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Year 2020, Volume: 3 Issue: 3, 135 - 138, 29.12.2020
https://doi.org/10.33187/jmsm.774123

Abstract

References

  • [1] World Health Organization,Coronavirus disease (COVID-19) outbreak, 2020, available at https://www.who.int/health-topics/Coronavirus.
  • [2] A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, R. M. Eggo, F. Sun, M. Jit, J. D. Munday, N. Davies, Early dynamics of transmission and control of COVID-19: a Mathematical modeling study, The Iancet infectious diseases, 2020.
  • [3] A. L. Kapetanovic, D. Poljack, Modeling the epidemic outbreak and dynamics of COVID-19 in Croatia, (2020), pp.arXiv-2005.
  • [4] D. Caccavo,Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model, available at https://doi.org/10.1101/2020.03.19.20039388.
  • [5] F. Ndairou, I. Area, J. J. Nieto, D.F. Torres,Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan, Chaos, Solitons Fractals, (2020), p.109846.
  • [6] R. Sameni, Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus, (2020), arXiv:2003.11371.
  • [7] J. C. Blackwood, L. M. Childs, An introduction compartmental modeling for the budding infectious disease modeller, Letters in Biomathematics, 5(1)(2018), 195-221.
  • [8] E. L. Piccolomiini, F. Zama, Monitoring Italian COVID-19 spread by an adaptive SEIRD model, medRxiv, 2020.
  • [9] C. Anastassopoulou, L. Russo, A. Tsakris, C. Siettos, Data-based analysis, modelling and forecasting of the COVID-19 outbreak, PloS one, 15(3) (2020), p.e0230405.
  • [10] P. Khrapov, A. Loginova, Mathematical modeling of the dynamics of coronavirus COVID-19 epidemic development in China, Int. J. Open Inf. Technol., 8(4)(2020), 13-16.
  • [11] F. Brauer, C. Castillo-Chavez, Z. Feng, Mathematical Model in, Epidemiology, Springer, New York, 2019.
  • [12] M. Y. Li, An Introduction to Mathematical Modeling Infectious Diseases, (Vol.2) Cham: Springer, 2018.
  • [13] M. A. A. Al-qaness, A. A. Ewees, H. Fan, M. E. Abd Elaziz , Optimization methods for Forecasting confirmed cases of COVID-19 in China, J. Clinical Medicine, 9(3) (2020), pp.674.
  • [14] J. Fernandez-Villaverde, C. I. Jones, Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States and Cities(No.w27128), National Bureau of Economic Research, 2020.
  • [15] https://www.worldometers.info/coronavirus/.
  • [16] W. O. Kermack, A. G. MacKendrick, A contribution to the mathematical theory of epidemics, In: Proceedings of the Royal Society of London, vol. A, p. 700-721, 1927.
  • [17] L. Ferrari, G. Gerardi, G. Manzi, A. Micheletti, F. Nicolussi, S. Salini, Modeling provisional COVID-19 epidemic data in Italy using an adjusted time-dependent SIRD model, (2020), arXiv :2005.12170.
  • [18] E. L. Piccolomiini, F. Zama, Monitoring Italian COVID-19 spread by an adaptive SEIRD model, medRxiv, 2020.
  • [19] A. Ianni, Rossi, N., Describing the COVID-19 Outbreak Fitting Modified SIR models to Data, medRxiv, 2020.
  • [20] G. Chowell, Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts. Infectious Disease Modelling, 2(2017), 379-398.

Fitting an Epidemiological Model to Transmission Dynamics of COVID-19

Year 2020, Volume: 3 Issue: 3, 135 - 138, 29.12.2020
https://doi.org/10.33187/jmsm.774123

Abstract

A rapid increase in daily new cases was reported in the world from February 19 to April 3, 2020. In this study, a susceptible-infected-recovered-dead (SIRD) was developed to analyse the dynamics of the global spread of COVID-19 during the above-mentioned period of time. The values of the model parameters fitted the reported data were estimated by minimizing the sum of squared errors using the Levenberg-Marquardt optimization algorithm. A time-dependent infection rate was considered. The set of differential equations in the model was solved using the fourth order Runge-Kutta method. It was observed that a time-dependent parameter gives a better fit to a dynamic data. Based on the fitted model, the average value of basic reproduction number (\textit{R0}) for COVID-19 trasmission was estimated to be 2.8 which shows that the spread of COVID-19 disease in the world was growing exponentially. This may indicate that the control measures implemented worldwide could not decrease the COVID-19 transmission.

