ECOL 8910: Perspectives in Computational Ecology

Spring 2017: Multi-scale Modeling

ECOL 8910: Perspectives in Computational Ecology
Student summary 

Unifying the epidemiological and evolutionary dynamics of pathogens

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Grenfell, B.T., Pybus, O.G., Gog, J.R., Wood, J.L., Daly, J.M., Mumford, J.A. and Holmes, E.C., 2004. Unifying the epidemiological and evolutionary dynamics of pathogens. science, 303(5656), pp.327-332.

This paper unified ideas from epidemiology and evolutionary biology and defined phylodynamics as the melding of immunodynamics, epidemiology, and evolutionary biology. Their goal was to outline a framework for unraveling the processes that cause epidemiological patterns observed in RNA viruses of vertebrates. They studied RNA viruses because of their high mutation rates, large population sizes, and short generation times. A major determinant of epidemic behavior is the varying timescales at which susceptible individuals are replenished. In constrast, the main determinant of evolutionary behavior is natural selection arising from host immunity. Thus, the authors categorized infections according to length of infection and intensity of cross-immunity. For example, measles produces short infections with strong cross immunity while influenza produces short infections with partial cross immunity.

Idealized genetic tree shapes. The main division is between those viruses subject to continual immune-driven selection (such as human influenza A virus and intra-host HIV), in which trees have a strong temporal structure, and viruses where immune selection is absent or weak (such as many RNA viruses), in which the trees depict population size and spatial dynamics. The types of evolutionary inference that can be made from the various phylogenies are also indicated. (A, B, and C represent three subpopulations from which viruses have been sampled.)

The amount of variation in viral genetics is modulated by the host immune response and bottleneck at transmission, which shapes the resultant viral diversity transmitted to other hosts. If the bottle neck is non-selective, genetic drift can occur. Static patterns arise when there is a single optimum level of pathogen adaptation to host immunity. The rate to fixation will depend on the amount of selection. The highest rate of pathogen adaptation occurs when there is intermediate levels of immunity and viral load. In contrast to static patterns among all hosts, dynamic patterns can result within hosts depending on immune history and viral adaptation, which they define as the Evolutionary Infectivity Profile (EIP). The authors advocate the phylodynamic approach because the interactions between processes can potentially influence each other, especially for RNA viruses which have overlapping transmission and evolutionary timescales.