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Journal articles on the topic 'Age-structured model'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Dewi, Sonya, and Peter Chesson. "The age-structured lottery model." Theoretical Population Biology 64, no. 3 (November 2003): 331–43. http://dx.doi.org/10.1016/s0040-5809(03)00094-7.

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2

Cochran, John M., and Yongzhi Xu. "Age-structured dengue epidemic model." Applicable Analysis 93, no. 11 (July 10, 2014): 2249–76. http://dx.doi.org/10.1080/00036811.2014.918963.

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3

Bekkal-Brikci, Fadia, Khalid Boushaba, and Ovide Arino. "Nonlinear age structured model with cannibalism." Discrete & Continuous Dynamical Systems - B 7, no. 2 (2007): 201–18. http://dx.doi.org/10.3934/dcdsb.2007.7.201.

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4

McGarvey, Richard. "An Age-Structured Open-Access Fishery Model." Canadian Journal of Fisheries and Aquatic Sciences 51, no. 4 (April 1, 1994): 900–912. http://dx.doi.org/10.1139/f94-089.

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A dynamic model for open-access fisheries is presented. In addition to density dependence in recruitment and fishing effort changing in proportion to the level of profit fishermen earn which characterizes previous open-access models, it incorporates full age structure for the fish stock, lognormal environmental recruitment variability, and gear selectivity. The predator–prey cycling solution of the original Schaefer dynamic model, and subsequent open-access models, persists for these model extensions. Density dependence in recruitment induces greater global stability. Environmental recruitment variability, common in marine populations, is destabilizing in the neighborhood of the open-access equilibrium. These two influences, combined in the open-access fishery model, generate robust long-lasting irregular cycles of stock and effort. Volterra proved for the original Lotka–Volterra model that the time averages of the variables over one cycle were exactly equal to their equilibrium steady states. This is shown to extend as a good approximation for the model presented here. Approximating model steady states of effort and catch by the corresponding averages from data time series underlies a new algorithm of parameter evaluation, applied here to an open-access model of the Georges Bank sea scallop (Placopecten magellanicus) fishery.

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5

Hethcote, Herbert W. "An age-structured model for pertussis transmission." Mathematical Biosciences 145, no. 2 (October 1997): 89–136. http://dx.doi.org/10.1016/s0025-5564(97)00014-x.

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6

Gourley, S. A., and Rongsong Liu. "An Age-structured Model of Bird Migration." Mathematical Modelling of Natural Phenomena 10, no. 6 (2015): 61–76. http://dx.doi.org/10.1051/mmnp/201510606.

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7

Degond, Pierre, Angelika Manhart, and Hui Yu. "An age-structured continuum model for myxobacteria." Mathematical Models and Methods in Applied Sciences 28, no. 09 (August 2018): 1737–70. http://dx.doi.org/10.1142/s0218202518400043.

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Myxobacteria are social bacteria, that can glide in two dimensions and form counter-propagating, interacting waves. Here, we present a novel age-structured, continuous macroscopic model for the movement of myxobacteria. The derivation is based on microscopic interaction rules that can be formulated as a particle-based model and set within the Self-Organized Hydrodynamics (SOH) framework. The strength of this combined approach is that microscopic knowledge or data can be incorporated easily into the particle model, whilst the continuous model allows for easy numerical analysis of the different effects. However, we found that the derived macroscopic model lacks a diffusion term in the density equations, which is necessary to control the number of waves, indicating that a higher order approximation during the derivation is crucial. Upon ad hoc addition of the diffusion term, we found very good agreement between the age-structured model and the biology. In particular, we analyzed the influence of a refractory (insensitivity) period following a reversal of movement. Our analysis reveals that the refractory period is not necessary for wave formation, but essential to wave synchronization, indicating separate molecular mechanisms.

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8

Fitzgibbon, W. E., M. E. Parrott, and G. F. Webb. "A diffusive age-structured SEIRS epidemic model." Methods and Applications of Analysis 3, no. 3 (1996): 358–69. http://dx.doi.org/10.4310/maa.1996.v3.n3.a5.

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9

Andreasen, Viggo, and Thomas Frommelt. "A School-Oriented, Age-Structured Epidemic Model." SIAM Journal on Applied Mathematics 65, no. 6 (January 2005): 1870–87. http://dx.doi.org/10.1137/040610684.

