| Title: | Deterministic Compartmental Model of Gonorrhoea with Vaccination |
|---|---|
| Description: | Model for gonorrhoea vaccination, using odin. |
| Authors: | Lilith Whittles [aut, cre], Dariya Nikitin [aut], Trystan Leng [aut] |
| Maintainer: | Lilith Whittles <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.4.23 |
| Built: | 2026-05-15 09:44:18 UTC |
| Source: | https://github.com/mrc-ide/gonovax |
adjust model run in absence of vaccination so diagnoses are spread across vaccine protected and non-vaccine protected strata as if the rate of movement was the same as in an equivalent model run in the presence of vaccination
adjust_baseline(baseline, y)adjust_baseline(baseline, y)
baseline |
A model run in the absence of vaccine uptake |
y |
A model run in the presence of vaccine uptake |
An adjusted baseline in which 'a' and 's' diagnoses in the baseline are divided across vaccine protected and non-vaccine protected strata allowing comparison of model runs with of baselines by vaccine-status
aggregate model runs by vaccination group x vaccine strata
aggregate( x, what, as_incid = FALSE, stratum = NULL, f = identity, stochastic = FALSE, ... )aggregate( x, what, as_incid = FALSE, stratum = NULL, f = identity, stochastic = FALSE, ... )
x |
a transformed model run output |
what |
a character string of the cumulative trajectory to transform |
as_incid |
logical specifying whether to convert to incidence |
stratum |
an integer denoting the stratum to aggregate over, if NULL aggregation is over all strata |
f |
function to apply across parameter sets (eg. mean / median) |
stochastic |
indicates if function is being used on output of stochastic or deterministic version of the trial model, only matters if as_incid = TRUE |
... |
named arguments to pass to f |
a transformed time series / array
Calculate the log likelihood of the data given the parameters diagnoses and attendances lhoods are negative binomial p_symp lhood is betabinomial
compare(pars, transform)compare(pars, transform)
pars |
A named vector of parameters |
transform |
the transform function to use in the comparison |
a single log likelihood
compare model runs with vaccination to a baseline runs
compare_baseline( y, baseline, uptake_first_dose, uptake_second_dose, cost_params, disc_rate )compare_baseline( y, baseline, uptake_first_dose, uptake_second_dose, cost_params, disc_rate )
y |
list of model runs, e.g. created by 'run_onevax_xvwv', each list entry refers to a different parameter set |
baseline |
list of baseline runs e.g. created by 'run_onevax_xvwv'. Should be same length as 'y' |
uptake_first_dose |
numeric (0-1) either length 1 or same as 'y' |
uptake_second_dose |
numeric (0-1) either length 1 or same as 'y' |
cost_params |
list of cost effectiveness parameters, containing entries 'qaly_loss_per_diag_s', 'unit_cost_manage_symptomatic', 'unit_cost_manage_asymptomatic', 'unit_cost_screen_uninfected'. Each entry length 1 or same as 'y' |
disc_rate |
discount rate for cost-effectiveness output per annum, default = 0 |
A list of matrices with rows = time point and columns = parameter set list entries are: 'cum_treated' = cumulative number treated in model run 'cum_vaccinated' = cumulative vaccinated treated in model run 'diag_a' = annual number of asymptomatic diagnoses in model run 'diag_s' = annual number of symptomatic diagnoses in model run 'treated' = annual number treated in model run 'screened' = annual number screened in model run 'vaccinated' = annual number vaccinated in model run (includes those getting vbe and boosters) 'vbe' = annual number vaccinated before entry in model run 'revaccinated' = annual number receiving booster in model run 'offered_primary' = annual number offered primary vaccination (does not include hesitant or vbe) 'inc_cum_treated' = cumulative number treated compared to baseline 'inc_cum_vaccinated' = cumulative number vaccinated compared to baseline 'inc_diag_a' = annual number of asymptomatic diagnoses compared to baseline 'inc_diag_s' = annual number of symptomatic diagnoses compared to baseline 'inc_treated' = annual number treated compared to baseline 'inc_screened' = annual number screened compared to baseline 'inc_vaccinated' = annual number vaccinated compared to baseline (includes those getting vbe and boosters) 'inc_vbe' = annual number vaccinated before entry compared to baseline 'inc_revaccinated' = annual number receiving booster compared to baseline 'inc_cum_revaccinated' = cumulative number revaccinated by recieving booster compared to baseline 'inc_offered_primary' = annual number offered primary vaccination compared to baseline (does not include hesitant or vbe) 'inc_primary' = annual number receiving primary vaccination compared to baseline (does not include vbe) 'inc_cum_primary' = cumulative number of individuals receiving primary vaccination compared to baseline (does not include vbe) 'inc_doses' = number of doses compared to baseline (per year). Assumes primary vaccination uses 2 doses, booster uses 1 dose. 'inc_cum_doses' = cumulative number of doses compared to baseline. Assumes primary vaccination uses 2 doses, booster uses 1 dose. 'inc_primary_doses' = annual number of primary doses administered compared to baseline, where 2 doses are needed for full protection. 'inc_cum_primary_doses' = cumulative number of primary doses administered compared to baseline, where 2 doses are needed for full protection. 'inc_booster_doses' = annual number of booster doses administered to waned individuals for revaccination compared to baseline, where 1 dose is needed for full protection. 'inc_cum_booster_doses' = cumulative number of booster doses administered to waned individuals for revaccination compared to baseline, where 1 dose is needed for full protection. 