ANOVA to Compare Three Methods to Track COVID-19 in Nine Countries

ANOVA en la comparación de tres métodos para rastrear COVID-19 en nueve países

  • Jorge Juárez-Lucero Instituto Nacional de Astrofísica Óptica y Electrónica
  • Anabel Sánchez-Sánchez INSTITUTO NACIONAL DE ASTROFÍSICA ÓPTICA Y ELECTRÓNICA
  • Raquel Díaz-Hernández Instituto Nacional de Astrofísica Óptica y Electrónica
  • María del Rayo Guevara-Villa Universidad Politécnica de Puebla
  • Altamirano-Robles L. Instituto Nacional de Astrofísica Óptica y Electrónica
Keywords: One-way ANOVA, nonlinear regression, SIR, SEIR, COVID-19

Abstract

A new coronavirus denominated first 2019-nCoV and later SARS-CoV-2 was found in Wuhan, China in December of 2019. This paper compares three mathematical methods: nonlinear regression, SIR, and SEIR epidemic models, to track the covid-19 disease in nine countries affected by the SARS-CoV-2 virus, to help epidemiologists to know the disease trajectory, considering initial data in the pandemic, mainly 100 days from the beginning. To evaluate the results obtained with the three methods one-way ANOVA is applied. The average of predicted infected cases with SARS-CoV-2, obtained with the mentioned methods was: for United States of America 1,098,508, followed by Spain with 226,721, Italy with 202,953, France with 183,897 United Kingdom with 182,190, Germany with 159,407, Canada with 58,696, Mexico with 50,366 and Argentina with 4,860 in average. The one-way ANOVA does not show a significant difference among the results of the projected infected cases by SARS-CoV-2, using nonlinear regression, SIR, and SEIR epidemic methods. The above could mean that initially any method can be used to model the pandemic course.

Author Biography

Jorge Juárez-Lucero, Instituto Nacional de Astrofísica Óptica y Electrónica

Biomedical science and technologies 

References

Phan T. Novel coronavirus: From discovery to clinical diagnostics. Infect Gen Evol [Internet]. 2020;79:104210-104211. Available from: https://doi.org/10.1016/j.meegid.2020.104211

Xie M, Chen Q. Insight into 2019 novel coronavirus - an updated intrim review and lessons from SARS-CoV and MERS-CoV. Int J Infect Dis [Internet]. 2020;94:119-124. Available from: https://doi.org/10.1016/j.ijid.2020.03.071

Lupia T, Scabini S, Mornese-Pinna S, Di-Perri G, Giuseppe-De-Rosa F, Corcione S. 2019 novel coronavirus (2019-nCoV) outbreak: A new challenge. J Global Antimicrob Resist. [Internet] 2020;21:22-27. Available from: https://doi.org/10.1016/j.jgar.2020.02.021

Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R. COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. J Adv Res [Internet]. 2020;24:91-98. Available from: https://doi.org/10.1016/j.jare.2020.03.005

Ghinai I, McPherson TD, Hunter JC, Kirking HL, Christiansen D, Joshi K, et al. First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the US. The Lancet [Internet]. 2020;395(10230):1137-1144. Available from: https://doi.org/10.1016/S0140-6736(20)30607-3

Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agents [Internet]. 2020;55(3):105924. Available from: https://doi.org/10.1016/j.ijantimicag.2020.105924

Luk HKH, Li X, Fung J, Lau SPK, Woo PCY. Molecular epidemiology, evolution, and phylogeny of SARS coronavirus. Infect Genet Evol [Internet]. 2019;71(Jul):21-30. Available from: https://doi.org/10.1016/j.meegid.2019.03.001

Ramadan N, Shaib H. Middle East respiratory syndrome coronavirus (MERS-CoV): A review. Germs [Internet]. 2019;9(1):35-42. Available from: https://dx.doi.org/10.18683%2Fgerms.2019.1155