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10

FENG, ZHILAN, LIBIN RONG, and ROBERT K. SWIHART. "DYNAMICS OF AN AGE-STRUCTURED METAPOPULATION MODEL." Natural Resource Modeling 18, no. 4 (June 28, 2008): 415–40. http://dx.doi.org/10.1111/j.1939-7445.2005.tb00166.x.

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11

GONZÁLEZ-PARRA, GILBERTO, LUCAS JÓDAR, FRANCISCO JOSÉ SANTONJA, and RAFAEL JACINTO VILLANUEVA. "An Age-Structured Model for Childhood Obesity." Mathematical Population Studies 17, no. 1 (January 29, 2010): 1–11. http://dx.doi.org/10.1080/07481180903467218.

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12

Tchuenche, J. M. "An age-structured model with delay mortality." Biosystems 81, no. 3 (September 2005): 255–60. http://dx.doi.org/10.1016/j.biosystems.2005.05.002.

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13

Krzyzanski, Wojciech, Pawel Wiczling, and Asfiha Gebre. "Age-structured population model of cell survival." Journal of Pharmacokinetics and Pharmacodynamics 44, no. 4 (March 29, 2017): 305–16. http://dx.doi.org/10.1007/s10928-017-9520-6.

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14

Castro, Manuela L. de, Jacques A. L. Silva, and Dagoberto A. R. Justo. "Stability in an age-structured metapopulation model." Journal of Mathematical Biology 52, no. 2 (September 29, 2005): 183–208. http://dx.doi.org/10.1007/s00285-005-0352-4.

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15

Gandolfi, Alberto, Mimmo Iannelli, and Gabriela Marinoschi. "An age-structured model of epidermis growth." Journal of Mathematical Biology 62, no. 1 (February 23, 2010): 111–41. http://dx.doi.org/10.1007/s00285-010-0330-3.

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16

Chu, Jixun, Zhihua Liu, Pierre Magal, and Shigui Ruan. "Normal Forms for an Age Structured Model." Journal of Dynamics and Differential Equations 28, no. 3-4 (October 7, 2015): 733–61. http://dx.doi.org/10.1007/s10884-015-9500-8.

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17

Henson, Shandelle M. "A continuous, age-structured insect population model." Journal of Mathematical Biology 39, no. 3 (September 16, 1999): 217–43. http://dx.doi.org/10.1007/s002850050169.

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18

Boulanouar, M. "Mathematical analysis of a model of age-cycle length structured cell population with quiescence." Issues of Analysis 28, no. 2 (June 2021): 27–43. http://dx.doi.org/10.15393/j3.art.2021.10030.

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19

Yang, Jianxin, Zhipeng Qiu, and Xue-Zhi Li. "Global stability of an age-structured cholera model." Mathematical Biosciences and Engineering 11, no. 3 (2014): 641–65. http://dx.doi.org/10.3934/mbe.2014.11.641.

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20

Al-arydah, Mo’tassem, and Robert Smith̏. "An age-structured model of human papillomavirus vaccination." Mathematics and Computers in Simulation 82, no. 4 (December 2011): 629–52. http://dx.doi.org/10.1016/j.matcom.2011.10.006.

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21

Griffiths, Jeff, Dawn Lowrie, and Janet Williams. "An age-structured model for the AIDS epidemic." European Journal of Operational Research 124, no. 1 (July 2000): 1–14. http://dx.doi.org/10.1016/s0377-2217(99)00288-x.

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22

Chowdhury, M., and E. J. Allen. "A stochastic continuous-time age-structured population model." Nonlinear Analysis: Theory, Methods & Applications 47, no. 3 (August 2001): 1477–88. http://dx.doi.org/10.1016/s0362-546x(01)00283-8.

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23

Yang, Kai, and Fabio Milner. "The logistic, two-sex, age-structured population model." Journal of Biological Dynamics 3, no. 2-3 (May 2009): 252–70. http://dx.doi.org/10.1080/17513750802283261.

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24

Guo, Zhong-Kai, Hai-Feng Huo, and Hong Xiang. "Bifurcation analysis of an age-structured alcoholism model." Journal of Biological Dynamics 12, no. 1 (January 1, 2018): 987–1011. http://dx.doi.org/10.1080/17513758.2018.1535668.

