Journal of Inflammatory Diseases
The Official Journal of Qazvin University of Medical Sciences
Outlines
Predicting the Prevalence of COVID-19 and its Mortality Rate in Iran Using Lyapunov Exponent
Abstract
Background: COVID-19 was first reported in late December 2019 in Wuhan, China, and spread rapidly throughout the world including Iran. Objective The purpose of this paper is to predict the prevalence of coronavirus and the number of confirmed cases and deaths in Iran based on the theory of chaos and measuring the Lyapunov exponent. Methods: In this analytical study, the number of confirmed cases, recovered patients, total tests, and deaths between February 20 and May 30, 2020 were collected daily from the website of the Iranian Ministry of Health and Medical Education. The prevalence rate and the time to reach saturation in a short period were estimated using a formula using Lyapunov exponent and the initial and final number of confirmed cases in Matlab software. Findings: Simulation of all confirmed cases between 20 February 2020 to 4 May 2020 show the number of people infected with the coronavirus would be close to saturation, but in end of May 2020 the number of people with the disease re-entered the second phase of increase. The slope of the simulation curve decreases in the second phase and the virus spreads in May at a slower rate than in the first phase (April). The simulation diagram of the total confirmed patients to the total number of tests performed also shows the entry into the second phase of increasing in May. Conclusion: Simulation results of all confirmed cases and total deaths in Iran, using chaos theory and the Lyapunov-based model, can properly represent the real data and can predict the trend of spread and time to approach saturation in a short time. Sensitivity to the initial condition in the equation by changing the quarantine restrictions and the observance of health protocols causes a change in the rate of total number of confirmed patients enters the third or fourth increasing phase. Also, based on the calculated deaths, it is predicted that the total number of deaths at the end of May will reach less than 5% of the total number of people who have recovered and died.
References
- 1.Bauch, C. T. (2008). The role of mathematical models in explaining recurrent outbreaks of infectious childhood diseases. In Mathematical Epidemiology (pp. 297-319). Springer, Berlin, Heidelberg.
- 2.Pourbohloul, B., Meyers, L. A., Skowronski, D. M., Krajden, M., Patrick, D. M., & Brunham, R. C. (2005). Modeling control strategies of respiratory pathogens. Emerging infectious diseases, 11(8), 1249.
- 3.Ssematimba, A., Hagenaars, T. J., & De Jong, M. C. (2012). Modelling the wind-borne spread of highly pathogenic avian influenza virus between farms. PLoS One, 7(2).
- 4.Briggs, A. D., Wolstenholme, J., Blakely, T., & Scarborough, P. (2016). Choosing an epidemiological model structure for the economic evaluation of non-communicable disease public health interventions. Population health metrics, 14(1), 17.
- 5.Garnerin, P., & Valleron, A. J. (1992). The French communicable diseases computer network: a technical view. Computers in biology and medicine, 22(3), 189-200.
- 6.Toubiana, L., & Vibert, J. F. (1998). A neural network model for the spread of communicable diseases. In Geomed97 (pp. 249-259). Vieweg+ Teubner Verlag, Wiesbaden.
- 7.Perez, L., & Dragicevic, S. (2009). An agent-based approach for modeling dynamics of contagious disease spread. International journal of health geographics, 8(1), 50.
- 8.Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., ... & Dighe, A. (2020). Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet infectious diseases.
- 9.Silverman, J. D., Hupert, N., & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States. Science translational medicine, 12(554).
- 10.CAO, S., FENG, P., & SHI, P. (2020). Study on the epidemic development of corona virus disease-19 (COVID-19) in Hubei province by a modified SEIR model. Journal of Zhejiang University (Medical Science), 49(1), 0-0.
- 11.Chen, T. M., Rui, J., Wang, Q. P., Zhao, Z. Y., Cui, J. A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious diseases of poverty, 9(1), 1-8.
