Supporting information Text S1 for Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2 Contents
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1 Materials and Methods 1 1.1 Sequence and location data . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Incorporating uncertainty in air community assignment . . . . . . . . . . . . 3 1.3 Bayesian statistical analysis of sequence and trait evolution . . . . . . . . . . 4 1.3.1 GLM diffusion implementation and predictor support . . . . . . . . . 5 1.3.2 GLM diffusion predictors . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.3 Fitting diffusion models to empirical tree distributions . . . . . . . . 10 1.4 Comparing simulated spatial expansion to recorded H1N1 pandemic data . . 11
منابع مشابه
Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2
Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights ...
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