'inc_vbe_doses' = annual number of vaccinations given before entry, where 2 doses give full protection. 'inc_cum_vbe_doses' = cumulative number of vaccinations given before entry, where 2 doses give full protection. 'cases_averted_per_dose' = cumulative number of cases (i.e. diagnoses) averted per dose of vaccine 'cases_averted_per_dose_pv' = present value of cases_averted_per_dose (i.e. sum of annual numbers discounted at rate 'disc_rate' to time 0) 'inc_doses_wasted' annual number of first doses given that are not followed by a second dose, compared to baseline 'inc_cum_doses_wasted' cumulative number of first doses given that are not followed by a second dose, compared to baseline 'pv_inc_doses' = present value of number of doses compared to baseline (i.e. sum of annual numbers discounted at rate 'disc_rate' to time 0) 'pv_red_net_cost' = present value of the reduction in net costs compared to baseline (i.e. sum of annual reduction in costs discounted at rate 'disc_rate' to time 0) 'pv_qaly_gain' = present value of the QALYs gained due to vaccination compared to baseline (i.e. QALYs gained each year discounted at rate 'disc_rate' to time 0, and summed) 'cet_20k' = cost effectiveness threshold (i.e. £ value per dose at which vaccination becomes cost-effective) calculated using £20,000 / QALY. (pv_red_net_cost + pv_qaly_gain * £20,000) / pv_inc_doses 'cet_30k' = cost effectiveness threshold (i.e. £ value per dose at which vaccination becomes cost-effective) calculated using £30,000 / QALY (pv_red_net_cost + pv_qaly_gain * £30,000) / pv_inc_doses 'inc_costs_9' = present value of incremental costs assuming £9 / dose. Incremental costs are calculated as: pv_inc_doses * £9 - pv_red_net_cost 'inc_costs_18' = present value of incremental costs assuming £18 / dose. Incremental costs are calculated as: pv_inc_doses * £18 - pv_red_net_cost 'inc_costs_50' = present value of incremental costs assuming £50 / dose. Incremental costs are calculated as: pv_inc_doses * £50 - pv_red_net_cost 'inc_costs_85' = present value of incremental costs assuming £85 / dose Incremental costs are calculated as: pv_inc_doses * £85 - pv_red_net_cost
compare model runs with vaccination to a baseline run for the branching XPVWRH model where both partial and full vaccination have a given level of efficacy
compare_baseline_xpvwrh( y, baseline, uptake_first_dose, uptake_second_dose, cost_params, disc_rate, vea, vea_p )compare_baseline_xpvwrh( y, baseline, uptake_first_dose, uptake_second_dose, cost_params, disc_rate, vea, vea_p )
y |
list of model runs, e.g. created by 'run_onevax_xpvwrh', each list entry refers to a different parameter set |
baseline |
list of baseline runs e.g. created by 'run_onevax_xpvwrh'. Should be same length as 'y' |
uptake_first_dose |
numeric (0-1) either length 1 or same as 'y' |
uptake_second_dose |
numeric (0-1) either length 1 or same as 'y' |
cost_params |
list of cost effectiveness parameters, containing entries 'qaly_loss_per_diag_s', 'unit_cost_manage_symptomatic', 'unit_cost_manage_asymptomatic', 'unit_cost_screen_uninfected'. Each entry length 1 or same as 'y' |
disc_rate |
discount rate for cost-effectiveness output per annum, default = 0 |
vea |
= scalar indicating efficacy of full vaccination against aquisition (between 0-1) |
vea_p |
scalar indicating efficacy of partial vaccination against acquisition (between 0-1) |
A list of matrices with rows = time point and columns = parameter set list entries are: 'cum_treated' = cumulative number treated in model run 'cum_vaccinated' = cumulative vaccinated treated in model run 'diag_a' = annual number of asymptomatic diagnoses in model run 'diag_s' = annual number of symptomatic diagnoses in model run 'treated' = annual number treated in model run 'screened' = annual number screened in model run 'vaccinated' = annual number vaccinated in model run (includes those getting vbe and boosters) 'vbe' = annual number vaccinated before entry in model run 'revaccinated' = annual number receiving booster in model run 'inc_cum_treated' = cumulative number treated compared to baseline 'pv_cases_averted' = cumulative number treated when discounting applied compared to baseline 'inc_cum_vaccinated' = cumulative number vaccinated compared to baseline 'inc_diag_a' = annual number of asymptomatic diagnoses compared to baseline 'inc_diag_s' = annual number of symptomatic diagnoses compared to baseline 'inc_treated' = annual number treated compared to baseline 'inc_screened' = annual number screened compared to baseline 'inc_vaccinated' = annual number vaccinated compared to baseline (includes those getting vbe and boosters) 'inc_vbe' = annual number vaccinated before entry compared to baseline 'inc_revaccinated' = annual number receiving booster compared to baseline 'inc_cum_revaccinated' = cumulative number revaccinated by recieving booster compared to baseline 'inc_primary' = annual number receiving primary vaccination (full or partial) compared to baseline (does not include vbe) 'inc_cum_primary' = cumulative number of individuals receiving primary vaccination (full or partial) compared to baseline (does not include vbe) 'inc_part_to_full'= annual number of individuals who have already received their 1st dose, who receive their 2nd dose compared to baseline 'inc_cum_part_to_full' = cumulative number of individuals who have already received their 1st dose, who receive their 2nd dose compared to baseline 'inc_doses' = number of doses compared to baseline (per year). Assumes primary vaccination uses 2 doses, booster uses 1 dose. 'inc_cum_doses' = cumulative number of doses compared to baseline. Assumes primary vaccination uses 2 doses, booster uses 1 dose. 'inc_primary_part_doses' = annual number of 1st doses administered as part of primary vaccination which were not followed by a 2nd dose compared to baseline. 