- 12.Basiri, M. R. (2020). Theory about treatments and morbidity prevention of corona virus disease (covid-19). Journal of Pharmacy and Pharmacology, 8, 89-90.
- 13.Alipour, A. & Ghadami, A. & Alipour, Z., & Abdollahzadeh, H. (2020). Preliminary Validation of the Corona Disease Anxiety Scale (CDAS) in the Iranian Sample, Quarterly Journal of Health Psychology, 8 (4), (series 32), 163-175.
- 14.Pourghaznein, T., & Salati, S. (2020). National Approach in Response to the COVID-19 Pandemic in Iran. International Journal of Community Based Nursing and Midwifery, 8(3), 275.
- 15.Dictionary, O. E. (2002), Concise Oxford English Dictionary.
- 16.Chat, H., Villermaux, E., & Chomaz, J. M. (Eds.). (2012), Mixing: chaos and turbulence, Vol. 373, Springer Science & Business Media.
- 17.Redondo Apraiz, J. M. (2014), "Turbulence, entropy and dynamics", lecture notes.
- 18.Hashemi Golpayegani, S. M. R. (2009).Chaos and Its Applications in Engineering, Tehran: Amirkabir University of Technology Publication.
- 19.Kouzehgari, s. (2018). Explanation of International System Behavior based on Chaos Principal, Case Study The Middle East Region, (Doctoral dissertation), Faculty of Humanities,Tarbiat Modares University.
- 20.Kinsner, W. (2006). Characterizing chaos through Lyapunov metrics. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(2), 141-151.
- 21.Nazarimehr, F., Jafari, S., Golpayegani, S. M. R. H., & Sprott, J. C. (2017). Can Lyapunov exponent predict critical transitions in biological systems? Nonlinear Dynamics, 88(2), 1493-1500.
- 22.Schuster, H. G. (1995). Deterministic Chaos, 3rd augm. ed.
- 23.Nychka, D., Ellner, S., Gallant, A. R., & McCaffrey, D. (1992). Finding chaos in noisy systems. Journal of the Royal Statistical Society: Series B (Methodological), 54(2), 399-426.
- 24.McCue, L. S., & Troesch, A. W. (2011). Use of Lyapunov exponents to predict chaotic vessel motions. In Contemporary Ideas on Ship Stability and Capsizing in Waves (pp. 415-432). Springer, Dordrecht.
- 25.Abrishami, H., Moeini, A., & Ahrari, m. (2007). Using Lyapunov Exponent for Modeling Time Series of Oil Price Based on Logistic Map, Tahghighat- E- Eghtesadi, No. 76, pp. 77-100.
- 26.Moeini Sedeh, S., et. al (2015). Nonlinear analysis of dynamic lumbar stability during repetitive trunk flexion extension at symmetric and asymmetric directions, Iranian Journal of Biomedical Engineering, 7(2014) 333-340.
- 27.Ferriere, R., & Gatto, M. (1995). Lyapunov exponents and the mathematics of invasion in oscillatory or chaotic populations. Theoretical Population Biology, 48(2), 126-171.
- 28.Babak Dehghanpisheh, (2020, March 4). Coronavirus has spread to nearly all Iran provinces: president, REUTERS, Retrieved at https://www.reuters.com/article/us-health-coronavirus-iran-rouhani/coronavirus-has-affected-almost-all-iranian-provinces-president/.
- 29.Worldometer, (2020). COVID-19 Coronavirus Pandemic, Retrieved from https://www.worldometers.info/coronavirus/countries.
- 30.Bonasera, A., & Zhang, S. (2020). Chaos, Percolation and the Coronavirus Spread. Frontiers in Physics, 8, 171.##.
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© 2024, Journal of Inflammatory Diseases. This open-access article is available under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) International License (https://creativecommons.org/licenses/by-nc/4.0/), which allows for the copying and redistribution of the material only for noncommercial purposes, provided that the original work is properly cited.
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