Journal of Applied Nonlinear Dynamics
A Mathematical Study of Pandemic COVID-19 Virus with Special Emphasis on Uncertain Environments
Journal of Applied Nonlinear Dynamics 11(2) (2022) 427--457 | DOI:10.5890/JAND.2022.06.012
Subhashis Das$^1$, Prasenjit Mahato$^1$, Sanat Kumar Mahato$^1$, Debkumar Pal$^2$
$^1$ Department of Mathematics, Sidho-Kanho-Birsha University, Purulia, 723104, West Bengal, India
$^2$ Chandrahati Dilip Kumar High School (H.S.), Chandrahati, 712504, West Bengal, India
Download Full Text PDF
Abstract
\textbf{\textit{Background {$\&$} objectives}}\textbf{: }Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a highly infectious virus which causes the severe respiratory disease for human also known as Coronavirus Disease (COVID) emerged in China in December 2019 that spread rapidly all over the world. As there is no proper medicine or vaccine against the virus SARS-CoV-2 or COVID-19 to control the spread of the virus, all the countries are taking many steps as preventive measures, like lockdown, stay-at-home, social distancing, sanitization, use of mask, etc. For almost three months of lockdown many countries are relaxing the lockdown period and the movement of people. The objective of this study is to develop a new mathematical model, called the SEIQRS model in imprecise environment and to find out the essentiality of quarantine, stay-at-home orders, lockdown as precautionary measures to protect the human community. \par \textbf{\textit{Methods}}\textbf{: }In this study, after developing the COVID-19 SEIQRS model, the SEIQRS fuzzy model and the SEIQRS interval model are constructed by taking parameters as triangular fuzzy numbers and interval numbers respectively. Solution curves are drawn for two imprecise models by using MATLAB R2014a software package and the sensitivity analysis is also performed with respect to the control parameters. The next generation matrix approach is adopted to calculate the basic reproduction number ($R_0$) from the SEIQRS model to assess the transmissibility of the SARS-CoV-2. \par \textbf{\textit{Results}}\textbf{: }The basic reproduction number ($R_0$) is calculated for this model and to get the stability and disease free equilibrium the value of the basic reproduction number must be less than 1. Also, we find the solution curves in different uncertain environments and sensitivity studies show the importance of newly added population $(\alpha)$, rate of spreading asymptomatic infection $(\beta)$, rate of developing symptoms of infection
$(\lambda)$, proportion of infected population in quarantine $(\gamma)$. \par \textbf{\textit{Interpretation {$\&$} conclusions}}\textbf{:} Our model shows that quarantine, lockdown are essential to control the spread of the disease as at present there is no such medicine or vaccine to combat COVID-19. Once the virus establishes transmission within the community, it will very difficult to stop the infection. As a measure of public health, healthcare and community preparedness, it would be serious to control any impending outbreak of COVID-19 in the country.
Acknowledgments
Authors are thankful to the respected referees for their constructive suggestions for the improvement of this research work. We also remain grateful to the Editor of this journal for considering our article for publication. The first and third authors would like to acknowledge the financial support by DST-INSPIRE, Government of India, Ministry of Science \& Technology, New Delhi, India
(DST/INSPIRE Fellowship/2017/IF170166).
References
-
[1]  | Viboud, C., Simonsen, L., Fuentes, R., Flores, J., Miller, M.A., and
Chowell, G. (2015), Global mortality impact of the 1957-1959 influenza
pandemic, J. Infect. Dis., 212, 738-745.
|
-
[2]  | Dawood, F.S., Iuliano, A.D., Reed, C., Meltzer, M.I., Shay, D.K., Cheng,
P.Y., Bandaranayake, D., Breiman, R.F., Brooks, W.A., Buchy, P., Feikin,
D.R., Fowler, K.B., Gordon, A., Hien, N.T., Horby, P., Huang, Q.S., Katz,
M.A., Krishnan, A., Lal, R., Montgomery, J.M., M{\o}lbak, K., Pebody, R.,
Presanis, A.M., Razuri, H., Steens, A., Tinoco, Y.O., Wallinga, J., Yu, H.,
Vong, S., Bresee, J., and Widdowson, M.A. (2012), Estimated global mortality
associated with the first 12 months of 2009 pandemic influenza A H1N1 virus
circulation: A modelling study, Lancet Infect. Dis., 12, 687-695.
