Run the vaccine model

run(
country = NULL,
population = NULL,
tt_contact_matrix = 0,
contact_matrix_set = NULL,
R0 = 3,
tt_R0 = 0,
beta_set = NULL,
time_period = 365,
replicates = 10,
seed = stats::runif(1, 0, 1e+08),
prob_hosp = probs$prob_hosp, prob_hosp_multiplier = probs$prob_hosp_multiplier,
tt_prob_hosp_multiplier = probs$tt_prob_hosp_multiplier, prob_severe = probs$prob_severe,
prob_non_severe_death_treatment = probs$prob_non_severe_death_treatment, prob_non_severe_death_no_treatment = probs$prob_non_severe_death_no_treatment,
prob_severe_death_treatment = probs$prob_severe_death_treatment, prob_severe_death_no_treatment = probs$prob_severe_death_no_treatment,
p_dist = probs$p_dist, rel_infectiousness = probs$rel_infectiousness,
rel_infectiousness_vaccinated = probs$rel_infectiousness_vaccinated, dur_E = durs$dur_E,
dur_IMild = durs$dur_IMild, dur_ICase = durs$dur_ICase,
dur_get_ox_survive = durs$dur_get_ox_survive, dur_get_ox_die = durs$dur_get_ox_die,
dur_not_get_ox_survive = durs$dur_not_get_ox_survive, dur_not_get_ox_die = durs$dur_not_get_ox_die,
dur_get_mv_survive = durs$dur_get_mv_survive, dur_get_mv_die = durs$dur_get_mv_die,
dur_not_get_mv_survive = durs$dur_not_get_mv_survive, dur_not_get_mv_die = durs$dur_not_get_mv_die,
dur_rec = durs$dur_rec, dur_R = vaccine_pars$dur_R,
tt_dur_R = vaccine_pars$tt_dur_R, dur_V = vaccine_pars$dur_V,
vaccine_efficacy_infection = vaccine_pars$vaccine_efficacy_infection, tt_vaccine_efficacy_infection = vaccine_pars$tt_vaccine_efficacy_infection,
vaccine_efficacy_disease = vaccine_pars$vaccine_efficacy_disease, tt_vaccine_efficacy_disease = vaccine_pars$tt_vaccine_efficacy_disease,
max_vaccine = vaccine_pars$max_vaccine, tt_vaccine = vaccine_pars$tt_vaccine,
dur_vaccine_delay = vaccine_pars$dur_vaccine_delay, vaccine_coverage_mat = vaccine_pars$vaccine_coverage_mat,
hosp_bed_capacity = NULL,
ICU_bed_capacity = NULL,
tt_hosp_beds = 0,
tt_ICU_beds = 0,
seeding_cases = 20,
seeding_age_order = NULL,
init = NULL,
use_dde = TRUE,
...
)

