Structural Minimization of Risk on Estimation of Heterogeneity Distributions

Population heterogeneity dynamics is one of the research directions in IIASA's Population Program. One typical and practical problem related to hidden heterogeneity is the estimation of the heterogeneity distribution. This paper describes the approach to such an estimation which is based on the method of structural minimization of mean risk. It is shown how this method can be implemented to some real data. The main ideas of the method are also described

Modeling of immune life history and body growth: the role of antigen burden

In this paper, a recently developed mathematical model of age related changes in population of peripheral T cells (Romanyukha, Yashin, 2003) is used to describe ontogenetic changes of the immune system. The treatise is based on the assumption of linear dependence of antigen load from basal metabolic rate, which, in turn, depends on body mass following the allometric relationship – 3/4 power scaling law (Kleiber, 1932; West, Brown, 2005). Energy cost of antigen burden, i.e. the energy needed to produce and maintain immune cells plus the energy loss due to infectious diseases, is estimated and used as a measure of the immune system effectiveness. The dependence of optimal resource allocation from the parameters of antigen load is studied.

Modeling of Immunosenescence and Risk of Death from Respiratory Infections: Evaluation of the Role of Antigenic Load and Population Heterogeneity

It is well known that efficacy of immune functions declines with age. It results in an increase of severity and duration of respiratory infections and also in dramatic growth of risk of death due to these diseases after age 65. The goal of this work is to describe and investigate the mechanism underlying the age pattern of the mortality rate caused by infectious diseases and to determine the cause-specific hazard rate as a function of immune system characteristics. For these purposes we develop a three-compartment model explaining observed risk-of-death. The model incorporates up-to-date knowledge about cellular mechanisms of aging, disease dynamics, population heterogeneity in resistance to infections, and intrinsic aging rate. The results of modeling show that the age-trajectory of mortality caused by respiratory infections may be explained by the value of antigenic load, frequency of infections and the rate of aging of the stem cell population (i.e. the population of T-lymphocyte progenitor cells). The deceleration of infection-induced mortality at advanced age can be explained by selection of individuals with a slower rate of stem cell aging. Parameter estimates derived from fitting mortality data indicate that infection burden was monotonically decreasing during the twentieth century, and changes in total antigenic load were gender-specific: it experienced periodic fluctuations in males and increased approximately two-fold in females

Individual fecundity dynamically predicts remaining life expectancy in medflies

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Study of the bivariate survival data using frailty models based on Lévy processes

Frailty models allow us to take into account the non-observable inhomogeneity of individual hazard functions. Although models with time-independent frailty have been intensively studied over the last decades and a wide range of applications in survival analysis have been found, the studies based on the models with time-dependent frailty are relatively rare. In this paper, we formulate and prove two propositions related to the identifiability of the bivariate survival models with frailty given by a nonnegative bivariate Lévy process. We discuss parametric and semiparametric procedures for estimating unknown parameters and baseline hazard functions. Numerical experiments with simulated and real data illustrate these procedures. The statements of the propositions can be easily extended to the multivariate case