'inc_cum_primary_part_doses' = cumulative number of 1st doses administered as part of primary vaccination which were not followed by a 2nd dose compared to baseline. 'inc_primary_full_doses' = annual number 1st and 2nd doses administered together as part of primary vaccination compared to baseline. 'inc_cum_primary_full_doses' = cumulative number of 1st and 2nd doses administered together as part of primary vaccination compared to baseline. 'inc_primary_total_doses' = annual number of primary doses administered compared to baseline. Includes doses contributing to partial and full vaccination 'inc_cum_primary_total_doses' = cumulative number of primary doses administered compared to baseline. Includes doses contributing to partial and full vaccination. 'inc_booster_doses' = annual number of booster doses administered to waned individuals for revaccination compared to baseline, where 1 dose is needed for full protection. 'inc_cum_booster_doses' = cumulative number of booster doses administered to waned individuals for revaccination compared to baseline, where 1 dose is needed for full protection. 'inc_vbe_doses' = annual number of vaccinations given before entry, where 2 doses give full protection. 'inc_cum_vbe_doses' = cumulative number of vaccinations given before entry, where 2 doses give full protection. 'cases_averted_per_dose' = cumulative number of cases (i.e. diagnoses) averted per dose of vaccine 'cases_averted_per_dose_pv' = present value of cases_averted_per_dose (i.e. sum of annual numbers discounted at rate 'disc_rate' to time 0) 'pv_inc_doses' = present value of number of doses compared to baseline (i.e. sum of annual numbers discounted at rate 'disc_rate' to time 0) 'pv_red_net_cost' = present value of the reduction in net costs compared to baseline (i.e. sum of annual reduction in costs discounted at rate 'disc_rate' to time 0) 'pv_qaly_gain' = present value of the QALYs gained due to vaccination compared to baseline (i.e. QALYs gained each year discounted at rate 'disc_rate' to time 0, and summed) 'cet_20k' = cost effectiveness threshold (i.e. £ value per dose at which vaccination becomes cost-effective) calculated using £20,000 / QALY. (pv_red_net_cost + pv_qaly_gain * £20,000) / pv_inc_doses 'cet_30k' = cost effectiveness threshold (i.e. £ value per dose at which vaccination becomes cost-effective) calculated using £30,000 / QALY (pv_red_net_cost + pv_qaly_gain * £30,000) / pv_inc_doses 'inc_costs_9' = present value of incremental costs assuming £9 / dose. Incremental costs are calculated as: pv_inc_doses * £9 - pv_red_net_cost 'inc_costs_18' = present value of incremental costs assuming £18 / dose. Incremental costs are calculated as: pv_inc_doses * £18 - pv_red_net_cost 'inc_costs_35' = present value of incremental costs assuming £35 / dose. Incremental costs are calculated as: pv_inc_doses * £35 - pv_red_net_cost 'inc_costs_50' = present value of incremental costs assuming £50 / dose. Incremental costs are calculated as: pv_inc_doses * £50 - pv_red_net_cost 'inc_costs_70' = present value of incremental costs assuming £70 / dose. Incremental costs are calculated as: pv_inc_doses * £70 - pv_red_net_cost 'inc_costs_85' = present value of incremental costs assuming £85 / dose Incremental costs are calculated as: pv_inc_doses * £85 - pv_red_net_cost 'vacprotec_full' = number of fully vaccine protected individuals in the population at a given timepoint 'vacprotec_part' = number of partially vaccine protected individuals in the population at a given timepoint 'vacprotec_total' = total number of vaccine protected individuals in the population (both partially and fully vaccinated) at a given timepoint 'vacprotec_full_prop' = proportion of the population experiencing full vaccine protection at a given timepoint 'vacprotec_part_prop' = proportion of the population experiencing partial vaccine protection at a given timepoint 'vacprotec_total_prop' = total proportion of the population experiencing some form of vaccine protection at a given timepoint 'level_vacprotec' = the level of vaccine protection in the population at a given timepoint. Given as the sum of the products of the number of partially and fully vaccinated individuals and the one dose and two dose vaccine efficacies respectively. 'inc_diag_a_xwh' = annual number of asymptomatic diagnoses in non- vaccine protected strata compared to an adjusted baseline 'inc_diag_s_xwh' = annual number of symptomatic diagnoses in non- vaccine protected strata compared to an adjusted baseline 'inc_diag_t_xwh' = annual number of total diagnoses in non-vaccine protected strata compared to an adjusted baseline 'inc_diag_a_pvr' = annual number of asymptomatic diagnoses in vaccine protected strata compared to an adjusted baseline 'inc_diag_s_pvr' = annual number of symptomatic diagnoses in vaccine protected strata compared to an adjusted baseline 'inc_diag_t_pvr' = annual number of total diagnoses in vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_a_xwh' = cumulative number of asymptomatic diagnoses in non- vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_s_xwh' = cumulative number of symptomatic diagnoses in non- vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_t_xwh' = cumulative number of total diagnoses in non-vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_a_pvr' = cumulative number of asymptomatic diagnoses in vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_s_pvr' = cumulative number of symptomatic diagnoses in vaccine protected strata compared to an adjusted baseline 'inc_cum_diag_t_pvr' = cumulative number of total diagnoses in vaccine protected strata compared to an adjusted baseline 'percentage_cases_avert' = percentage of cases avoided in each time-point compared to a