Research article

On stiff, fuzzy IRD-14 day average transmission model of COVID-19 pandemic disease

  • Received: 05 June 2020 Accepted: 08 July 2020 Published: 14 July 2020
  • COVID-19, a new pandemic disease is becoming one of the major threats for surviving. Many new models are arrived to study the disease mathematically. Here we are introducing a new model in which instead of studying a day by day changes we are studying the average of 14 day transmission because its life or the patients incubation period is about an average of 14 days. Also, since this is pandemic, and being not aware of susceptible population among the world's population, we considered the model without S-susceptible population. i.e., IRD-Infectious, Recovered, Death-model. In this new model, we are also introducing a new method of calculating new number called N0-average transmission number. This is used to study the average spread of infection instead of basic reproduction number R0. The motto of this paper is not to predict the daily cases but to control the current spread of disease and deaths by identifying the threshold number, exceeding which will increase the spread of infection and number of deaths due to this pandemic. Also if the 14 day average IRD-populations are maintained under this threshold number, will definitely control this pandemic disease globally. Stability analysis and test for stiff system of differential equations are studied. Our main aim is to present the medical world, a threshold population of infected, recovered and death cases for every average of 14 days to quickly overcome this pandemic disease COVID-19.

    Citation: Prasantha Bharathi Dhandapani, Dumitru Baleanu, Jayakumar Thippan, Vinoth Sivakumar. On stiff, fuzzy IRD-14 day average transmission model of COVID-19 pandemic disease[J]. AIMS Bioengineering, 2020, 7(4): 208-223. doi: 10.3934/bioeng.2020018

    Related Papers:

  • COVID-19, a new pandemic disease is becoming one of the major threats for surviving. Many new models are arrived to study the disease mathematically. Here we are introducing a new model in which instead of studying a day by day changes we are studying the average of 14 day transmission because its life or the patients incubation period is about an average of 14 days. Also, since this is pandemic, and being not aware of susceptible population among the world's population, we considered the model without S-susceptible population. i.e., IRD-Infectious, Recovered, Death-model. In this new model, we are also introducing a new method of calculating new number called N0-average transmission number. This is used to study the average spread of infection instead of basic reproduction number R0. The motto of this paper is not to predict the daily cases but to control the current spread of disease and deaths by identifying the threshold number, exceeding which will increase the spread of infection and number of deaths due to this pandemic. Also if the 14 day average IRD-populations are maintained under this threshold number, will definitely control this pandemic disease globally. Stability analysis and test for stiff system of differential equations are studied. Our main aim is to present the medical world, a threshold population of infected, recovered and death cases for every average of 14 days to quickly overcome this pandemic disease COVID-19.


    加载中

    Acknowledgments



    This research paper was not supported by any funds or grants from any government or non-government sectors. The authors would like to thank the anonymous reviewers for their useful suggestions in making the paper a better one.

    Conflict of interest



    All authors declare no conflict of interest.

