• A. A. Ayoade Department of Mathematics, University of Lagos, Lagos, Nigeria
  • T. Latunde, Lecturer Department of Mathematics, Federal University Oye-Ekiti, Oye-Ekiti, Nigeria
  • R. O. Folaranmi Department of Mathematical and Computing Sciences, KolaDaisi University, Ibadan, Nigeria



Coronavirus; incubation period; infectivity; case fatality ratio; reproduction number.


Coronavirus epidemics emerged in the 1960s and the world has witnessed seven coronavirus outbreaks since then. Four of the coronaviruses instigate human influenza while the rest: Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) trigger severe respiratory disorders (SARS, MERS and COVID-19 respectively). The etiology of SARS, MERS and COVID-19 are similar but their epidemiology, in terms of incubation period, infectivity, case fatality ratio and the serial interval differ. In an attempt to compare the infectivity and case fatality ratio of the diseases, a mathematical model was considered for each disease. The key epidemiological quantity, the basic reproduction number, was derived for each model to examine the transmission potential of each disease. The mortality rates for the diseases were also investigated by considering the global report of COVID-19 as of October 1 2020 together with the history of SARS and MERS. Results from the computations showed that COVID-19 had the highest transmission potential and at the same time the lowest case fatality ratio. It was also revealed that COVID-19 would have wrecked more havocs had its case fatality ratio was as high as that of MERS.


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Wikipedia Website.

Ngonghala C N, Iboi E, Eikenberry S, Scotch M, MacIntyre C R, Bonds M H and Gumel AB.

Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel coronavirus. Maths Biosci., 2020, 325, 108364

Al-Asuoad N, Alaswad S, Rong L and Shillor M. Biomath., 5, 2016; 1-12,

Cakir Z and Savas H B. Elec. J. Gen. Med., 7, 2020, 34-48,

Khan M A and Atangana A. Alexandra Eng. J., 3, 2020, 44-57,

Naheed A, Singh M and Lucy D. Numerical study of SARS epidemic model with the

inclusion of diffusion in the system. Appl. Maths and Comput., 2014, 229, 480–498

Cao C, Chen W, Zheng S, Zhao J, Wang J and Cao W. Hindawi Publishing Corporation

BioMed Res Intl,

Anderson R M, Fraser C, Ghani A C, Donnelly C A, Riley S, Ferguson N M, Leung G M,

Lam T H and Hedley A J. Phil. Trans. R. Soc. Lond. B (2004), 359, 2020, 1091-1105, DOI


Bauch C T, Lloyd-Smith J O, Coffee M P and Galvani A P. Epidemiology, 16(6), 2005, 791-

, DOI: 10.1097/01.ede.0000181633.80269.4c

Naheed, A, Singh, M and Lucy, D. Effect of treatment on transmission dynamics of SARS

epidemic. HSOA J. of Infect and Non Infect Dis., 2016, 2(1), 1-11

Guanghong D, Chang L, Jianqiu G, Ling W, Ke C and Di Z. Chinese Science Bulletin,

(21), 2004, 2332-2338, DOI: 10.1360/04we0073

Stein R A. Int. J. Infect Dis., 12, 2011, 25-36, doi:10.1016/j.ijid.2010.06.020

Chang H-J. Chang BioMed Eng OnLine, 5, 2017, 66-75, DOI 10.1186/s12938‑017‑0370‑7

Tahir M, Ali Shah I S, Zaman G and Khan T. Prevention strategies for mathematical model

MERS-coronavirus with stability analysis and optimal control. J of Nanosci and Nanotech Appls,

, 3(1), 1-11

Eikenberry S E, Mancuso M, Iboi E, Phan T, Kostelich T E, Kuang Y and Gumel A B

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail

the COVID-19 pandemic. Inf. Dis. Modeling, 2020, 5, 293–308

Kucharski A J, Russell T W, Diamond C, Liu Y, Edmunds J, Funk S, Eggo R M Sun F, Jit

M, Munday J D et al. Early dynamics of transmission and control of COVID-19: a mathematical

modeling study. The Lancet Infectious Diseases, 2020

Asian I, Demir M, Wise M G and Lenhart S. medRxiv preprint. [accessed 30 June 2020].

