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Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes. [ABSTRACT FROM AUTHOR]

Rapid advances have recently been made at SRS Daresbury in optical-detection methods for both micro-imaging x-ray absorption spectroscopy (ODXAS, ODEXAFS), and in the development of volume-integrated synchrotron-laser pump-probe XAS methods (PP-ODXAS). The purpose of the present work is to describe the latest developments that enable these two methods to be coupled, providing micro-imaging of both the transient and CW synchrotron-laser interactions. Examples are provided whereby the distribution of both radiative and non-radiative centres in heterogeneous silicate samples can be mapped and directly correlated with the chemistry and structure of the host lattice. [ABSTRACT FROM AUTHOR]

Mathematical models of transmission have become invaluable management tools in planning for the control of emerging infectious diseases. A key variable in such models is the reproductive number R. For new emerging infectious diseases, the value of the reproductive number can only be inferred indirectly from the observed exponential epidemic growth rate r. Such inference is ambiguous as several different equations exist that relate the reproductive number to the growth rate, and it is unclear which of these equations might apply to a new infection. Here, we show that these different equations differ only with respect to their assumed shape of the generation interval distribution. Therefore, the shape of the generation interval distribution determines which equation is appropriate for inferring the reproductive number from the observed growth rate. We show that by assuming all generation intervals to be equal to the mean, we obtain an upper bound to the range of possible values that the reproductive number may attain for a given growth rate. Furthermore, we show that by taking the generation interval distribution equal to the observed distribution, it is possible to obtain an empirical estimate of the reproductive number. [ABSTRACT FROM AUTHOR]