|
-
[3]  | Bernoulli, D. and Blower, S. (2004), 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, 275-288.
|
-
[4]  | Hamer, W.H. (1906), Epidemic disease in England, Lancet., 1, 733-739.
|
-
[5]  | Kermack, W.O. and McKendrick, A.G. (1927), A Contribution to the
Mathematical Theory of Epidemics, Proc. R. Soc. A Math. Phys. Eng. Sci.,
115,
700-721.
|
-
[6]  | Bailey, N.T.J. (1975), The Mathematical Theory of Inectious Disease and
its Applications, Charles Griffins and Company, london.
|
-
[7]  | Meng, X. and Chen, L. (2008), Global Dynamical Behaviors for an SIR
Epidemic Model with time delay and pause vaccination, Tawanese Journal of
Mathematics, 12(5), 1107-1122.
|
-
[8]  | Anderson, R.M. and May, R.M. (1986), The invasion, persistence and spread
of infectious diseases within animal and plant communities, Philos. Trans.
R. Soc. Lond. B. Biol. Sci., 314, 533-570.
|
-
[9]  | Brauer, F. and Castillo-Chavez, C. (2001), Mathematical Models in
Population Biology and Epidemiology. Springer.
|
-
[10]  | Chitnis, N., Hyman, J.M., and Cushing, J.M. (2008), Determining important
parameters in the spread of malaria through the sensitivity analysis of a
mathematical model, Bull. Math. Biol., 70, 1272-1296.
|
-
[11]  | Chitnist, N., Cushing, J.M., and Hyman, J.M. (2006), Bifurcation Analysis
of a Mathematical Model for Malaria Transmission, SIAM, 67(1), 24-45
|
-
[12]  | Chiyaka, C., Garira, W., and Dube, S. (2009), Effects of treatment and drug
resistance on the transmission dynamics of malaria in endemic areas,
Theor.
Popul. Biol., 75, 14-29.
|
-
[13]  | Kar, T.K. and Jana, S. (2013), Application of three controls optimally in
a vector-borne disease - a mathematical study, Commun. Nonlinear Sci. Numer.
Simul., 18, 2868-2884.
|
-
[14]  | Coronavirus COVID-19 (2019-nCoV),
https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html{\#} /bda7594740fd40299423467b48e9ecf6.
|
-
[15]  | COVID-19/Coronavirus Real Time Updates With Credible Sources in US and
Canada $\vert $ 1Point3Acres, https://coronavirus.1point3acres.com/en.
|
-
[16]  | Coronavirus - El mapa del coronavirus en Espa\~{n}a: 27.321 muertos y
m{a}s de 229.000 contagiados - RTVE.es,
https://www.rtve.es/noticias/20200514/mapa-del-coronavirus-espana/2004681.shtml.
|
-
[17]  | COVID-19 ITALIA - Desktop,
https://opendathunadpc.maps.arcgis.com/apps/opsdashboard/index.html{\#} /b0c68bce2cce478eaac82fe38d4138b1.
|
-
[18]  | MoHFW $\vert $ Home, https://www.mohfw.gov.in/.
|
-
[19]  | Khajanchi, S., Sarkar, K., Mondal, J., and Perc, M. (2020), Dynamics of the
COVID-19 pandemic in India, 1-43, https://arxiv.org/abs/2005.06286.
|
-
[20]  | Sarkar, K. and Khajanchi, S. (2020), Modeling and forecasting of the
COVID-19 pandemic in India, Chaos, Solutons {$\&$ Fractals}, 139, 110049.
