Abstract
This paper discusses the logistic equation subject to uncertainties in the intrinsic growth rate, \(\alpha \), in the initial population density, \(N_0\), and in the environmental carrying capacity, K. These parameters are treated as independent random variables. The random variable transformation method is applied to compute the first probability density function of the time–population density, N(t), and of its inflection point, \(t^{*}\). Results for the density functions of N(t), for a fixed \(t>0\), and \(t^{*}\) are also provided for \(\alpha \), \(N_0\) and K uniformly distributed. Finally, numerical experiments illustrate the proposed theoretical results.
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References
Casabán MC, Cortés JC, Romero JV, Roselló MD (2015) Probabilistic solution of random SI-type epidemiological models using the random variable transformation technique. Commun Nonlinear Sci Numer Simul 24:86–97
Casabán MC, Cortés JC, Navarro-Quiles A, Romero JV, Roselló MD, Villanueva RJ (2016) A comprehensive probabilistic solution of random SIS-type epidemiological models using the random variable transformation technique. Commun Nonlinear Sci Numer Simul 32:199–210
Dorini FA, Cecconello MS, Dorini LB (2016) On the logistic equation subject to uncertainties in the environmental carrying capacity and initial population density. Commun Nonlinear Sci Numer Simul 33:160–173
Henderson D, Plaschko P (2006) Stochastic differential equations in science and engineering. World Scientific, Singapore
Holland EP, Burrow JF, Dytham C, Aegerter JN (2009) Modelling with uncertainty: introducing a probabilistic framework to predict animal population dynamics. Ecol Model 220:1203–1217
Kegan B, West RW (2005) Modeling the simple epidemic with deterministic differential equations and random initial conditions. Math Biosci 195:179–193
Kot M (2001) Elements of mathematical ecology. Cambridge University Press, Cambridge
Lek S (2007) Uncertainty in ecological models. Ecol Model 207:1–2
Nasell I (2003) Moment closure and the stochastic logistic model. Theor Popul Biol 63:159–168
Soong TT (1973) Random differential equations in science and engineering. Academic Press, New York
Udwadia FE (1989) Some results on maximum entropy distributions for parameters known to lie in finite intervals. SIAM Rev 31:103–109
Wake GC, Watt SD (1996) The relaxation of May’s conjecture for the logistic equation. Appl Math Lett 9:59–62
Acknowledgements
First author acknowledges the financial support from the Brazilian Council for Development of Science and Technology-CNPq (Grants Numbers 308149/2013-0 and 482320/2013-3).
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Communicated by Jose Alberto Cuminato.
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Dorini, F.A., Bobko, N. & Dorini, L.B. A note on the logistic equation subject to uncertainties in parameters. Comp. Appl. Math. 37, 1496–1506 (2018). https://doi.org/10.1007/s40314-016-0409-6
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DOI: https://doi.org/10.1007/s40314-016-0409-6
Keywords
- Logistic equation
- Uncertainties
- Random variable transformation technique
- First probability density function