Modeling Adaptive Behavior in Influenza Transmission
Mathematical Modelling of Natural Phenomena (2012)
- Volume: 7, Issue: 3, page 253-262
- ISSN: 0973-5348
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topWang, W.. "Modeling Adaptive Behavior in Influenza Transmission." Mathematical Modelling of Natural Phenomena 7.3 (2012): 253-262. <http://eudml.org/doc/222262>.
@article{Wang2012,
abstract = {Contact behavior plays an important role in influenza transmission. In the progression of
influenza spread, human population reduces mobility to decrease infection risks. In this
paper, a mathematical model is proposed to include adaptive mobility. It is shown that the
mobility response does not affect the basic reproduction number that characterizes the
invasion threshold, but reduces dramatically infection peaks, or removes the peaks.
Numerical calculations indicate that the mobility response can provide a very good
protection to susceptible individuals, and a combination of mobility response and
treatment is an effective way to control influenza outbreak.},
author = {Wang, W.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {mobility; response; pattern; stability},
language = {eng},
month = {6},
number = {3},
pages = {253-262},
publisher = {EDP Sciences},
title = {Modeling Adaptive Behavior in Influenza Transmission},
url = {http://eudml.org/doc/222262},
volume = {7},
year = {2012},
}
TY - JOUR
AU - Wang, W.
TI - Modeling Adaptive Behavior in Influenza Transmission
JO - Mathematical Modelling of Natural Phenomena
DA - 2012/6//
PB - EDP Sciences
VL - 7
IS - 3
SP - 253
EP - 262
AB - Contact behavior plays an important role in influenza transmission. In the progression of
influenza spread, human population reduces mobility to decrease infection risks. In this
paper, a mathematical model is proposed to include adaptive mobility. It is shown that the
mobility response does not affect the basic reproduction number that characterizes the
invasion threshold, but reduces dramatically infection peaks, or removes the peaks.
Numerical calculations indicate that the mobility response can provide a very good
protection to susceptible individuals, and a combination of mobility response and
treatment is an effective way to control influenza outbreak.
LA - eng
KW - mobility; response; pattern; stability
UR - http://eudml.org/doc/222262
ER -
References
top- M. Balinska, C. Rizzo. Behavioural responses to influenza pandemics : what do we know ? PLoS. Curr., (2009), p. RRN1037.
- V. Capasso, G. Serio. Ageneralization of the Kermack-McKendrick deterministic epidemic model. Math. Biosci., 42 (1978), 43-61.
- J. Cui, Y. Sun, H. Zhu. The impact of media on the control of infectious diseases. J. Dynam. Differential Equations, 20 (2008), 31-53.
- W. R. Derrick, P. van den Driessche. A disease transmision model in a nonconstant population. J. Math. Biol., 31 (1993), 495-512.
- 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.
- 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.
- S. Funk, E. Gilad, V. A. A. Jansen. Endemic disease, awareness, and local behavioural response. J. Theor. Biol., 264 (2010), 501-509.
- D. Gao, S. Ruan. An SIS patch model with variable transmission coefficients. Mathematical Biosciences, 232 (2011), 110-115.
- I. Z. Kiss, J. Cassell, M. Recker, P. L. Simon. The impact of information transmission on epidemic outbreaks. Math. Biosci., 225 (2010), 1-10.
- J.P. LaSalle, S. Lefschetz. Stability by Lyapunov’s Direct Method. Academic Press, New York, 1961.
- 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.
- 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.
- P. Poletti, B. Caprile, M. Ajelli A. Pugliese, S. Merler. Spontaneous behavioural changes in response to epidemics. J. Theor. Biol., 260 (2009), 31-40.
- Z. Qiu, Z. Feng. Transmission dynamics of an influenza model with vaccination and antiviral treatment. Bull. Math. Biol., 72 (2010), 1-33.
- S. Ruan, W. Wang. Dynamical behavior of an epidemic model with a nonlinear incidence rate. J. Differential Equations, 188 (2003), 135-163.
- S. Ruan, W. Wang, S. Levin. The effect of global travel on the spread of SARS. Mathematical Biosciences and Engineering, 3 (2006), 205-218.
- 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.
- 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.
- 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.
- 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.
- 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.
- W. Wang. Epidemic models with nonlinear infection forces. Mathematical Biosciences and Engineering, 3 (2006), 267-279.
- W. Wang, S. Ruan. Simulating the SARS outbreak in Beijing with limited data. J. Theor. Biol., 227 (2004), 369-379.
- D. Xiao, S. Ruan. Global analysis of an epidemic model with nonmonotone incidence rate. Math. Biosci., 208 (2007), 419-429.
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