baseline of no vaccination when cases are counted as successful treatment 'cum_percentage_cases_avert'= percentage of cases avoided cumulatively across all timepoints compared to a baseline of no vaccination when cases are counted as successful treatment 'percentage_diag_avert' = percentage of cases avoided in each time-point compared to a baseline of no vaccination when cases are counted as the sum of all diagnoses 'cum_percentage_diags_avert'= percentage of cases avoided cumulatively across all timepoints compared to a baseline of no vaccination when cases are counted as the sum of all diagnoses 'inc_diag_t' = total number of diagnoses in each time point 'inc_cum_diag_t' = cumulative total number of diagnoses over time 'inc_cum_diag_a' = cumulative number of asymptomatic diagnoses over time 'inc_cum_diag_s' = cumulative number of symptomatic diagnoses over time 'pv_num_vaccinations' = present value cumulative number of vaccinations
Calculate the log likelihood of the data given the parameters
compare_basic(pars)compare_basic(pars)
pars |
A named vector of parameters |
a single log likelihood
Create mapping for movement between strata due to diagnosis waning
create_diagnosis_waning_map(n_vax, z, n_diag_rec = 1)create_diagnosis_waning_map(n_vax, z, n_diag_rec = 1)
n_vax |
Integer in (0, 5) denoting total number of strata |
z |
Scalar denoting rate of waning diagnosis |
n_diag_rec |
integer for the number of diagnosis history substrata |
an array of the mapping
Creates uptake mapping array with dimensions n_group x n_vax x n_vax and assigns the relevant primary uptake and booster uptake values defined by the user.
create_uptake_map( n_group, n_vax, primary_uptake, booster_uptake, i_eligible, i_v, screening_or_diagnosis )create_uptake_map( n_group, n_vax, primary_uptake, booster_uptake, i_eligible, i_v, screening_or_diagnosis )
n_group |
scalar indicating number of activity groups |
n_vax |
scalar indicating number the number of stratum in the model |
primary_uptake |
proportion of the unvaccinated population who accept primary vaccination |
booster_uptake |
proportion of the formerly fully vaccinated, waned population who accept a booster vaccination dose |
i_eligible |
vector of indices of stratum which are eligible for vaccination, of same length as the number of paths from unvaccinated to vaccinated |
i_v |
vector of indices of stratum which are vaccinated and experience protection |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes with dimensions n_group x n_vax x n_vax
Creates uptake mapping for the branching XPVWRH model where individuals can move from unvaccinated (X) to vaccinated (V) or partially vaccinated (P) as well as revaccinated from waned (W) to (R) and, and partially vaccinated (P) to fully vaccianted (V). The former reflects the specific indices which are chosen for assigning uptakes.
create_uptake_map_xpvwrh( array, r1, r2, r2_p, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )create_uptake_map_xpvwrh( array, r1, r2, r2_p, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )
array |
a vaccine map array of dimensions n_group by n_vax by n_vax generated through create_vax_map_branching() |
r1 |
proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r2_p |
proportion of partially vaccinated individuals who receive a second dose when returning to the clinic due to screening or illness |
booster_uptake |
proportion of the formerly fully vaccinated, waned population who accept a booster vaccination dose |
idx |
list containing indices of all X, P, V, W, R & H strata and n_vax through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes of same dimensions
Creates uptake mapping for the branching XPVWRH model where individuals can move from unvaccinated (X) to vaccinated (V) or partially vaccinated (P) as well as revaccinated from waned (W) to (R) and, and partially vaccinated (P) to fully vaccianted (V). The former reflects the specific indices which are chosen for assigning uptakes.
create_uptake_map_xpvwrh_trackvt( array, r1, r2, r1_p, r2_p, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )create_uptake_map_xpvwrh_trackvt( array, r1, r2, r1_p, r2_p, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )
array |
a vaccine map array of dimensions n_group by n_vax by n_vax generated through create_vax_map_branching() |
r1 |
proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r1_p |
proportion of partially vaccinated individuals who receive a >=1 dose when returning to the clinic due to screening or illness |
r2_p |
proportion of partially vaccinated individuals who receive a second dose when returning to the clinic due to screening or illness |
booster_uptake |
proportion of the formerly fully vaccinated, waned population who accept a booster vaccination dose |
idx |
list containing indices of all X, P, V, W, R & H strata and n_vax through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes of same dimensions
Creates uptake mapping array with dimensions n_group x n_vax x n_vax and assigns the relevant primary uptake and booster uptake values defined by the user.
create_uptake_map_xvw( n_group, n_vax, uptake, idx, n_diag_rec = 1, screening_or_diagnosis )create_uptake_map_xvw( n_group, n_vax, uptake, idx, n_diag_rec = 1, screening_or_diagnosis )
n_group |
scalar indicating number of activity groups |
n_vax |
scalar indicating number the number of stratum in the model |
uptake |
proportion of the unvaccinated population who accept vaccination |
idx |
list containing indices of all X, V, W strata and n_vax through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes with dimensions n_group x n_vax x n_vax
Creates uptake mapping array with dimensions n_group x n_vax x n_vax and assigns the relevant primary uptake and booster uptake values defined by the user.
