All functions


Example Ct value data


Example Gaussian Process prior parameter table


True simulated SEIR incidence


Example SEIR model parameter table


Likelihood for using proportion detectable only


Function to give probability of observing x given age a and the viral kinetics curve


Plot distribution fits


Plot posterior density


Plot probability of infection


Predicted distribution fits


Probability of having a detectable Ct for a given time since infection


Given a vector of daily infection probabilities, converts this to the input expected by the Gaussian process model


Simulate full line list data


Subset line list data by testing strategy. Options:

  1. Sample a random fraction of the population if the only argument is frac_report

  2. Sample some random fraction of the population at a subset of time points, specified by timevarying_prob

  3. Observe symptomatic individuals with some fixed probability, frac_report if symptomatic is TRUE

  4. Observe symptomatic individuals with some time-varying probability, timevarying_prob, if symptomatic is TRUE INPUTS:

    1. individuals: the full line list from the simulation, returned by virosolver::simulate_observations_wrapper

    2. solve_times: vector of times at which individuals can be reported

    3. frac_report: the overall fraction/probability of individuals who are reported

    4. timevarying_prob: a tibble with variables t and prob. This gives the probability of being reported on day t

    5. symptomatic: if TRUE, then individuals are reported after developing symptoms. If FALSE, then we take a random cross-section OUTPUTS:

    6. A tibble with line list data for individuals who were observed

    7. A plot of incidence for both observed individuals and the entire simulated population

    8. Plot growth rate of cases/infections in the entire population and observed population


Simulate observed Ct values for the line list dataset. NOTE this differs to virosolver::simulate_viral_loads, as this function only solves the viral kinetics model for the observation time INPUTS: 1. linelist: the line list for observed individuals 2. kinetics_pars: vector of named parameters for the viral kinetics model OUTPUTS: 1. A tibble with the line list data and the viral load/ct/observed ct at the time of sampled