The equilibria that allow bacterial persistence in human hosts

We propose that microbes that have developed persistent relationships with human hosts have evolved cross-signalling mechanisms that permit homeostasis that conforms to Nash equilibria and, more specifically, to evolutionarily stable strategies. This implies that a group of highly diverse organisms has evolved within the changing contexts of variation in effective human population size and lifespan, shaping the equilibria achieved, and creating relationships resembling climax communities. We propose that such ecosystems contain nested communities in which equilibrium at one level contributes to homeostasis at another. The model can aid prediction of equilibrium states in the context of further change: widespread immunodeficiency, changing population densities, or extinctions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62883/1/nature06198.pd

Mathematical models for the epidemiology and evolution of mycobacterium tuberculosis

© Springer International Publishing AG 2017. This chapter reviews the use of mathematical and computational models to facilitate understanding of the epidemiology and evolution of Mycobacterium tuberculosis. First, we introduce general epidemiological models, and describe their use with respect to epidemiological dynamics of a single strain and of multiple strains of M. tuberculosis. In particular, we discuss multi-strain models that include drug sensitivity and drug resistance. Second, we describe models for the evolution of M. tuberculosis within and between hosts, and how the resulting diversity of strains can be assessed by considering the evolutionary relationships among different strains. Third, we discuss developments in integrating evolutionary and epidemiological models to analyse M. tuberculosis genetic sequencing data. We conclude the chapter with a discussion of the practical implications of modelling – particularly modelling strain diversity – for controlling the spread of tuberculosis, and future directions for research in this area

Comorbid Psychiatric Illnesses

Comorbidity among psychiatric illnesses is common, as is comorbidity between psychiatric and physical illnesses. Current knowledge of psychiatric comorbidity points to several possible underlying factors, notably an overlap in their definitions and symptoms; unidirectional and bidirectional causation; disordered sleep; and a range of shared risk factors. Psychiatric illness may lead to poorer self-care and sleep problems, whereas being physically ill may impact upon an individual’s psychological wellbeing. An integration of the various causal models that have been proposed to explain the comorbidities is discussed, incorporating different socio-psychological and biological factors to explain the development of depression and anxiety. These issues are detailed in the following chapter with a focus on depression. Implications for treatment are also discussed