A new Bayesian multiscale framework with cut inference jointly models heterogeneous viral load trajectories and household transmission, recovering parameters without bias on simulated data when viral sampling is frequent.
Hernandez-Ceron, Z
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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 inference for disease transmission models informed by viral dynamics
A new Bayesian multiscale framework with cut inference jointly models heterogeneous viral load trajectories and household transmission, recovering parameters without bias on simulated data when viral sampling is frequent.
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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.