create_uptake_map_xvwr( n_group, n_vax, primary_uptake, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )create_uptake_map_xvwr( n_group, n_vax, primary_uptake, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )
n_group |
scalar indicating number of activity groups |
n_vax |
scalar indicating number the number of stratum in the model |
primary_uptake |
proportion of the unvaccinated population who accept primary vaccination |
booster_uptake |
proportion of the formerly fully vaccinated, waned population who accept a booster vaccination dose |
idx |
list containing indices of all X, V, W, R strata and n_vax through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes with dimensions n_group x n_vax x n_vax
Creates uptake mapping array with dimensions n_group x n_vax x n_vax and assigns the relevant primary uptake and booster uptake values defined by the user.
create_uptake_map_xvwv( n_group, n_vax, primary_uptake, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )create_uptake_map_xvwv( n_group, n_vax, primary_uptake, booster_uptake, idx, n_diag_rec = 1, screening_or_diagnosis )
n_group |
scalar indicating number of activity groups |
n_vax |
scalar indicating number the number of stratum in the model |
primary_uptake |
proportion of the unvaccinated population who accept primary vaccination |
booster_uptake |
proportion of the formerly fully vaccinated, waned population who accept a booster vaccination dose |
idx |
list containing indices of all X, V, W strata and n_vax through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
screening_or_diagnosis |
string indicating screening or diagnosis |
an array of the uptakes with dimensions n_group x n_vax x n_vax
Create mapping for movement between strata due to vaccination
create_vax_map(n_vax, v, i_u, i_v)create_vax_map(n_vax, v, i_u, i_v)
n_vax |
Integer denoting total number of strata |
v |
0-1 vector of length two indicating whether activity group should be offered vaccination. |
i_u |
indices of strata eligible for vaccination |
i_v |
indices of strata being vaccinated |
an array of the mapping
Create mapping for movement between strata due to vaccination where vaccination uptake splits off into two types (partial and full) from the naive population (X). Different to create_vax_map as this function specifically maps vbe to V (3) than P(2)
create_vax_map_branching(n_vax, v, i_e, i_p, set_vbe = FALSE, idx)create_vax_map_branching(n_vax, v, i_e, i_p, set_vbe = FALSE, idx)
n_vax |
Integer denoting total number of strata |
v |
0-1 vector of length two indicating whether activity group should be offered vaccination. |
i_e |
indices of strata eligible for vaccination |
i_p |
indices of strata vaccinated and protected |
set_vbe |
Boolean which indicates that vaccination is occurring at some level of uptake upon entering the model |
idx |
list containing indices of all X, P, V, W, R & H strata and n_vax through vaccine-protected strata until that protection has waned |
an array of the mapping
Create mapping for movement between strata due to vaccine waning
create_waning_map(n_vax, i_v, i_w, z, n_diag_rec = 1)create_waning_map(n_vax, i_v, i_w, z, n_diag_rec = 1)
n_vax |
Integer in (0, 5) denoting total number of strata |
i_v |
indices of strata being vaccinated |
i_w |
Integer in (0, 5) denoting which stratum receives waned vaccinees |
z |
Scalar denoting rate of waning |
n_diag_rec |
integer for the number of diagnosis history substrata |
an array of the mapping
Create mapping for movement between strata due to vaccine waning where waning from the partially vaccinated stratum (P) moves individuals back to a naive unvaccinated state (X), and waning from fully vaccinated stratum (V) moves individuals into a separate waned stratum (W) Note, this structure is specific to xpvwrh
create_waning_map_branching(n_vax, i_v, i_w, z, n_erlang = 1, n_diag_rec = 1)create_waning_map_branching(n_vax, i_v, i_w, z, n_erlang = 1, n_diag_rec = 1)
n_vax |
Integer denoting total number of strata |
i_v |
indices of strata receiving protection through vaccination |
i_w |
Scalar in (0, 6) denoting which stratum receives waned vaccinees |
z |
Scalar denoting rate of waning |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer giving number of diagnosis history strata |
an array of the mapping
Create mapping for movement between strata due to vaccine waning in a vaccine trial with erlang compartments
create_waning_map_trial(n_vax, i_v, i_w, z)create_waning_map_trial(n_vax, i_v, i_w, z)
n_vax |
Integer in (0, 5) denoting total number of strata |
i_v |
indices of strata under vaccination protection |
i_w |
indicies denoting which stratum receives waned vaccinees |
z |
Scalar denoting rate of waning |
an array of the mapping
pdf of a betabinomial parametrised in terms of probability and over-dispersion
dbetabinom(x, size, prob, rho, log = FALSE)dbetabinom(x, size, prob, rho, log = FALSE)
x |
data |
size |
integer of sample size |
prob |
probability of observing a single success |
rho |
overdispersion parameter, has support [0, 1] with low values being less overdispersion |
log |
logical indicating whether to return log value |
probability of observing x
extract flows used for run_grid
extract_flows(y)extract_flows(y)
y |
a transformed model run output |
cumulative and incident flows
extract flows for the XVW trial model
extract_flows_trial(y)extract_flows_trial(y)
y |
a transformed model run output |
cumulative and incident flows
extract flows used for run_grid when the branching xpvwrh model has been run
extract_flows_xpvwrh(y)extract_flows_xpvwrh(y)
y |
a transformed model run output |
cumulative and incident flows
generates the appropriate strata labels for the number of strata in the model, which depends on the value given to n_erlang
gen_erlang_labels(n_erlang = 1, n_diag_rec = 1)gen_erlang_labels(n_erlang = 1, n_diag_rec = 1)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