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25

Allen, Micheal S., and Leandro E. Miranda. "An age-structured model for erratic crappie fisheries." Ecological Modelling 107, no. 2-3 (April 1998): 289–303. http://dx.doi.org/10.1016/s0304-3800(98)00006-4.

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26

Busenberg, Stavros N., Mimmo Iannelli, and Horst R. Thieme. "Global Behavior of an Age-Structured Epidemic Model." SIAM Journal on Mathematical Analysis 22, no. 4 (July 1991): 1065–80. http://dx.doi.org/10.1137/0522069.

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27

Franco, Daniel, Hartmut Logemann, and Juan Perán. "Global stability of an age-structured population model." Systems & Control Letters 65 (March 2014): 30–36. http://dx.doi.org/10.1016/j.sysconle.2013.11.012.

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28

Maad Sasane, Sara. "An age structured cell cycle model with crowding." Journal of Mathematical Analysis and Applications 444, no. 1 (December 2016): 768–803. http://dx.doi.org/10.1016/j.jmaa.2016.06.065.

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29

Tarkhanov, N. "Lyapunov stability for an age-structured population model." Ecological Modelling 216, no. 2 (August 2008): 232–39. http://dx.doi.org/10.1016/j.ecolmodel.2008.03.017.

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30

Li, Xiao Rang, and Zhien Ma. "An improved model of age-structured population dynamics." Mathematical and Computer Modelling 20, no. 12 (December 1994): 143–50. http://dx.doi.org/10.1016/0895-7177(94)90130-9.

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31

Inaba, Hisashi. "Persistent age distributions for an age‐structured two‐sex population model*." Mathematical Population Studies 7, no. 4 (January 2000): 365–98. http://dx.doi.org/10.1080/08898480009525467.

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32

Calsina, A., and O. El idrissi. "Asymptotic Behavior of an Age-Structured Population Model and Optimal Maturation Age." Journal of Mathematical Analysis and Applications 233, no. 2 (May 1999): 808–26. http://dx.doi.org/10.1006/jmaa.1999.6350.

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33

Ludwig, Donald, and Carl J. Walters. "Are Age-Structured Models Appropriate for Catch-Effort Data?" Canadian Journal of Fisheries and Aquatic Sciences 42, no. 6 (June 1, 1985): 1066–72. http://dx.doi.org/10.1139/f85-132.

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Simulated data have been used to evaluate the performance of schemes for estimating optimum fishing effort using a simple stock-production model and R. B. Deriso's age-structured model Even when the data are generated using Deriso's model, the simpler production model generally gives as good or better estimates for the optimal effort. The only exception to this result is when data are provided with unrealistically large contrasts in effort and catch per unit effort over time. The implication of these findings is that simple production models should often be used in stock assessments based on catch/effort data, even when more realistic and structurally correct models are available to the analyst; the best choice depends on how much contrast has occurred in the historical effort and catch per unit effort data, rather than on prior knowledge about which model structure is biologically more realistic.

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34

Safi, Mohammad A., and Mahmoud H. DarAssi. "Mathematical analysis of an age-structured HSV-2 model." Journal of Computational Methods in Sciences and Engineering 19, no. 3 (July 17, 2019): 841–56. http://dx.doi.org/10.3233/jcm-181111.

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35

Okuwa, Kento, Hisashi Inaba, and Toshikazu Kuniya. "Mathematical analysis for an age-structured SIRS epidemic model." Mathematical Biosciences and Engineering 16, no. 5 (2019): 6071–102. http://dx.doi.org/10.3934/mbe.2019304.

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36

M. Colombo, Rinaldo, and Mauro Garavello. "Optimizing vaccination strategies in an age structured SIR model." Mathematical Biosciences and Engineering 17, no. 2 (2020): 1074–89. http://dx.doi.org/10.3934/mbe.2020057.

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37

Tian, Xiaohong, Rui Xu, Ning Bai, and Jiazhe Lin. "Bifurcation analysis of an age-structured SIRI epidemic model." Mathematical Biosciences and Engineering 17, no. 6 (2020): 7130–50. http://dx.doi.org/10.3934/mbe.2020366.

