Abstract
Immuno-epidemiological modeling of infectious diseases is a technique that links a within-host mathematical model with a between-host mathematical model. In the simplest nested models the within-host model is an ODE structured by the time since infection. The between-host model is a time-since-infection and chronological time structured PDE. The nested immuno-epidemiological are related to age-since-infection models that we consider in Chap. 8. The linking between the two models consists in two parts: linking through the time-since-infection and linking through the parameters of the epidemiological model which depend on the within-host variables. It is very important that the within-host model and the between-host model are consistent. For instance, if the disease is chronic, the within-host model should lead to chronic infection, that is have a stable infected equilibrium, and the between-host model should contain no recovered class. Such is the case with HIV. On the other hand, if the disease always leads to recovery, the within-host model should be an outbreak model, while the between-host model should have a recovered class. This scenario is appropriate for diseases such as influenza.
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References
R. Ben-Shachar, K. Koelle, Minimal within-host dengue models highlight the specific roles of the immune response in primary and secondary dengue infections. J. R. Soc. Interface 12, 20140886 (2015)
S. Bhattacharya, M. Martcheva, An immuno-eco-epidemiological model of competition. J. Biol. Dyn. 10, 314–341 (2016)
D. Coombs, M.A. Gilchrist, C. Ball, Evaluating the importance of within- and between-host selection pressures on the evolution of chronic pathogens. Theor. Popul. Biol. 72, 576–591 (2007)
Y.-X. Dang, X.-Z. Li, M. Martcheva, Competitive exclusion in a multi-strain immuno-epidemiological influenza model with environmental transmission. J. Biol. Dyn. 10, 416–456 (2016)
Z. Feng, H.R. Thieme, Recurrent outbreaks of childhood diseases revisited: the impact of isolation. Math. Biosci. 128, 93–130 (1995)
R.S. Fritz, F.G. Hayden, D.P. Calfee, L.M.R. Cass, A.W. Peng, W.G. Alvord, W. Strober, S.E. Straus, Nasal cytokine and chemokine responses in experimental influenza a virus infection: Results of a placebo-controlled trial of intravenous zanamivir treatment. J. Infect. Dis. 180, 586–593 (1999)
M.A. Gilchrist, D. Coombs, Evolution of virulence: interdependence, constraints, and selection using nested models. Theor. Popul. Biol. 69, 145–153 (2006)
M.A. Gilchrist, A. Sasaki, Modeling host-parasite coevolution: a nested approach based on mechanistic models. J. Theor. Biol. 218, 289–308 (2002)
M.G. Guzman et al., Dengue: a continuing global threat. Nat. Rev. Microbiol. 8, S7–S16 (2010)
M. Iannelli, Mathematical Theory of Age-Structured Population Dynamics (Giardini, Pisa, 1995)
M. Iannelli, F. Milner, The Basic Approach to Age-Structured Population Dynamics (Springer, New York, 2017)
I. Kawaguchi, A. Sasaki, M. . Boots, Why are dengue virus serotypes so distantly related? Enhancement and limiting serotype similarity between dengue virus strains. Proc. Biol. Sci. 270, 2241–2247 (2003)
M. Martcheva, An Introduction to Mathematical Epidemiology. Texts in Applied Mathematics, vol. 61 (Springer, New York, 2015)
M. Martcheva, X.-Z. Li, Linking immunological and epidemiological dynamics of HIV: the case of super-infection. J. Biol. Dyn. 7, 161–182 (2013)
F.A. Milner, A. Pugliese, Periodic solutions: a robust numerical method for an S-I-R model of epidemics. J. Math. Biol. 39, 471–492 (1999)
N.M. Nguyen, et al., Host and viral features of human dengue cases shape the population of infected and infectious aedes aegypti mosquitoes. Proc. Natl Acad. Sci. USA 110, 9072–9077 (2013)
E. Numfor, S. Bhattacharya, S. Lenhart, M. Martcheva, Optimal control in coupled within-host and between-host models. Math. Model. Nat. Phenom. 9, 171–203 (2014)
E. Numfor, S. Bhattarachya, S. Lenhart, M. Martcheva, Optimal control in multi-group coupled within-host and between-host models. Electron. J. Differ. Equ. 23, 87–117 (2016)
K.A. Pawelek, G.T. Huynh, M. Quinlivan, A. Cullinane, L. Rong, A.S. Perelson, Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comp. Biol. 8, e1002588 (2012)
H.R. Thieme, C. Castillo-Chavez, How may infection-age-dependent infectivity affect the dynamics of HIV/AIDS? SIAM J. Appl. Math. 53, 1447–1479 (1993)
N. Tuncer, M. Martcheva, Analytical and numerical approaches to coexistence of strains in a two-strain SIS model with diffusion. J. Biol. Dyn. 6, 406–439 (2012)
W. Wang, Y. Cai, M. Wu, K. Wang, Z. Li, Complex dynamics of a reaction-diffusion epidemic model. Nonlinear Anal. Real World Appl. 13, 2240–2258 (2012)
P.S. Wikramaratna, A. Kucharski, S. Gupta, V. Andreasen, A.R. McLean, J.R. Gog, Five challenges in modelling interacting strain dynamics. Epidemics 10, 31-4 (2014)
D. Wodarz, Killer Cell Dynamics. Interdisciplinary Applied Mathematics, vol. 32 (Springer, New York, 2007). Mathematical and Computational Approaches to Immunology
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Li, XZ., Yang, J., Martcheva, M. (2020). Nested Immuno-Epidemiological Models. In: Age Structured Epidemic Modeling. Interdisciplinary Applied Mathematics, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-42496-1_3
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