., Kasim, M. F., Nuraini, N., Lestari, B., Triany, D., & Widyastuti, W. (2020). Superspreading in early transmissions of COVID-19 in Indonesia. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-79352-5
Hurint, R. U., Ndii, M. Z., & Lobo, M. (2017). Analisis Sensitivitas Model Epidemi SEIR. Natural Science: Journal of Science and Technology, 6(1). https://doi.org/10.22487/25411969.2017.v6.i1.8076
Ifguis, O., El Ghozlani, M., Ammou, F., Moutcine, A., & Abdellah, Z. (2020). Simulation of the Final Size of the Evolution Curve of Coronavirus Epidemic in Morocco using the SIR Model. Journal of Environmental and Public Health, 2020, 1–5. https://doi.org/https://www.hindawi.com/journals/jeph/2020/9769267/
Khosravi, A., Chaman, R., Rohani-Rasaf, M., Zare, F., Mehravaran, S., & Emamian, M. . (2020). The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran. Cambridge University Press Public Health Emergency Collection. https://doi.org/10.1017/S0950268820001247
Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., Davies, N., Gimma, A., van Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C. I., Clifford, S., Quilty, B. J., Bosse, N. I., … Flasche, S. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 3099(20), 1–7. https://doi.org/10.1016/S1473-3099(20)30144-4
Obadia, T., Haneef, R., & Boëlle, P. Y. (2012). The R0 package: A toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Medical Informatics and Decision Making, 12(1). https://doi.org/10.1186/1472-6947-12-147
Pambuccian, S. E. (2020). The COVID-19 pandemic: Implications for the cytology laboratory. Journal of the American Society of Cytopathology. https://doi.org/https://doi.org/10.1016/j.jasc.2020.03.001
Parhusip, H. A. (2020). Study on COVID-19 in the World and Indonesia Using Regression Model of SVM, Bayesian Ridge and Gaussian. Jurnal Ilmiah Sains, 20(2), 49. https://doi.org/10.35799/jis.20.2.2020.28256
Paul L, D., Street, E. J., Leslie, T. F., Yang, Y. T., & Jacobsen, K. H. (2019). Complexity of the Basic Reproduction Number (R0). EID, 25(1). https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article
Prem, K., Liu, Y., Russell, T., Kucharski, A. J., Eggo, R. M., Davies, N., Group, C. for the M. M. of I. D. C.-19 W., Jit, M., & Klepac, P. (2020). The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China. MedRxiv, 2667(20), 2020.03.09.20033050. https://doi.org/10.1101/2020.03.09.20033050
Putra, Z. A., & Abidin, S. A. Z. (2020). Application of seir model in covid-19 and the effect of lockdown on reducing the number of active cases. In Indonesian Journal of Science and Technology (Vol. 5, Issue 2, pp. 185–192). https://doi.org/10.17509/ijost.v5i2.24432
Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(5), 1467–1474. https://doi.org/10.1016/j.dsx.2020.07.045
Ud Din, R., Shah, K., Ahmad, I., & Abdeljawad, T. (2020). Study of Transmission Dynamics of Novel COVID-19 by Using Mathematical Model. Advances in Difference Equations, 2020(1). https://doi.org/10.1186/s13662-020-02783-x
Wu, Y.-C., Chen, Ching-Sunga, Chan, & Yu-Jiuna. (2020). The outbreak of COVID-19 An overview. Journal of the Chinese Medical Association, March 2020. https://doi.org/10.1097/JCMA.0000000000000270
Yuan, J., Li, M., Lv, G., & Lu, Z. K. (2020). Monitoring Transmissibility and Mortality of COVID-19 in Europe. International Journal of Infectious Diseases, 95, 311–315. https://doi.org/10.1016/j.ijid.2020.03.050
Downloads
Published
Issue
Section
License
Authors who publish articles in JTAM (Jurnal Teori dan Aplikasi Matematika) agree to the following terms:
- Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution–ShareAlike License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).