example_ct_data

Example Ct value data 
example_gp_partab

Example Gaussian Process prior parameter table 
example_seir_incidence

True simulated SEIR incidence 
example_seir_partab

Example SEIR model parameter table 
likelihood_detectable()

Likelihood for using proportion detectable only 
p_a()

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

Plot distribution fits 
plot_posterior_density()

Plot posterior density 
plot_prob_infection()

Plot probability of infection 
predicted_distribution_fits()

Predicted distribution fits 
prop_detectable_single()

Probability of having a detectable Ct for a given time since infection 
reverse_gp_model()

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

Simulate full line list data 
simulate_reporting()

Subset line list data by testing strategy. Options:
Sample a random fraction of the population if the only argument is frac_report
Sample some random fraction of the population at a subset of time points, specified by timevarying_prob
Observe symptomatic individuals with some fixed probability, frac_report if symptomatic is TRUE
Observe symptomatic individuals with some timevarying probability, timevarying_prob, if symptomatic is TRUE
INPUTS:
individuals: the full line list from the simulation, returned by virosolver::simulate_observations_wrapper
solve_times: vector of times at which individuals can be reported
frac_report: the overall fraction/probability of individuals who are reported
timevarying_prob: a tibble with variables t and prob. This gives the probability of being reported on day t
symptomatic: if TRUE, then individuals are reported after developing symptoms. If FALSE, then we take a random crosssection
OUTPUTS:
A tibble with line list data for individuals who were observed
A plot of incidence for both observed individuals and the entire simulated population
Plot growth rate of cases/infections in the entire population and observed population

simulate_viral_loads_wrapper()

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 