a character vector of length n_vax containing strata labels
generates the appropriate strata labels for the number of strata in the model, which depends on the value given to n_erlang
gen_erlang_labels_trackvt(n_erlang = 1, n_diag_rec = 1)gen_erlang_labels_trackvt(n_erlang = 1, n_diag_rec = 1)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
a character vector of length n_vax containing strata labels
generates the appropriate strata labels for the number of strata in the model, which depends on the value given to n_erlang and diagnosis history levels desired (n_diag_rec)
gen_labels(n_erlang = 1, n_diag_rec = 1)gen_labels(n_erlang = 1, n_diag_rec = 1)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer giving the number of levels of diagnosis history for each X, V(*n_erlang), and W stratum |
a character vector of length n_vax containing strata labels
generates the appropriate strata labels for the number of strata in the model, which depends on the value given to n_erlang and diagnosis history levels desired (n_diag_rec)
gen_trial_labels(n_erlang = 1, n_diag_rec = 1)gen_trial_labels(n_erlang = 1, n_diag_rec = 1)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer giving the number of levels of diagnosis history for each X, V(*n_erlang), and W stratum |
a character vector of length n_vax containing strata labels
Posterior parameters of gonorrhoea natural history
gono_params(n = NULL)gono_params(n = NULL)
n |
an integer vector (or value) containing the indices of the required parameter sets (1:982). If 'n = NULL' the full parameter set is returned |
A list of parameters
Posterior parameters of gonorrhoea natural history
gono_params_trial(n = NULL)gono_params_trial(n = NULL)
n |
an integer vector (or value) containing the indices of the required parameter sets (1:982). If 'n = NULL' the full parameter set is returned |
A list of parameters
Get annual time series of GUMCAD diagnoses and testing figures, and annual proportion symptomatic from GRASP
gonovax_data()gonovax_data()
A data.frame containing the gonovax year (i.e. year after 2007), the year, the total diagnoses in MSM, the total tests in MSM and the source of the data, the proportion of symptomatic diagnoses
Convert a year into the number of years after 2009
gonovax_year(year)gonovax_year(year)
year |
an integer year |
An integer, being the number of years after 2009
gonovax_year(2019) gonovax_year(c(2018, 2019))gonovax_year(2019) gonovax_year(c(2018, 2019))
Convert a gonovax year into calendar years
gonovax_year_as_year(gonovax_year)gonovax_year_as_year(gonovax_year)
gonovax_year |
an integer |
An integer, being the calendar year
gonovax_year_as_year(3)gonovax_year_as_year(3)
Create initial conditions for the model
Create initial conditions for the model
initial_params_xvw(pars, coverage = 0) initial_params(pars, n_vax = 1, coverage = 1)initial_params_xvw(pars, coverage = 0) initial_params(pars, n_vax = 1, coverage = 1)
pars |
A parameter list containing 'N0', 'q', 'prev_Asl' and 'prev_Ash' elements. |
coverage |
a vector of length 'n_vax' that sums to 1 denoting the initial proportion in each vaccine stratum |
n_vax |
an integer indicating the number of vaccine compartments |
A list of initial conditions
A list of initial model states
Create initial conditions for the model trial
initial_params_trial(pars, n_vax = 1, p_v = 1, n_diag_rec = n_diag_rec)initial_params_trial(pars, n_vax = 1, p_v = 1, n_diag_rec = n_diag_rec)
pars |
A parameter list containing 'N0', and 'q' elements. |
n_vax |
an integer indicating the number of vaccine compartments |
p_v |
a vector of length 'n_vax' that sums to 1 denoting the proportion in each vaccine stratum |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
A list of initial model states
Create initial conditions for the model
initial_params_xpvwrh( pars, coverage_p = 0, coverage_v = 0, hes = 0, t = FALSE, n_erlang = 1, n_diag_rec = 1 )initial_params_xpvwrh( pars, coverage_p = 0, coverage_v = 0, hes = 0, t = FALSE, n_erlang = 1, n_diag_rec = 1 )
pars |
A parameter list containing 'N0', 'q', 'prev_Asl' and 'prev_Ash' elements. |
coverage_p |
partial (one-dose) vaccine coverage of the population already present (as a proportion) |
coverage_v |
two-dose vaccine coverage of the population already present (as a proportion) |
hes |
proportion of population vaccine hesitant |
t |
number of years, only use when using function outside of run_onevax_xpvwrh() to generate initial conditions for tests |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
number of diagnosis history strata |
A list of initial conditions
Create initial conditions for the model
initial_params_xpvwrh_trackvt( pars, coverage_p = 0, coverage_v = 0, hes = 0, t = FALSE, n_erlang = 1, n_diag_rec = 1 )initial_params_xpvwrh_trackvt( pars, coverage_p = 0, coverage_v = 0, hes = 0, t = FALSE, n_erlang = 1, n_diag_rec = 1 )
pars |
A parameter list containing 'N0', 'q', 'prev_Asl' and 'prev_Ash' elements. |
coverage_p |
partial (one-dose) vaccine coverage of the population already present (as a proportion) |
coverage_v |
two-dose vaccine coverage of the population already present (as a proportion) |
hes |
proportion of population vaccine hesitant |
t |
number of years, only use when using function outside of run_onevax_xpvwrh() to generate initial conditions for tests |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
number of diagnosis history strata |
A list of initial conditions
Create initial conditions for the model in a vaccine trial
initial_params_xvw_trial(pars, p_v = 0.5, n_erlang = 1, n_diag_rec = 1)initial_params_xvw_trial(pars, p_v = 0.5, n_erlang = 1, n_diag_rec = 1)
pars |
A parameter list containing 'N0', and 'q' elements. |
p_v |
scalar giving proportion of the trial cohort vaccinated |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
A list of initial conditions.
