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Analysis of Infectious Mortality by Means of the Individualized Risk Model

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Mathematical Modeling of Biological Systems, Volume II

Summary

The goal of this chapter is to describe the mechanism underlying the age-specific increase in death risk related to immunosenescence, to determine the cause-specific hazard rate as a function of immune system characteristics. A mathematical model that allows for the estimation of the age-specific risk of death caused by infectious diseases has been developed. The model consists of three compartments: (1) a model of immunosenescence, (2) a model of infectious disease, and (3) a model giving the relationship between disease severity and the risk of death. The proposed model makes it possible to analyze age-specific mortality from infectious diseases and to predict future changes in mortality due to public health activity. At the same time it can be used for individualized risk assessment.

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References

  1. Aspinall, R.: Longevity and the immune response. Biogerontology, 1, 273–278 (2000).

    Article  Google Scholar 

  2. Aviv, A., Levy, D., Mangel, M.: Growth, telomere dynamics and successful and unsuccessful human aging. Mech. Ageing Dev., 124, 829–837 (2003).

    Article  Google Scholar 

  3. Bodnar, A. G., Ouellette, M., Frolkis, M., Holt, S. E., Chiu, C.-P., Morin, G. B., Harley, C. B., Shay, J. W., Lichtsteiner, S., Wright, W. E.: Extension of life-span by introduction of telomerase into normal human cells. Science, 279, 349–352 (1998).

    Article  Google Scholar 

  4. Caruso, C., Lio, D., Cavallone, L., Franceschi, C.: Aging, longevity, inflammation, and cancer. Ann. NY. Acad. Sci., 1028, 1–13 (2004).

    Article  Google Scholar 

  5. Cawthon, R. M., Smith, K. R., O‘Brien, E., Sivatchenko, A., Kerber, R. A.: Association between telomere length in blood and mortality in people aged 60 years or older. Lancet, 361, 393–395 (2003).

    Article  Google Scholar 

  6. De Boer, R. J.: Mathematical model of human CD4+ T-cell population kinetics. The Netherlands Journal of Medicine, 60, 17–26 (2002).

    Google Scholar 

  7. Effros, R. B.: Costimulatory mechanisms in the elderly. Vaccine, 18, 1661–1665 (2000).

    Article  Google Scholar 

  8. Effros, R. B.: T cell replicative senescence pleiotropic effects on human aging. Ann NY Acad Sci, 1019, 123–126 (2004).

    Article  Google Scholar 

  9. Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., Cawthon, R. M.: Accelerated telomere shortening in response to life stress. PNAS, 101, 17312–17315 (2004).

    Article  Google Scholar 

  10. Fagnoni, F. F., Vescovini, R., Passeri, G., Bologna, G., Pedrazzoni,M., Lavagetto, G., Casti, A., Franceschi, C., Passeri, M., Sansoni, P.: Shortage of circulating naive CD8+ T cells provides new insights on immunodeficiency in aging. Blood, 95, 2860–2868 (2000).

    Google Scholar 

  11. Horiuchi, S., Finch, C. E., Mesle, F., Vallin, J.: Differential patterns of age-related mortality increase in middle age and old age. J. Gerontol. A. Biol. Sci. Med. Sci., 58, B495–507 (2003).

    Google Scholar 

  12. Horiuchi, S., Wilmoth, J.: Age patterns of the life table aging rate for major causes of death in Japan, 1951–1990. J Gerontol A Biol Sci Med Sci, 52, B67–77 (1997).

    Google Scholar 

  13. Marchuk, G.: Mathematical modelling of immune response in infectious diseases. Kluwer Academic Publishers, Dordrecht (1997).

    Google Scholar 

  14. Marchuk, G., Romanyukha, A. A., Bocharov, G.: Mathematical model of antiviral immune response. II. Parameters identification of acute course of viral hepatitis B data. J. Theor. Biol., 151, 41–70 (1991).

    Article  Google Scholar 

  15. Marchuk, G. I., Berbentzova, E. P.: Acute pneumonia: immunology, severity assessment, clinic, treatment. Nauka, Moscow (1989) (in Russian).

    Google Scholar 

  16. Mariani, L., Turchetti, G., Franceschi, C.: Chronic antigenic stress, immunosenescence and human survivorship over the 3 last centuries: heuristic value of a mathematical model. Mech. Ageing Dev., 124, 453–8 (2003).

    Article  Google Scholar 

  17. Nikolich-Zugich, J.: T cell aging: naive but not young. J. Exp. Med., 201, 837–840 (2005).

    Article  Google Scholar 

  18. Nowak, M. A., May, R. M.: Virus Dynamics: Mathematical Principles of Immunology and Virology. Oxford University Press, Oxford (2000).

    MATH  Google Scholar 

  19. Romanyukha, A. A., Rudnev, S. G., Sidorov, I. A.: Energy cost of infection burden: An approach to understanding the dynamics of host–pathogen interactions. J. Theor. Biol., 241, 1–13 (2006).

    Article  MathSciNet  Google Scholar 

  20. Romanyukha, A. A., Yashin, A.: Age related changes in population of peripheral T cells: towards a model of immunosenescence. Mech. Ageing Dev., 124, 433–443 (2003).

    Article  Google Scholar 

  21. Rufer, N., Brummendorf, T. H., Kolvraa, S., Bischoff, C., Christensen, K., Wadsworth, L., Schulzer, M., Lansdorp, P. M.: Telomere fluorescence measurements in granulocytes and T lymphocyte subsets point to a high turnover of hematopoietic stem cells and memory T cells in early childhood. J. Exp. Med., 190, 157–168 (1999).

    Article  Google Scholar 

  22. Sannikova, T. E., Rudnev, S. G., Romanyukha, A. A., Yashin, A. I.: Immune system aging may be affected by HIV infection: mathematical model of immunosenescence. Russian Journal of Numerical Analysis and Mathematical Modeling, 19, 315–329 (2004).

    Article  MATH  MathSciNet  Google Scholar 

  23. Segerstrom, S. C., Miller, G. E.: Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry. Psychol. Bull., 130, 601–630 (2004).

    Article  Google Scholar 

  24. Ukraintseva, S. V., Yashin, A. I.: How individual age-associated changes may influence human morbidity and mortality patterns. Mech. Ageing Dev., 122, 1447–1460 (2001).

    Article  Google Scholar 

  25. Vaupel, J. W., Carey, J. R., Christensen, K., Johnson, T. E., Yashin, A. I., Holm, N. V., Iachine, I. A., Kannisto, V., Khazaeli, A. A., Liedo, P., Longo, V. D., Zeng, Y., Manton, K. G., Curtsinger, J. W.: Biodemographic trajectories of longevity. Science, 280, 855–860 (1998).

    Article  Google Scholar 

  26. Wigginton, J. E., Kirschner, D.: A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. J. Immunol., 166, 1951– 1967 (2001).

    Google Scholar 

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Sannikova, T.E. (2008). Analysis of Infectious Mortality by Means of the Individualized Risk Model. In: Deutsch, A., et al. Mathematical Modeling of Biological Systems, Volume II. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4556-4_15

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