A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
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In a non-Markovian discrete epidemic model with asymptomatic carriers, generation-time probabilities and moments are obtained by rearranging the basic reproduction number, yielding an expected generation time that is a convex combination of pre- and post-symptomatic components.
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Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
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Generation time in a discrete epidemic model with asymptomatic carriers: beyond geometric waiting times
In a non-Markovian discrete epidemic model with asymptomatic carriers, generation-time probabilities and moments are obtained by rearranging the basic reproduction number, yielding an expected generation time that is a convex combination of pre- and post-symptomatic components.