Create initial conditions for the model
initial_params_xvwrh(pars, coverage = 0, hes = 0, n_diag_rec = 1)initial_params_xvwrh(pars, coverage = 0, hes = 0, n_diag_rec = 1)
pars |
A parameter list containing 'N0', 'q', 'prev_Asl' and 'prev_Ash' elements. |
coverage |
a vector of length 'n_vax' that sums to 1 denoting the initial proportion in each vaccine stratum |
hes |
proportion of population vaccine hesitant |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list of initial conditions
Run a mcmc sampler
mcmc(pars, n_steps, compare = NULL, progress = FALSE, n_chains = 1)mcmc(pars, n_steps, compare = NULL, progress = FALSE, n_chains = 1)
pars |
A ['mcmc_parameters'] object containing information about parameters ( parameter ranges, priors, proposal kernel, observation functions). |
n_steps |
Number of MCMC steps to run |
compare |
likelihood function to compare data to epidemic trajectory should return a single value representing the log-likelihood. Default is compare_basic() |
progress |
Logical, indicating if a progress bar should be displayed, using ['progress::progress_bar']. |
n_chains |
Optional integer, indicating the number of chains to run. If more than one then we run a series of chains and merge them with [mcmc_combine()]. Chains are run in series, with the same model. |
This is a basic Metropolis-Hastings MCMC sampler. The 'model' is run with a set of parameters to evaluate the likelihood. A new set of parameters is proposed, and these likelihoods are compared, jumping with probability equal to their ratio. This is repeated for 'n_steps' proposals.
This function is adapted from the 'pmcmc' in the mcstate package https://github.com/mrc-ide/mcstate
A 'gonovax_mcmc' object containing 'pars' (sampled parameters) and 'probabilities' (log prior, log likelihood and log posterior values for these probabilities).
all functions in mcmc-tools.R are adapted from pmcmc-tools.R functions in github.com/mrc-ide/mcstate Combine multiple [mcmc()] samples into one object
mcmc_combine(..., samples = list(...))mcmc_combine(..., samples = list(...))
... |
Arguments representing [mcmc()] sample, i.e., 'gonovax_mcmc' objects. Alternatively, pass a list as the argument 'samples'. Names are ignored. |
samples |
A list of 'gonovax_mcmc' objects. This is often more convenient for programming against than '...' |
Thin results of running [mcmc()].‘mcmc_thin' takes every 'thin'’th sample, while 'mcmc_sample' randomly selects a total of 'n_sample' samples.
mcmc_thin(object, burnin = NULL, thin = NULL) mcmc_sample(object, n_sample, burnin = NULL)mcmc_thin(object, burnin = NULL, thin = NULL) mcmc_sample(object, n_sample, burnin = NULL)
object |
Results of running [mcmc()] |
burnin |
Optional integer number of iterations to discard as "burn-in". If given then samples '1:burnin' will be excluded from your results. Remember that the first sample represents the starting point of the chain. It is an error if this is not a positive integer or is greater than or equal to the number of samples (i.e., there must be at least one sample remaining after discarding burnin). |
thin |
Optional integer thinning factor. If given, then every ‘thin'’th sample is retained (e.g., if 'thin' is 10 then we keep samples 1, 11, 21, ...). |
n_sample |
The number of samples to draw from 'object' *with replacement*. This means that 'n_sample' can be larger than the total number of samples taken (though it probably should not) |
Model of gonorrhoea with dual vaccines This is an odin model.
Parameters for the dualvax model
model_params( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_diag_rec = 1 )model_params( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_diag_rec = 1 )
gono_params |
A dataframe of natural history parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list of inputs to the model many of which are fixed and represent data. These correspond largely to 'user()' calls within the odin code, though some are also used in processing just before the model is run.
Parameters for the vaccination trial model
model_params_trial( gono_params_trial = NULL, demographic_params_trial = NULL, initial_params_trial = NULL, vax_params = NULL, p_v = 0, n_erlang = 1, N = 6e+05, n_diag_rec = 1, asymp_recorded = TRUE )model_params_trial( gono_params_trial = NULL, demographic_params_trial = NULL, initial_params_trial = NULL, vax_params = NULL, p_v = 0, n_erlang = 1, N = 6e+05, n_diag_rec = 1, asymp_recorded = TRUE )
gono_params_trial |
A dataframe of natural history parameters |
demographic_params_trial |
A dataframe of demographic parameters |
initial_params_trial |
A list of starting conditions |
vax_params |
A vector of vaccination params |
p_v |
A scalar indicating the percentage of the trial cohort that is vaccinated |
n_erlang |
scalar giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
N |
integer to assign the total number of individuals in the trial (split equally across the two arms) |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
asymp_recorded |
logical indicating if the trial screens for and records asymptomatic diagnosis. If FALSE, asymptomatic infected individuals undergoing treatment do not move diagnosis history stratum |
A list of inputs to the model many of which are fixed and represent data. These correspond largely to 'user()' calls within the odin code, though some are also used in processing just before the model is run.
