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Inference using mathematical models of infectious disease dynamics is a powerful tool to analyse epidemiological data and elucidate pathogen life cycles. Key epidemiological parameters can be estimated from demographic time series by computing the likelihood of alternative models of pathogen transmission. Here we use this inference approach to analyze data from an evolution experiment in which we monitored both the epidemiology and the evolution of the temperate bacteriophage λ during an epidemic. We estimate parameter values for all the life-history traits of two distinct strains of the virus. In particular, we estimate the ability of the two virus strains to modulate plastically the rate of lysogenization with the multiplicity of infection. Our work illustrates how inference from experimental evolution data can feedback on the development of models aiming to predict the epidemiology and evolution of infectious diseases.

Original publication

DOI

10.1101/2020.08.19.256917

Type

Journal article

Publication Date

19/08/2020