Law S, Leung AW, Xu C. Severe Acute Respiratory Syndrome (SARS) and Coronavirus disease-2019 (COVID-19): From Causes to Preventions in Hong Kong. Int J Infect Dis [Internet]. 2020;94:156-163. Available from: https://doi.org/10.1016/j.ijid.2020.03.059

Wong ACP, Li X, Lau SKP, Woo PC. Global Epidemiology of Bat Coronaviruses. Viruses [Internet]. 2019; 11(2):174. Available from: https://doi.org/10.3390/v11020174

Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, He D. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis [Internet]. 2020; 93:211-216. Available from: https://doi.org/10.1016/j.ijid.2020.02.058

Shatnawi M, Lazarova-Molnar S, Zaki N. Modeling and simulation of epidemic spread: Recent advances. In: 9th International Conference on Innovations in Information Technology (IIT) [Internet]. Abu Dhabi: IEEE; 2013:118-123. Available from: https://doi.org/10.1109/Innovations.2013.6544404

Ershkov SV, Christianto V, Rachinskaya A., Prosviryakov EY. A nonlinear heuristic model for estimation of Covid-19 impact to world. Roma Rep Phys [Internet]. 2020;72:1-16. Available from: http://www.rrp.infim.ro/2020/AN72605.pdf

Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Soliton Fract [Internet]. 2020; 139:110057. Available form: https://doi.org/10.1016/j.chaos.2020.110057

He S, Peng Y, Sun K. SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dyn [Internet]. 2020; 101:1667–1680. Available from: https://doi.org/10.1007/s11071-020-05743-y

Brauer F, Castillo-Chávez C. Discrete population models. In: Mathematical models in population biology and epidemiology [Internet]. New York: Springer; 2001. 51-94p. Available from: https://doi.org/10.1007/978-1-4757-3516-1_2

Agrawal A, Tenguria A, Modi G. MATLAB Programming for Simulation of an SIR Deterministic Epidemic Model. IJMTT [Internet]. 2017;50(1):71-73. Available from: https://doi.org/10.14445/22315373/IJMTT-V50P509

Al-Sheikh SA. Modeling and Analysis of an SEIR Epidemic Model with a Limited Resource for Treatment. GJSFR Mathematics and Decision Sciences [Internet]. 2012; 12(14):56-66. Available from: https://globaljournals.org/GJSFR_Volume12/5-Modeling-and-Analysis-of-an-SEIR-Epidemic.pdf

Agrawal A, Tenguria A, Modi G. Global analysis of an SEIRS epidemic model with saturated incidence and saturated treatment. AJOMCOR [Internet]. 2017;22(2):43-56. Available from: https://www.ikprress.org/index.php/AJOMCOR/article/view/1150

Yan P, Liu S. SEIR epidemic model with delay. ANZIAM J [Internet]. 2006;48(1):119-134. Available from: https://doi.org/10.1017/S144618110000345X

Stehlé J, Voirin N, Barrat A, Cattuto C, Colizza V, Isella L, Vanhems P. Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med [Internet]. 2011;9:87. Available from: https://doi.org/10.1186/1741-7015-9-87

Siriprapaiwan S, Moore EJ, Koonprasert S. Generalized reproduction numbers, sensitivity analysis and critical immunity levels of an SEQIJR disease model with immunization and varying total population size. Math Comput Simul [Internet]. 2018;146:70-89. Available from: https://doi.org/10.1016/j.matcom.2017.10.006

McGee J. MathWorks. Coronavirus Tracker - Country Modeling [Internet]. 2020; Available from: https://github.com/joshmcgee24/coronavirustracker

Published
2021-01-28
How to Cite
Juárez Lucero, J. J., Sánchez-Sánchez, A., Díaz-Hernández, R., Guevara-Villa, M. del R., & Altamirano-Robles, L. (2021). ANOVA to Compare Three Methods to Track COVID-19 in Nine Countries: ANOVA en la comparación de tres métodos para rastrear COVID-19 en nueve países. Mexican Journal of Biomedical Engineering, 42(1), 36-46. Retrieved from http://www.rmib.mx/index.php/rmib/article/view/1110
Section
Special Issue