    [1] Zadeh LA (1965) Fuzzy sets. Inform Contr 8: 338-353. doi: 10.1016/S0019-9958(65)90241-X
    [2] Chang SSL, Zadeh LA (1972) On fuzzy mapping and control. doi: 10.1109/TSMC.1972.5408553
    [3] Buckley JJ, Feuring T (2000) Fuzzy differential equations. Fuzzy Set Syst 110: 43-54. doi: 10.1016/S0165-0114(98)00141-9
    [4] Dubois D, Prade H (1982) Towards fuzzy differential calculus, Part 3: Differentiation. Fuzzy Set Syst 8: 225-233. doi: 10.1016/S0165-0114(82)80001-8
    [5] Lupulescu V (2009) On a class of fuzzy functional differential equations. Fuzzy set Syst 160: 1547-1562. doi: 10.1016/j.fss.2008.07.005
    [6] Kaleva O (1987) Fuzzy differential equations. Fuzzy Set Syst 24: 301-317. doi: 10.1016/0165-0114(87)90029-7
    [7] Kaleva O (1990) The Cauchy problem for fuzzy differential equations. Fuzzy Set Syst 35: 389-386. doi: 10.1016/0165-0114(90)90010-4
    [8] Ma M, Friedman M, Kandel A (1999) Numerical solutions of fuzzy differential equations. Fuzzy Set Syst 105: 133-138. doi: 10.1016/S0165-0114(97)00233-9
    [9] Seikkala S (1987) On the fuzzy initial value problem. Fuzzy Set Syst 24: 319-330. doi: 10.1016/0165-0114(87)90030-3
    [10] Diamond P, Kloeden P (1984)  Metric Spaces of Fuzzy Sets: Theory and Applications Singapore: World Scientific.
    [11] Chalco-Cano Y, Roman-Flores H (2008) On new solutions of fuzzy differential equation. Chaos Soliton Fract 38: 112-119. doi: 10.1016/j.chaos.2006.10.043
    [12] Abbasbandy S, Viranloo TA (2002) Numerical solution of fuzzy differential equation by Taylor method. Comput Meth Appl mat 2: 113-124. doi: 10.2478/cmam-2002-0006
    [13] Abbasbandy S, Viranloo TA (2004) Numerical solution of fuzzy differential equation by Runge-Kutta method. Nonlinear Stud 11: 117-129.
    [14] Curtiss CF, Hirschfelder JO (1952) Integration of stiff equations. Proc Natl Acad Sci USA 38: 235-243. doi: 10.1073/pnas.38.3.235
    [15] Söderlind G, Jay L, Calvo M (2015) Stiffness 1952–2012: Sixty years in search of a definition. BIT Numer Math 55: 531-558. doi: 10.1007/s10543-014-0503-3
    [16] Shampine LF (1981) Evaluation of a test set for stiff ODE solvers. ACM Trans Math Softw 7: 409-420. doi: 10.1145/355972.355973
    [17] Higham DJ, Trefethen LN (1993) Stiffness of ODEs. BIT 33: 285-303. doi: 10.1007/BF01989751
    [18] Spijker MN (1996) Stiffness in numerical initial-value problems. J Comp Appl Math 72: 393-406. doi: 10.1016/0377-0427(96)00009-X
    [19] Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc Roy Soc Lond A 115: 700-721. doi: 10.1098/rspa.1927.0118
    [20] Palese P, Young JF (1982) Variation of influenza A, B, and C viruses. Science 215: 1468-1474. doi: 10.1126/science.7038875
    [21] Allen LJS (2007)  An Introduction to Mathematical Biology NJ: Prentice Hall.
    [22] He S, Tang S, Rong L (2020) A discrete stochastic model of the COVID-19 outbreak: Forecast and control. MBE 17: 2792-2804. doi: 10.3934/mbe.2020153
    [23] Zhou W, Wang A, Xia F, et al. (2020) Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak. MBE 17: 2693-2707. doi: 10.3934/mbe.2020147
    [24] Yin F, Lv J, Zhang X, et al. (2020) COVID-19 information propagation dynamics in the chinese sina-microblog. MBE 17: 2676-2692. doi: 10.3934/mbe.2020146
    [25] Dai C, Yang J, Wang K (2020) Evaluation of prevention and control interventions and its impact on the epidemic of coronavirus disease 2019 in Chongqing and Guizhou Provinces. MBE 17: 2781-2791. doi: 10.3934/mbe.2020152
    [26] Rong X, Yang L, Chu H, et al. (2020) Effect of delay in diagnosis on transmission of COVID-19. MBE 17: 2725-2740. doi: 10.3934/mbe.2020149
    [27] Tian J, Wu J, Bao Y, et al. (2020) Modeling analysis of COVID-19 based on morbidity data in Anhui, China. MBE 17: 2842-2852. doi: 10.3934/mbe.2020158
    [28] Li C, Xu J, Liu J, et al. (2020) The within-host viral kinetics of SARS-CoV-2. MBE 17: 2853-2861. doi: 10.3934/mbe.2020159
    [29] Volpert V, Banerjee M, Petrovskii S (2020) On a quarantine model of coronavirus infection and data analysis. Math Model Nat Phenom 15: 24. doi: 10.1051/mmnp/2020006
    [30] Tang B, Bragazzi NL, Li Q, et al. (2020) An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect Dis Model 5: 248-225.
    [31] Yang Z, Zeng Z, Wang K, et al. (2020) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 12: 165-174. doi: 10.21037/jtd.2020.02.64
    [32] Tuli S, Tuli S, Tuli R, et al. (2020) Predicting the growth and trend of COVID-19 pandemic using machine learning and Cloud computing. Internet Thing 11: 100222. doi: 10.1016/j.iot.2020.100222
    [33] Pai C, Bhaskar A, Rawoot V (2020) Investigating the dynamics of COVID-19 pandemic in India under lockdown. Chaos Soliton Fract 138: 109988. doi: 10.1016/j.chaos.2020.109988
    [34] Ribeiro MHDM, Da Silva RG, Mariani VC, et al. (2020) Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos Soliton Fract 135: 109853. doi: 10.1016/j.chaos.2020.109853
    [35] Khan MA, Atangana A (2020) Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative. doi: 10.1016/j.aej.2020.02.033
    [36] Gong X, Fatmawati, Khan MA (2020) A numerical solution of the competition model among bank data in Caputo-Fabrizio derivative. doi: 10.1016/j.aej.2020.02.008
    [37] Jan R, Khan MA, Gómez-Aguilar JF (2020) Asymptomatic carriers in transmission dynamics of dengue with control interventions. Optim Control Appl Meth 41: 430-447. doi: 10.1002/oca.2551
    [38] Khan MA, Ullah S, Ullah S, et al. (2020) Fractional order SEIR model with generalized incidence rate. AIMS Mathematics 5: 2843-2857. doi: 10.3934/math.2020182
    [39] Windarto, Khan MA, Fatmawati (2020) Parameter estimation and fractional derivatives of dengue transmission model. AIMS Mathematics 5: 2758-2779. doi: 10.3934/math.2020178
    [40] Dhandapani PB, Baleanu D, Thippan J, et al. (2019) Fuzzy type RK4 solutions to fuzzy hybrid retarded delay differential equations. Front Phys 7: 168. doi: 10.3389/fphy.2019.00168
    [41] Dhandapani PB, Thippan J, Sivakumar V (2019) Numerical solution of fuzzy multiple hybrid single retarded delay differential equations. Int J Recent Technol Eng 8: 1946-1949.
    [42] Dhandapani PB, Thippan J, Sivakumar V (2019) Numerical solutions of fuzzy multiple hybrid single neutral delay differential equations. Int J Sci Technol Res 8: 520-523.
    [43] Daily updates of coronavirus COVID-19 pandemic disease.Available from: https://www.worldometers.info/coronavirus/.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4699) PDF downloads(292) Cited by(9)

Article outline

Figures and Tables

Figures(7)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog