Estimation of epidemiological indicators of COVID-19 in Algeria with an SIRD model
Objectives: Since the first report the coronavirus disease (COVID-19) in China on December 2019, enormous number of researches are ongoing to understand its epidemiological characteristics. One of the most important tools used to understand the epidemic curve and to predict its dynamic are epidemiological and statistical/mathematical models.
Methods: We used an SIR (Suspected-Infected-Recovered-Deceased) model to estimate the coefficients of infection, recovery and death, the reproduction number, the specific time of contact, the rate of recovery to the rate of death and the basic reproduction number and the peak of the COVID-19 epidemic in Algeria.
Results: According to the estimation of the SIRD model the peak of the epidemic will be reached on August-September in Algeria. The coefficient of infection, recovery and death are estimated at 0.1655, 0.1077 and 0.0035 (day-1) respectively. The basic reproduction value is 1.4876. The specific time of contact is estimated at 6.0423 days and the rate of recovery to the rate of death is estimated at 30.4183.
Conclusion: These results could contribute in the epidemiological characterization of COVID-19 in Algeria which will be helpful for the Algerian Authorities in the anti-COVID-19 battle. Results highlight also the role of the SIRD model in the study of COVID-19 dynamics.
1.Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons and Fractals 2020;139:110057. [CrossRef]
2. Peng L, Yang W, Zhang D, Zhuge C and Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint, arXiv:2002.06563, 2020. [CrossRef]
3. Momtazmanesh S, Ochs HD, Uddin LQ, Perc M. Routes JM. et al. All together to Fight COVID-19. Am. J. Trop. Med. Hyg 2020;102:1181–1183. [CrossRef]
4. Hâncean M-G, Perc M, Lerner J. 2020 Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks. R. Soc. Open Sci 7:200780. [CrossRef]
5. Alsayed A, Sadir H, Kamil R, and Sari H. Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020. Int. J. Environ. Res. Public Health 2020;17:4076. [CrossRef]
6. Fahmya A E., M. El-desoukya M, Mohamed A S.A. Epidemic Analysis of COVID-19 in Egypt, Qatar and Saudi Arabia using the Generalized SEIR Model. medRxiv preprint, doi: https:// doi.org/10.1101/2020.08.19.20178129. Posted August 22, 2020. [CrossRef]
7. Calafiore G C., Novara C and Possieri C.A. Modified SIR Model for the COVID-19 Contagion in Italy. arXiv: 2003.14391v1 [physics.soc-ph] 31 Mar 2020. [CrossRef]
8. Perc M, Gorišek Miksic´ N, Slavinec M and Stožer A (2020) Forecasting COVID-19. Front. Phys. 8:127. [CrossRef]
9. Bentout S, Chekroun A_and Kuniya T. Parameter estimation and prediction for coronavirus disease outbreak 2019 (COVID-19) in Algeria. AIMS Public Health 7:306–318. [CrossRef]
10. Tiwari, A. (2020). Modeling and analysis of the COVID-19 epidemic in India. medRxiv. [CrossRef]
11. Al-Raeei M. The forecasting of COVID-19 with mortality using SIRD epidemic model for the United States, Russia, China, and the Syrian Arab Republic. Advances 10, 065325 (2020). [CrossRef]
12. Al-Raeei M. The basic reproduction number of the new coronavirus pandemic with mortality for India, the Syrian Arab Republic, the United States, Yemen, China, France, Nigeria and Russia with different rate of cases Clinical Epidemiology and Global Health.
13. Matadi MB. On the integrability of the SIRD epidemic model. Commun. Math. Biol. Neurosci. 2020;2020:47.
14. Fernández-Villaverde, J., Jones, C.I. "Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities". National Bureau of Economic Research). 2020. [CrossRef]
15. Arora S, Jain R, Pal Singh H, Epidemiological models of SARSCoV-2 (COVID-19) to control the transmission based on current evidence: A systematic review. Preprints, 12 July 2020. [CrossRef]
16. Batistela CM., Correab DPF., Buenoc ÁM, Piqueiraa JRC. Compartmental model with loss of immunity: analysis and parameters estimation for Covid-19. arXiv:2007.01295v2 [q-bio.PE] 4 Jul 2020.
