Free Access
Issue
Math. Model. Nat. Phenom.
Volume 7, Number 3, 2012
Epidemiology
Page(s) 253 - 262
DOI https://doi.org/10.1051/mmnp/20127315
Published online 06 June 2012
  1. M. Balinska, C. Rizzo. Behavioural responses to influenza pandemics : what do we know ? PLoS. Curr., (2009), p. RRN1037. [Google Scholar]
  2. V. Capasso, G. Serio. Ageneralization of the Kermack-McKendrick deterministic epidemic model. Math. Biosci., 42 (1978), 43-61. [CrossRef] [Google Scholar]
  3. J. Cui, Y. Sun, H. Zhu. The impact of media on the control of infectious diseases. J. Dynam. Differential Equations, 20 (2008), 31-53. [CrossRef] [MathSciNet] [Google Scholar]
  4. W. R. Derrick, P. van den Driessche. A disease transmision model in a nonconstant population. J. Math. Biol., 31 (1993), 495-512. [PubMed] [Google Scholar]
  5. A. d’Onofrio, P. Manfredi. Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases. J. Theor. Biol., 256 (2009), 473-478. [CrossRef] [PubMed] [Google Scholar]
  6. J. M. Epstein, J. Parker, D. Cummings, R. A. Hammond. Coupled contagion dynamics of fear and disease : mathematical and computational explorations. PLoS One, 3 (2008), e3955. [CrossRef] [PubMed] [Google Scholar]
  7. S. Funk, E. Gilad, V. A. A. Jansen. Endemic disease, awareness, and local behavioural response. J. Theor. Biol., 264 (2010), 501-509. [CrossRef] [PubMed] [Google Scholar]
  8. D. Gao, S. Ruan. An SIS patch model with variable transmission coefficients. Mathematical Biosciences, 232 (2011), 110-115. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  9. I. Z. Kiss, J. Cassell, M. Recker, P. L. Simon. The impact of information transmission on epidemic outbreaks. Math. Biosci., 225 (2010), 1-10. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  10. J.P. LaSalle, S. Lefschetz. Stability by Lyapunov’s Direct Method. Academic Press, New York, 1961. [Google Scholar]
  11. W. M. Liu, H. W. Hethcote, S. A. Levin. Dynamical behavior of epidemiological models with nonlinear incidence rates. J. Math. Biol., 25 (1987), 359-380. [Google Scholar]
  12. W. M. Liu, S. A. Levin, Y. Iwasa. Influence of nonlinear incidence rates upon the behavior of SIRS epidemiological models. J. Math. Biol., 23 (1986), 187-204. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  13. P. Poletti, B. Caprile, M. Ajelli A. Pugliese, S. Merler. Spontaneous behavioural changes in response to epidemics. J. Theor. Biol., 260 (2009), 31-40. [CrossRef] [PubMed] [Google Scholar]
  14. Z. Qiu, Z. Feng. Transmission dynamics of an influenza model with vaccination and antiviral treatment. Bull. Math. Biol., 72 (2010), 1-33. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  15. S. Ruan, W. Wang. Dynamical behavior of an epidemic model with a nonlinear incidence rate. J. Differential Equations, 188 (2003), 135-163. [CrossRef] [MathSciNet] [Google Scholar]
  16. S. Ruan, W. Wang, S. Levin. The effect of global travel on the spread of SARS. Mathematical Biosciences and Engineering, 3 (2006), 205-218. [CrossRef] [MathSciNet] [Google Scholar]
  17. L. Sattenspiel, D. A. Herring. Simulating the effect of quarantine on spread of the 1918-19 flue in central Canada. Bull. Math. Biol., 65 (2003), 1-26. [CrossRef] [PubMed] [Google Scholar]
  18. C. Sun, W. Yang, J. Arino, K. Khan. Effect of media-induced social distancing on disease transmission in a two patch setting. Math. Biosci., 230 (2011), 87-95. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  19. M. M. Tanaka, J. Kumm, M. W. Feldman. Coevolution of pathogens and cultural practices : a new look at behavioral heterogeneity in epidemics. Theor. Popul. Biol., 62 (2002), 111-119. [CrossRef] [PubMed] [Google Scholar]
  20. S. Tang, Y. Xiao, Y. Yang, Y. Zhou, J. Wu, Z. Ma. Community-based measures for mitigating the 2009 H1N1 pandemic in China. PLoS One. 5 (2010), e10911. [CrossRef] [PubMed] [Google Scholar]
  21. P. van den Driessche, J. Watmough. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci., 180 (2002), 29-48. [CrossRef] [MathSciNet] [Google Scholar]
  22. W. Wang. Epidemic models with nonlinear infection forces. Mathematical Biosciences and Engineering, 3 (2006), 267-279. [CrossRef] [MathSciNet] [Google Scholar]
  23. W. Wang, S. Ruan. Simulating the SARS outbreak in Beijing with limited data. J. Theor. Biol., 227 (2004), 369-379. [CrossRef] [PubMed] [Google Scholar]
  24. D. Xiao, S. Ruan. Global analysis of an epidemic model with nonmonotone incidence rate. Math. Biosci., 208 (2007), 419-429. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.