COVID-19 Mortality Prediction: A Case Study for Istanbul


  • Erkan Yilmaz Institute of Graduate Studies in Science, Istanbul University, 34134, Istanbul, Turkey, Theoretical and Computational Physics Research Lab. Department of Physics, Faculty of Science, Istanbul University, 34134 Istanbul, Turkey
  • Ekrem Aydiner Theoretical and Computational Physics Research Lab. Department of Physics, Faculty of Science, Istanbul University, 34134 Istanbul, Turkey
  • Özgur Ökcu Theoretical and Computational Physics Research Lab. Department of Physics, Faculty of Science, Istanbul University, 34134 Istanbul, Turkey


Sars Cov-2, COVID-19, Excess Mortality, SIR and SEIR Model, Pandemic, Mortality Prediction, Basic Reproduction Number


It is well known that it is very difficult to make predictions for the real number of deaths due to any pandemic by using SIR and similar models since the predicted solutions systematically can deviate from real data. On the other hand, death data in the long and effective pandemic period cannot reflect the real case. In order to get more correct solutions and obtain realistic predictions, the parameters of these equations must be determined more precisely. In this study, by using real data depending on all deaths in Istanbul as a case study for 2020-2022 we determined the values of the parameters of the SEIR model and obtained the solution of SEIR equations. Firstly, we show that our numerical solution has a good fit with real data of the deaths due to COVID-19 for 2020 first and second peaks and 2021 first peak. Based on this confirmation, we predicted possible the number of deaths for the 2021 second peak. Furthermore, we see that our results show the number of deaths due to COVID-19 in Istanbul. Our method strongly provides that the model can lead to correct results if the parameters of SEIR models are determined by using excess mortality approximation. Now, we extend the study to predict the number of deaths due to the pandemic effects in 2022-2023. We show that our prediction is still compatible with the number of deaths for each wave. Finally, we predict the number of deaths for the future wave of 2022-2023 and we calculate the number of infected people in Istanbul for herd immunity.


World Health Organization, Coronavirus disease 2019 (COVID-19): Situation report, 52 (2020). Available online at:

Republic of Turkey Ministry of Health, (2020). Available online at: (Turkish)

K. Heitman, Authority, autonomy and the first London Bills of Mortality, Centaurus, 62, (2020) 275– 284.

D. V. Glass, John Graunt and his natural and political observations, Proc. R. Soc. Lond. B., 159, (1963) 2-37.

D. Bernoulli, S. Blower, An attempt at a new analysis of the mortality caused by smallpox and of the advantages of inoculation to prevent it, Rev. Med. Virol, 14, (2004) 275-288.

W. H. Hamer, The Milroy Lectures, Bedford Press (1906).

N. Bacaer, Ross and Malaria (1911), A short history of mathematical population dynamics, Springer (2011).

W. O. Kermack, A. G. McKendrick, (1927). Contributions to the mathematical theory of epidemics,Proc. R. Soc. Lond. A, 115, (1927) 700-721.

W. O. Kermack, A. G. Kendrick, A Contribution to the mathematical theory of epidemics part II. The Problem of Endemicity, Proc. R. Soc. Lond. A, 138, (1932) 55-83.

W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics. III.—Further studies of the problem of endemicity, Proc. R. Soc. A, 141, (1933) 94-122.

R. M. Anderson, R. M. May, B. Anderson, Infectious diseases of humans: Dynamics and control, Oxford Science Publications (1991). 14

S. V. Scarpino, G. Petri, On the predictability of infectious disease outbreaks, Nat. Commun., 10, (2019) 898.

M. Chinazzi, J. T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler, A. P. Y. Piontti, K. Mu, L. Rossi, K. Sun, C. Viboud, X. Xiong, H. Yu, M. E. Halloran, I. M. Longini Jr, A. Vespignani, The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak, Science, 368, (2020) 395–400.

S. R¨udiger, A. Plietzsch, F. Sagu´es, Epidemics with mutating infectivity on small-world networks, Sci. Rep., 10, (2020) 1–11.

A. H. Bilge, O. Pekcan, M. V. G¨urol, Application of epidemic models to phase transitions, Phase ¨ Transitions, 85, (2012) 1009-1017.

J. Wangping, H. Ke, S. Yang, C. Wenzhe, W. Shengshu, Y. Shanshan, W. Jianwei, K. Fuyin, T. Penggang, L. Jing, L. Miao, H. Yao, Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China, Front. Med., 7, (2020) 169.