17. Cotta RM., Naveira-Cotta CP., Magal P. Parametric identification and public health measures influence on the covid-19 epidemic evolution in Brazil, medRxiv. 05/12/2020.03.31.
18. Wangping J, Ke H, Yang S, Wenzhe C, Shengshu W, Shanshan Y, Jianwei W, Fuyin K, Penggang T, Jing L, Miao L and Yao H. Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China. Front. Med. 2020. 7:169. [CrossRef]
19. Hao, T. Infection Dynamics of Coronavirus Disease 2019 (Covid-19) Modeled with the Integration of the Eyring Rate Process Theory and Free Volume Concept. medRxiv Preprint. 2020. https://doi.org/10.1101/2020.02.26.20028571. [CrossRef]
20. Ghosh P., Ghosh R., Chakraborty B. COVID-19 in India: Statewise Analysis and Prediction. MedRxiv. 2020. doi: https://doi. org/10.1101/2020.04.24.20077792 [CrossRef]
21. Martelloni G, Martelloni G. Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world. Chaos, Solitons and Fractals, 139, 2020, 110064. [CrossRef]
22. Tekindal MA, Yonar H, Yonar A, Tekindal M, Çevrimli MB, Alkan H, İnanç ZS, Mat B. Analyzing COVID-19 outbreak for Turkey and Eight Country with Curve Estimation Models, Box-Jenkins (ARIMA), Brown Linear Exponential Smoothing Method, Autoregressive Distributed Lag (ARDL) and SEIR Models. Eurasian J Vet Sci, 2020, Covid-19 Special Issue, 142-155 [CrossRef]
23. Singh, A., Dey, J., & Bhardwaj, S. (2020). Is this the beginning or the end of the COVID-19 outbreak in India? A data-driven mathematical model-based analysis. medRxiv. [CrossRef]
24. Lounis M, Azevedo JS. Application of a generalized SEIR model for covid-19 in Algeria. medRxiv preprint, August 23, 2020.
25. Mandal M., Mandal S. COVID-19 pandemic scenario in India compared to China and the rest of the world: data-driven and model analysis. medRxiv preprint, April 24, 2020. [CrossRef]
26. Chatterjee K., Chatterjee K., Kumar A., Shankar S. Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model. Medical Journal Armed Forces India 2020;76:147–155. [CrossRef]
27. Contreras S., Villavicencio HA., Medina-Ortiz D., Biron-Lattes J. P., Olivera-Nappa A., A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations, Chaos, Solitons & Fractals 2020;136:109925. [CrossRef]
28. Sinha, D. N. (2020). Mathematical Modeling to Estimate the Reproductive Number and the Outbreak Size of COVID-19: The case of India and the World. [CrossRef]
29. Ugo Avila Ponce de Leon, Angel GC Perez, and Eric Avila-Vales. A data driven analysis and forecast of an SEIARD epidemic model for covid-19 in mexico. arXiv preprint arXiv:2004.08288, 2020. [CrossRef]
30. Jia J., Ding J., Liu S., Liao G., Li J., Duan B., et al. Modeling the control of COVID-19: Impact of policy interventions and meteorological factors. Electronic Journal of Differential Equations. 2020, 23: 1-24.
31. Senapati A., Rana S., Das T., Chattopadhyay, J. Impact of intervention on the spread of COVID-19 in India: A model-based study. ArXiv preprint. 2020. 2004.04950.
32. Pal D., Ghosh D., Santra PK., Mahapatra G.S. Mathematical Analysis of a COVID-19 Epidemic Model by using Data-Driven Epidemiological Parameters of Diseases Spread in India. medRxiv Preprint. 2020. [CrossRef]
33. Menon A., Rajendran NK., Chandrachud A., Setlur G. (). Modeling and simulation of COVID-19 propagation in a large population with specific reference to India. medRxiv Preprint. 2020.
34. Mahajan A, Sivadas NA, Solanki R. An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India. Chaos, Solitons and Fractals 140 (2020) 110156. https://doi.org/10.1016/j.chaos.2020.110156
35. Algerian health and hospital reform minister: Carte épidémiologique. Available on: https://www.covid19.gov.dz/carte/ (Accessed on August, 25 2020).