|
-
[21]  | Weitz, J.S., Beckett, S.J., Coenen, A.R., Demory, D., Dominguez-Mirazo, M., Dushoff, J., Leung, C.Y., Li, G., Magalie, A., Park, S.W., and Rodriguez-Gonzalez, R. (2020), Modeling shield
immunity to reduce COVID-19 epidemic spread, Nat. Med.,
DOI:10.1038/s41591-020-0895-3.
|
-
[22]  | Khajji, B., Kada, D., Balatif, O., and Rachik, M. (2020), A multi-region
discrete time mathematical modeling of the dynamics of Covid-19 virus
propagation using optimal control, J. Appl. Math. Comput., 1-27.
|
-
[23]  | Handayani, L. (2020), The outbreak's modeling of Coronavirus (COVID-19)
using the modified SEIR model in indonesia, 5, 61-68.
|
-
[24]  | Mandal, S., Bhatnagar, T., Arinaminpathy, N., Agarwal, A., Chowdhury,
A., Murhekar, M., Gangakhedkar, R.R., and Sarkar, S. (2020), Prudent public
health intervention strategies to control the coronavirus disease 2019
transmission in India: A mathematical model-based approach, Indian J. Med.
Res., 1, 190-199.
|
-
[25]  | Victor, A. (2020), Mathematical Predictions for COVID-19 As a Global
Pandemic, SSRN Electron. J., DOI:10.2139/ssrn.3555879.
|
-
[26]  | Chen, T.M., Rui, J., Wang, Q.P., Zhao, Z.Y., Cui, J.A., and Yin, L. (2020),
A mathematical model for simulating the phase-based transmissibility of a
novel coronavirus, Infect. Dis. Poverty., 9, 1-8.
|
-
[27]  | Ivorra, B., Ferr{a}ndez, M.R., Vela-P{e}rez, M., and Ramos, A.M.
(2020), Mathematical modeling of the spread of the coronavirus disease 2019
(COVID-19) taking into account the undetected infections. The case of China.
Commun, Nonlinear Sci. Numer. Simul., 2019, 105303.
|
-
[28]  | De Visscher, A. (2020), A COVID-19 Epidemiological Model for Community
and Policy Maker Use, 1-21.
|
-
[29]  | Liu, X., Hewings, G.J.D., Qin, M., Xiang, X., Zheng, S., Li, X., and Wang,
S. (2020), Modelling the Situation of COVID-19 and Effects of Different
Containment Strategies in China with Dynamic Differential Equations and
Parameters Estimation, SSRN Electron. J., DOI:10.2139/ssrn.3551359.
|
-
[30]  | Yang, C. and Wang, J. (2020), A mathematical model for the novel
coronavirus epidemic in Wuhan, China, Math. Biosci. Eng., 17, 2708-2724.
|
-
[31]  | Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J.,
Funk, S., Eggo, R.M., Sun, F., Jit, M., Munday, J.D., Davies, N., Gimma, A.,
van Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C.I., Clifford, S.,
Quilty, B.J., Bosse, N.I., Abbott, S., Klepac, P., and Flasche, S. (2020), Early
dynamics of transmission and control of COVID-19: a mathematical modelling
study, Lancet Infect. Dis., 20, 553-558.
|
-
[32]  | Panja, P., Mondal, S.K., and Chattopadhyay, J. (2017), Dynamical Study in
Fuzzy Threshold Dynamics of a Cholera Epidemic Model, Fuzzy Inf. Eng., 9,
381-401.
|
-
[33]  | Das, S., Mahato, P., and Mahato, S.K. (2021), Disease control prey-predator model incorporating prey refuge under fuzzy uncertainty,
Model. Earth Syst. Environ., 7, 2149-2166.