A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, R. M. Eggo, Early dynamics of transmission and control of COVID-19: A mathematical modelling study, Lancet Infect Dis., 20, (2020) 553-558.

Q. Li, B. Tang, N. L. Bragazzi, Y. Xiao, J. Wu, Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability, Math. Biosci., 325, (2020) 108378.

L. Danon, E. Brooks-Pollock, M. Bailey, M. Keeling, A spatial model of CoVID-19 transmission in England and Wales: Early spread and peak timing, medRxiv 2020.02.12.20022566, (2020).

D. Fanelli, F. Piazza, Analysis and forecast of COVID-19 spreading in China, Italy and France, Chaos, Solitons and Fractals, 134, (2020) 109761.

K. Roosa, Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J. M. Hyman, P. Yan, G. Chowell, Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020, Infect. Dis. Model., 5, (2020) 256–263.

L. Li, Z. Yang, Z. Dang, C. Meng, J. Huang, H. Meng, D. Wang, G. Chen, J. Zhang, H. Peng, Y. Shao, Propagation analysis and prediction of the COVID-19, Infect. Dis. Model., 5, (2020) 282–292.

L. Peng, W. Yang, D. Zhang, C. Zhuge, L. Hong, Epidemic analysis of COVID-19 in China by dynamical modeling, medRxiv 2020.02.16.20023465, (2020).

L. Mangoni, M. Pistilli, Epidemic analysis of COVID-19 in Italy by dynamical modelling, SSRN Electronic Journal, (2020). Available online at:

S. Mangiarotti, M. Peyre, Y. Zhang, M. Huc, F. Roger and Y. Kerr. Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context. Epidemiology & Infection , Volume 148 , (2020). DOI:

Marius-F. Danca. Controlling dynamics of a COVID–19 mathematical model using a parameter switching algorithm.

O. Postavaru, S.R. Anton, A. Toma. COVID-19 pandemic and chaos theory. Mathematics and Computers in Simulation 181 (2021) 138–149

S. Ahmetolan, A. H. Bilge, A. Demirci, A. P.-Dobie, O. Ergonul, What can we estimate from fatality and infectious case data using the susceptible-infected-removed (SIR) model? A case study of Covid-19 pandemic, Frontiers in Medicine, 7, (2020) 556366.

H. Ritchie, E. Mathieu, L. Rod´es-Guirao, C. Appel, C. Giattino, E. Ortiz-Ospina, J. Hasell, B. Macdonald, D. Beltekian, M. Roser, Coronavirus Pandemic (COVID-19), (2020). Available online at: 15

F. Checchi, L. Roberts, Interpreting and using mortality data in humanitarian emergencies, Humanitarian Practice Network, (2005).

D. M. Boka, H. Wainer, How can we estimate the death toll from COVID-19?, Chance, 33, (2020) 67–72.

W. Farr 1885. Vital statistics: A memorial volume of selections from the reports and writings of William Farr. (edited for the Sanitary Institute of Great Britain by Noel A. Humphreys) London: The Sanitary Institute, (1885).

A. D. Langmuir, William Farr: Founder of modern concepts of surveillance, International Journal of Epidemiology, 5, (1976) 13–18.

C. J. Murray, A. D. Lopez, B. Chin, D. Feehan, K. H. Hill 2006. Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918-20 pandemic: a quantitative analysis, The Lancet, 368, (2006) 2211–2218.

C. Viboud, R. F. Grais, B. A. Lafont, M. A. Miller, L. Simonsen, Multinational influenza seasonal mortality study group. Multinational impact of the 1968 Hong Kong influenza pandemic: evidence for a smoldering pandemic, J Infect Dis., 192, (2005) 233–248.

J. Housworth, A. D. Langmuir, Excess mortality from epidemic influenza, 1957-1966. Am. J. Epidemiol, 100, (1974) 40–48.

H. Krelle, C. Barclay, C. Tallack, Understanding excess mortality: What is the fairest way to compare Covid-19 deaths internationally? (2020) Avaliable at online:

WHO, ECDC. EuroMOMO. Published 2020. Accessed 15.05.2020

Republic of Turkey, e-Government Portal, Avaible online at:

Republic of Turkey Ministry of Health, (2022). Available online at: Dil=2 vaccination in Turkey




How to Cite

Erkan Yilmaz, Ekrem Aydiner, & Özgur Ökcu. (2023). COVID-19 Mortality Prediction: A Case Study for Istanbul. American Scientific Research Journal for Engineering, Technology, and Sciences, 92(1), 26–45. Retrieved from