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Found 3 papers, 3 papers with code
Unifying incidence and prevalence under a time-varying general branching process
We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling.
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).