|
-
[34]  | Mahata, A., Mondal, S.P., Ahmadian, A., Ismail, F., Alam, S., and
Salahshour, S. (2018), Different Solution Strategies for Solving Epidemic
Model in Imprecise Environment, Complexity, 2018. DOI:10.1155/2018/4902142
|
-
[35]  | Das, A. and Pal, M. (2018), A mathematical study of an imprecise SIR
epidemic model with treatment control, J. Appl. Math. Comput., 56, 477-500.
|
-
[36]  | Cai, Y., Kang, Y., and Wang, W. (2017), A stochastic SIRS epidemic model
with nonlinear incidence rate, Appl. Math. Comput., 305, 221-240.
|
-
[37]  | Lahrouz, A., Omari, L., and Kiouach, D. (2011), Global analysis of a
deterministic and stochastic nonlinear SIRS epidemic model, Nonlinear Anal.
Model. Control., 16, 59-76.
|
-
[38]  | Chang, Z., Meng, X., and Lu, X. (2017), Analysis of a novel stochastic SIRS
epidemic model with two different saturated incidence rates, Phys. A Stat.
Mech. its Appl., 472, 103-116.
|
-
[39]  | Das, S., Mahato, P., and Mahato, S.K. (2020), A Prey Predator Model in Case
of Disease Transmission via Pest in Uncertain Environment, Differ. Equations
Dyn. Syst., 1-27. DOI:10.1007/s12591-020-00551-7
|
-
[40]  | Efimov, D., Ushirobira, R., Efimov, D., and Ushirobira, R. (2020), On an
interval prediction of COVID-19 development based on a SEIR epidemic model,
HAL Id? hal-02517866.
|
-
[41]  | Yew, K. and Meei, M. (2020), COVID-19: Development of a robust
mathematical model and simulation package with consideration for ageing
population and time delay for control action and resusceptibility,
Physica
D, 411, 132599.
|
-
[42]  | Kar, T.K. and Jana, S. (2013), A theoretical study on mathematical
modelling of an infectious disease with application of optimal control,
BioSystems, 111, 37-50.
|
-
[43]  | Chen, S.H. and Hsieh, C.H. (1999), Graded mean integration representation
of generalized fuzzy number, J. Chin. Fuzzy Syst. Assoc., 5(2), 1-7.
|
-
[44]  | Antosiewicz, H.A. (1963), Ordinary Differential Equations (G. Birkhoff
and G. C. Rota). SIAM Rev., 5, 160-161.
|
-
[45]  | Van Den Driessche, P. and Watmough, J. (2002), Reproduction numbers and
sub-threshold endemic equilibria for compartmental models of disease
transmission, Math. Biosci., 180, 29-48.
|
-
[46]  | Liu, Y., Gayle, A.A., Wilder-Smith, A., and Rockl\"{o}v, J. (2020), The
reproductive number of COVID-19 is higher compared to SARS coronavirus, J.
Travel Med., 27, 1-6.
|
-
[47]  | Clark, R.N. (2012), Routh's Stability Criterion, Control Syst. Dyn.,
472-477. \\DOI:10.1017/cbo9781139163873.020.
|
-
[48]  | Pontryagin, L.S., Mishchenko, E.F., Boltyanskii, V.G., and
Gamkrelidze, R.V. (1962), The mathematical theory of optimal processes,
http://cds.cern.ch/record/234445.
|
-
[49]  | Tang, B., Wang, X., Li, Q., Bragazzi, N.L., Tang, S., Xiao, Y., and Wu, J.
(2020), Estimation of the Transmission Risk of the 2019-nCoV and Its
Implication for Public Health Interventions, J. Clin. Med., 9, 462.
|
-
[50]  | Spencer, J., Shutt, D.P., Moser, S.K., Clegg, H., Wearing, H.J.,
Mukundan, H., and Manore, C.A. (2020), Epidemiological parameter review and
comparative dynamics of influenza, respiratory syncytial virus, rhinovirus,
human coronavirus, and adenovirus. medRxiv. DOI:10.1101/2020.02.04.20020404.
|