Liu Y, Gayle A A, Wilder-Smith A and Rocklv J. J. of Travel Med. 3, 2020, 67-79, doi:


Ivorra B, Ferrndez M R, Vela-Prez M and Ramos A M. Communi in Nonlinear Sci and

Numer Simula., 1, 2020, 108-116,

Atangana A. Modeling the spread of COVID-19 with new fractal-fractional operators: can

the lockdown save mankind before vaccination? Chaos, Soliton& Fractals, 2020, 136, 109860

Gralinski L E and Menachery V D. Return of the coronavirus: 2019-nCoV. Viruses, 2020,

, 1-9

Aguilar J B, Faust G S M, Westafer L M and Gutierrez J B. Preprint,


Xinmiao R, Liu Y, Huidi C and Meng F. Effect of delay in diagnosis on transmission of

COVID-19. Math Biosci and Engine., 2020, 17(2020), (mbe-17-03149),2725

Fang Y, Nie Y and Penny M. Transmission dynamics of the COVID-19 outbreak and

effectiveness of government interventions: a data-driven analysis. J. of Med. Virology, 2020,

(2020), 6-21

Chowell G, Abdirizak I, Lee S, Lee J, Jung E, Nishiura H and Viboud C. BMC Medicine, 3

, 6-15, DOI 10.1186/s12916-015-0450-0

Chen T-M, Rui J, Wang Q-P, Zhao Z-Y, Cui J-A and Yin L. Infect Dis of Poverty, 9, 2020,


Prompetchara E, Ketloy C and Palaga T. Asian Pacific Journal of Allergy and Immunology,

, 2020, 15-28, DOI 10.12932/AP-200220-0772

Mkhatshwa T P. Modeling super-spreading events for SARS. Unpublished M.A.

Dissertation, Marshall University, 2010. Retrieved on August 7, 2020 from

Yang C and Wang J. Math Biosci and Engine, 17(1), 2020, 2708-2724,

DOI: 10.3934/mbe.2020148

Brauer F and Wu J. Modeling SARS, West Nile virus, pandemic influenza and other

emerging infectious diseases: a Canadian team’s adventure. In Modeling and dynamics of infectious diseases, volume 11 of Ser. Contemp. Appl. Math. CAM. Beijing: Higher

Ed. Press, 2009, pp. 36-63.

Gumel AB. Using mathematics to understand and control the 2019 novel coronavirus

pandemic. This Day Live, May 3, 2020. Available at [Accessed May 4, 2020].

Coronavirus Disease 2019 (COVID-19) – Africa CDC. Retrieved on September 5, 2020


Peak C M, Childs L M and Grad Y H. Comparing nonpharmaceutical interventions for

containing emerging epidemics. Proc Natl Acad Sci U S A, 2017, 114, 4023–4028

Cauchemez S, Fraser C, Van Kerkhove M D, Donnelly C A, Riley S, Rambaut A, et al.

Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic,

surveillance biases, and transmissibility. Lancet Infect Dis., 2014, 14, 50–56

World Health Organization. MERS situation update, January 2020. Retrieved May15,


Muhammad A S, Suliman K, Abeer K, Nadia B and Rabeea S. J. Advanced Res., 24, 2020,





How to Cite

AYOADE, A. A.; LATUNDE, T.; FOLARANMI, R. O. COMPARATIVE ANALYSIS OF TRANSMISSIBILITY AND CASE FATALITY RATIO OF SARS, MERS AND COVID-19 VIA A MATHEMATICAL MODELING APPROACH. Journal of Fundamental and Applied Sciences, [S. l.], v. 13, n. 3, p. 1262–1274, 2021. DOI: 10.4314/jfas.v13i3.7. Disponível em: Acesso em: 21 jun. 2024.




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