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38

Duarte, M. V. E., J. L. Medeiros, O. Q. F. Araújo, and M. A. Z. Coelho. "An age-structured population balance model for microbial dynamics." Brazilian Journal of Chemical Engineering 20, no. 1 (March 2003): 1–6. http://dx.doi.org/10.1590/s0104-66322003000100002.

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39

J. Browne, Cameron, and Sergei S. Pilyugin. "Global analysis of age-structured within-host virus model." Discrete & Continuous Dynamical Systems - B 18, no. 8 (2013): 1999–2017. http://dx.doi.org/10.3934/dcdsb.2013.18.1999.

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40

Rudnicki, Ryszard, and Radosław Wieczorek. "On a nonlinear age-structured model of semelparous species." Discrete & Continuous Dynamical Systems - B 19, no. 8 (2014): 2641–56. http://dx.doi.org/10.3934/dcdsb.2014.19.2641.

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41

Mohebbi, Hossein, Azim Aminataei, Cameron J. Browne, and Mohammad Reza Razvan. "Hopf bifurcation of an age-structured virus infection model." Discrete & Continuous Dynamical Systems - B 23, no. 2 (2018): 861–85. http://dx.doi.org/10.3934/dcdsb.2018046.

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42

Nor Frioui, Mohammed, Tarik Mohammed Touaoula, and Bedreddine Ainseba. "Global dynamics of an age-structured model with relapse." Discrete & Continuous Dynamical Systems - B 25, no. 6 (2020): 2245–70. http://dx.doi.org/10.3934/dcdsb.2019226.

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43

Cui, Minggen, and Zhong Chen. "The exact solution of nonlinear age-structured population model." Nonlinear Analysis: Real World Applications 8, no. 4 (September 2007): 1096–112. http://dx.doi.org/10.1016/j.nonrwa.2006.06.004.

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44

Guldbrandsen Frøysa, Kristin, Bjarte Bogstad, and Dankert W. Skagen. "Fleksibest—an age–length structured fish stock assessment model." Fisheries Research 55, no. 1-3 (March 2002): 87–101. http://dx.doi.org/10.1016/s0165-7836(01)00307-1.

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45

Alshorman, Areej, Chathuri Samarasinghe, Wenlian Lu, and Libin Rong. "An HIV model with age-structured latently infected cells." Journal of Biological Dynamics 11, sup1 (June 24, 2016): 192–215. http://dx.doi.org/10.1080/17513758.2016.1198835.

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46

Cannarsa, Piermarco, and Giuseppe Da Prato. "Positivity of solutions in a perturbed age-structured model." Mathematical Population Studies 23, no. 1 (January 2, 2016): 3–16. http://dx.doi.org/10.1080/08898480.2014.925340.

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47

Martcheva, Maia, and Fabio A. Milner. "A two‐sex age‐structured population model: Well posedness." Mathematical Population Studies 7, no. 2 (February 1999): 111–29. http://dx.doi.org/10.1080/08898489909525450.

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48

Richard, Quentin. "Global stability in a competitive infection-age structured model." Mathematical Modelling of Natural Phenomena 15 (2020): 54. http://dx.doi.org/10.1051/mmnp/2020007.

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We study a competitive infection-age structured SI model between two diseases. The well-posedness of the system is handled by using integrated semigroups theory, while the existence and the stability of disease-free or endemic equilibria are ensured, depending on the basic reproduction number R0x and R0y of each strain. We then exhibit Lyapunov functionals to analyse the global stability and we prove that the disease-free equilibrium is globally asymptotically stable whenever max{R0x, R0y} ≤ 1. With respect to explicit basin of attraction, the competitive exclusion principle occurs in the case where R0x ≠ R0y and max{R0x, R0y} > 1, meaning that the strain with the largest R0 persists and eliminates the other strain. In the limit case R0x = Ry0 > 1, an infinite number of endemic equilibria exists and constitute a globally attractive set.

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49

Cochran, John M., and Yongzhi Xu. "A temperature-dependent age-structured mosquito life-cycle model." Applicable Analysis 91, no. 2 (February 2012): 403–18. http://dx.doi.org/10.1080/00036811.2011.629609.

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50

Wang, Wendi, and Xiao-Qiang Zhao. "An Age-Structured Epidemic Model in a Patchy Environment." SIAM Journal on Applied Mathematics 65, no. 5 (January 2005): 1597–614. http://dx.doi.org/10.1137/s0036139903431245.

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