## Arguments

country Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided. Population vector (for each age group). Default = NULL, which will cause population to be sourced from country Time change points for matrix change. Default = 0 Contact matrices used in simulation. Default = NULL, which will generate this based on the country. Basic Reproduction Number. Default = 3 Change time points for R0. Default = 0 Alternative parameterisation via beta rather than R0. Default = NULL, which causes beta to be estimated from R0 Length of simulation. Default = 365 Number of replicates. Default = 10 Random seed used for simulations. Deafult = runif(1, 0, 10000) probability of hospitalisation by age. Default = c(0.000744192, 0.000634166,0.001171109, 0.002394593, 0.005346437, 0.010289885, 0.016234604, 0.023349169, 0.028944623, 0.038607042, 0.057734879, 0.072422135, 0.101602458, 0.116979814, 0.146099064, 0.176634654 ,0.180000000) Time varying multiplier to probability of hospitalisation. Default = 1, which is no change to provided prob_hosp. Timing of changes to multiplier of probability of hospitalisation. Default = 0 Probability of developing severe symptoms by age. Default = c(0.05022296, 0.05022296, 0.05022296, 0.05022296, 0.05022296, 0.05022296, 0.05022296, 0.053214942, 0.05974426, 0.074602879, 0.103612417, 0.149427991, 0.223777304, 0.306985918, 0.385779555, 0.461217861, 0.709444444) Probability of death from non severe treated infection. Default = c(0.0125702, 0.0125702, 0.0125702, 0.0125702, 0.0125702, 0.0125702, 0.0125702, 0.013361147, 0.015104687, 0.019164124, 0.027477519, 0.041762108, 0.068531658, 0.105302319, 0.149305732, 0.20349534, 0.5804312) Probability of death in non severe hospital inections that aren't treated Probability of death from severe infection that is treated. Default = rep(0.5, 17) Probability of death from severe infection that is not treated. Default = rep(0.95, 17) Preferentiality of age group receiving treatment relative to other age groups when demand exceeds healthcare capacity. Relative infectiousness per age category relative to maximum infectiousness category. Default = rep(1, 17) Relative infectiousness per age category of vaccinated individuals relative to unvaccinated individuals. Default = rep(1, 17), which is no impact of vaccination on onwards transmissions Mean duration of incubation period (days). Default = 4.6 Mean duration of mild infection (days). Default = 2.1 Mean duration from symptom onset to hospitil admission (days). Default = 4.5 Mean duration of oxygen given survive. Default = 5 Mean duration of oxygen given death. Default = 5 Mean duration without oxygen given survive. Default = 5 Mean duration without oxygen given death. Default = 5 Mean duration of ventilation given survive. Default = 7.3 Mean duration of ventilation given death. Default = 6 Mean duration without ventilation given survive. Default = 7.3 Mean duration without ventilation given death. Default = 1 Duration of recovery after coming off ventilation. Default = 2 Mean duration of naturally acquired immunity (days). Can be time varying, with timing of changes given by tt_dur_R. Timing of changes in duration of natural immunity. Mean duration of vaccine-derived immunity (days) Efficacy of vaccine against infection. This parameter must either be length 1 numeric (a single efficacy for all age groups) or length 17 numeric vector (an efficacy for each age group). An efficacy of 1 will reduce FOI by 100 percent, an efficacy of 0.2 will reduce FOI by 20 percent etc. To specify changes in vaccine efficacy over time, vaccine efficacies must be provided as a list, with each list element being the efficacy at each time point specified by tt_vaccine_efficacy_infection. These efficacies must also be length 1 numeric (a single efficacy for all age groups) or length 17 numeric vector (an efficacy for each age group) Timing of changes in vaccine efficacy against infection. Default = 0, which assumes fixed efficacy over time. Must be the same length as the length of vaccine_efficacy_infection when provided as a list. Time changing efficacies can occur in response to changing vaccines being given and dosing strategy changes. Efficacy of vaccine against severe (requiring hospitilisation) disease (by age). This parameter must either be length 1 numeric (a single efficacy for all age groups) or length 17 numeric vector (an efficacy for each age group). An efficacy of 1 will reduce the probability of hospitalisation by 100 percent, an efficacy of 0.2 will reduce the probability of hospitalisation by 20 percent etc. To specify changes in vaccine efficacy over time, vaccine efficacies must be provided as a list, with each list element being the efficacy at each time point specified by tt_vaccine_efficacy_disease. These efficacies must also be length 1 numeric (a single efficacy for all age groups) or length 17 numeric vector (an efficacy for each age group). Timing of changes in vaccine efficacy against disease. Default = 0, which assumes fixed efficacy over time. Must be the same length as the length of vaccine_efficacy_disease when provided as a list. Time changing efficacies can occur in response to changing vaccines being given and dosing strategy changes. The maximum number of individuals who can be vaccinated per day. Time change points for vaccine capacity (max_vaccine). Mean duration of period from vaccination to vaccine protection. Vaccine coverage targets by age (columns) and priority (row) General bed capacity. Can be single number of vector if capacity time-varies. ICU bed capacity. Can be single number of vector if capacity time-varies. Times at which hospital bed capacity changes (Default = 0 = doesn't change) Times at which ICU bed capacity changes (Default = 0 = doesn't change) Initial number of cases seeding the epidemic Vector specifying the order in which seeds are allocated to ages. If NULL, seeds are distributed randomly within working ages. If specified, must be a vector of length 17 specifying the order seeds are allocated, e.g. 1:17 will allocate first seed to the youngest age group, then the second youngest and so on. Default = NULL Initial conditions for simulation provided. Allows overriding if initial conditions start with an already infected population etc. Default = NULL. Use the dde solver (default is TRUE) Additional arguments for solver

## Value

Simulation output