References

  • [1] World Health Organization,Coronavirus disease (COVID-19) outbreak, 2020, available at https://www.who.int/health-topics/Coronavirus.
  • [2] A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, R. M. Eggo, F. Sun, M. Jit, J. D. Munday, N. Davies, Early dynamics of transmission and control of COVID-19: a Mathematical modeling study, The Iancet infectious diseases, 2020.
  • [3] A. L. Kapetanovic, D. Poljack, Modeling the epidemic outbreak and dynamics of COVID-19 in Croatia, (2020), pp.arXiv-2005.
  • [4] D. Caccavo,Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model, available at https://doi.org/10.1101/2020.03.19.20039388.
  • [5] F. Ndairou, I. Area, J. J. Nieto, D.F. Torres,Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan, Chaos, Solitons Fractals, (2020), p.109846.
  • [6] R. Sameni, Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus, (2020), arXiv:2003.11371.
  • [7] J. C. Blackwood, L. M. Childs, An introduction compartmental modeling for the budding infectious disease modeller, Letters in Biomathematics, 5(1)(2018), 195-221.
  • [8] E. L. Piccolomiini, F. Zama, Monitoring Italian COVID-19 spread by an adaptive SEIRD model, medRxiv, 2020.
  • [9] C. Anastassopoulou, L. Russo, A. Tsakris, C. Siettos, Data-based analysis, modelling and forecasting of the COVID-19 outbreak, PloS one, 15(3) (2020), p.e0230405.
  • [10] P. Khrapov, A. Loginova, Mathematical modeling of the dynamics of coronavirus COVID-19 epidemic development in China, Int. J. Open Inf. Technol., 8(4)(2020), 13-16.
  • [11] F. Brauer, C. Castillo-Chavez, Z. Feng, Mathematical Model in, Epidemiology, Springer, New York, 2019.
  • [12] M. Y. Li, An Introduction to Mathematical Modeling Infectious Diseases, (Vol.2) Cham: Springer, 2018.
  • [13] M. A. A. Al-qaness, A. A. Ewees, H. Fan, M. E. Abd Elaziz , Optimization methods for Forecasting confirmed cases of COVID-19 in China, J. Clinical Medicine, 9(3) (2020), pp.674.
  • [14] J. Fernandez-Villaverde, C. I. Jones, Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States and Cities(No.w27128), National Bureau of Economic Research, 2020.
  • [15] https://www.worldometers.info/coronavirus/.
  • [16] W. O. Kermack, A. G. MacKendrick, A contribution to the mathematical theory of epidemics, In: Proceedings of the Royal Society of London, vol. A, p. 700-721, 1927.
  • [17] L. Ferrari, G. Gerardi, G. Manzi, A. Micheletti, F. Nicolussi, S. Salini, Modeling provisional COVID-19 epidemic data in Italy using an adjusted time-dependent SIRD model, (2020), arXiv :2005.12170.
  • [18] E. L. Piccolomiini, F. Zama, Monitoring Italian COVID-19 spread by an adaptive SEIRD model, medRxiv, 2020.
  • [19] A. Ianni, Rossi, N., Describing the COVID-19 Outbreak Fitting Modified SIR models to Data, medRxiv, 2020.
  • [20] G. Chowell, Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts. Infectious Disease Modelling, 2(2017), 379-398.
There are 20 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Endalew Tsega

Publication Date December 29, 2020
Submission Date July 26, 2020
Acceptance Date December 24, 2020
Published in Issue Year 2020 Volume: 3 Issue: 3

Cite

APA Tsega, E. (2020). Fitting an Epidemiological Model to Transmission Dynamics of COVID-19. Journal of Mathematical Sciences and Modelling, 3(3), 135-138. https://doi.org/10.33187/jmsm.774123
AMA Tsega E. Fitting an Epidemiological Model to Transmission Dynamics of COVID-19. Journal of Mathematical Sciences and Modelling. December 2020;3(3):135-138. doi:10.33187/jmsm.774123
Chicago Tsega, Endalew. “Fitting an Epidemiological Model to Transmission Dynamics of COVID-19”. Journal of Mathematical Sciences and Modelling 3, no. 3 (December 2020): 135-38. https://doi.org/10.33187/jmsm.774123.
EndNote Tsega E (December 1, 2020) Fitting an Epidemiological Model to Transmission Dynamics of COVID-19. Journal of Mathematical Sciences and Modelling 3 3 135–138.
IEEE E. Tsega, “Fitting an Epidemiological Model to Transmission Dynamics of COVID-19”, Journal of Mathematical Sciences and Modelling, vol. 3, no. 3, pp. 135–138, 2020, doi: 10.33187/jmsm.774123.
ISNAD Tsega, Endalew. “Fitting an Epidemiological Model to Transmission Dynamics of COVID-19”. Journal of Mathematical Sciences and Modelling 3/3 (December 2020), 135-138. https://doi.org/10.33187/jmsm.774123.
JAMA Tsega E. Fitting an Epidemiological Model to Transmission Dynamics of COVID-19. Journal of Mathematical Sciences and Modelling. 2020;3:135–138.
MLA Tsega, Endalew. “Fitting an Epidemiological Model to Transmission Dynamics of COVID-19”. Journal of Mathematical Sciences and Modelling, vol. 3, no. 3, 2020, pp. 135-8, doi:10.33187/jmsm.774123.
Vancouver Tsega E. Fitting an Epidemiological Model to Transmission Dynamics of COVID-19. Journal of Mathematical Sciences and Modelling. 2020;3(3):135-8.

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