Parameters for the dualvax model
model_params_xpvwrh( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1 )model_params_xpvwrh( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
gono_params |
A dataframe of natural history parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
A list of inputs to the model many of which are fixed and represent data. These correspond largely to 'user()' calls within the odin code, though some are also used in processing just before the model is run.
Parameters for the xpvrwrh model tracking time since vaccination
model_params_xpvwrh_trackvt( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1 )model_params_xpvwrh_trackvt( gono_params = NULL, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
gono_params |
A dataframe of natural history parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
A list of inputs to the model many of which are fixed and represent data. These correspond largely to 'user()' calls within the odin code, though some are also used in processing just before the model is run.
uses XPVWRH model run in the absence of vaccination or hesitancy. Saves down the number of individuals in each compartment, and moves a given proportion (hes) of them from the X to the H strata to generate new initial conditions in the presence of hesitancy.
restart_hes( y, n_vax = 6, hes = 0, n_erlang = 1, n_diag_rec = 1, branching = FALSE )restart_hes( y, n_vax = 6, hes = 0, n_erlang = 1, n_diag_rec = 1, branching = FALSE )
y |
a transformed model run output |
n_vax |
an integer indicating the number of vaccine compartments, consistent with the input |
hes |
proportion of population vaccine hesitant |
n_erlang |
integer giving the number of transitions that need to be made |
n_diag_rec |
integer for the number of diagnosis history substrata |
branching |
boolean to denote if xpvwrh branching model in use |
A list of initial conditions to restart a model with n_vax vaccination levels, and a populated hestitant stratum in the given proportion 'hes'
Create initial conditions based on previous model run
restart_params(y, n_vax = NULL)restart_params(y, n_vax = NULL)
y |
a transformed model run output |
n_vax |
an integer indicating the number of vaccine compartments, consistent with the input |
A list of initial conditions to restart a model with n_vax vaccination levels
Run odin model of gonorrhoea with or without vaccination
run( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_diag_rec = 1, transform = TRUE )run( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_diag_rec = 1, transform = TRUE )
tt |
a numeric vector of times at which the model state is output |
gono_params |
a data frame of parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_diag_rec |
integer for the number of diagnosis history substrata |
transform |
= TRUE |
run model from equilibrium with single vaccine at the input efficacy / duration grid locations for n parameter sets
run_grid( gono_params, init_params, cost_params, baseline, model, eff, dur, vbe = 0, strategy = NULL, uptake_total = 0, uptake_second_dose = uptake_total, t_stop = 99, full_output = FALSE, disc_rate = 0 )run_grid( gono_params, init_params, cost_params, baseline, model, eff, dur, vbe = 0, strategy = NULL, uptake_total = 0, uptake_second_dose = uptake_total, t_stop = 99, full_output = FALSE, disc_rate = 0 )
gono_params |
gono params |
init_params |
initial state parameters |
cost_params |
cost effectiveness parameters |
baseline |
optional input of a baseline to compare to, must be a gonovax_grid object if supplied |
model |
a gonovax run function, by default 'run_onevax' |
eff |
numeric vector (between 0-1) of vaccine efficacy against acquisition |
dur |
numeric vector of duration in years of the vaccine |
vbe |
single numeric indicating (between 0-1), default = 0 |
strategy |
default is null, no vaccination 'VoD': vaccination on diagnosis, 'VoA': vaccination on attendance, 'VoD(L)+VoA(H)': targeted vaccination (i.e. all diagnosed plus group H on screening) |
uptake_total |
numeric (0-1) of strategy uptake, default = 0 |
uptake_second_dose |
numeric (0-1) of strategy uptake, default = uptake_total |
t_stop |
time at which vaccination should stop (years), default = 99 |
full_output |
logical indicating whether full results should be output |
disc_rate |
discount rate for cost-effectiveness output per annum, default = 0 |
A 'gonovax_grid' object
run model with a two-dose vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are eligible for revaccination (R), and return to the R stratum, those with waned partial vaccines return to the unvaccinated stratum (X) and considered immunologically naive. A user defined proportion of the population is vaccine hesitant and is never vaccinated. Full acciantion (V) with 2 doses gives maximum protection whereas partial vaccination with 1 dose (P) gives less. Individuals can get 2 doses either by committing in X or upon visiting a clinic in P.
run_onevax_xpvwrh( tt, gono_params, init_params = NULL, dur_v = 1000, dur_p = dur_v, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur_v, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, vbe = 0, r1 = 0, r2 = 0, r2_p = 0, booster_uptake = (r1 * r2), strategy = NULL, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )run_onevax_xpvwrh( tt, gono_params, init_params = NULL, dur_v = 1000, dur_p = dur_v, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur_v, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, vbe = 0, r1 = 0, r2 = 0, r2_p = 0, booster_uptake = (r1 * r2), strategy = NULL, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur_v |
duration of time spent in V stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_p |
duration of time spent in the P stratum, partially vaccinated (accepting only the first dose) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
dur_revax |
scalar or numeric vector with same length as 'gono_params' giving duration of protection for revaccination, default to same as primary |
vea_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against acquisition, default to same as primary |
vei_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against infectiousness, default to same as primary |
ved_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against duration of infection, default to same as primary |
ves_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against symptoms, default to same as primary |
vea_p |
scalar indicating efficacy of partial vaccination against acquisition (between 0-1) |
vei_p |
scalar indicating efficacy of partial vaccination against infectiousness (between 0-1) |
ved_p |
scalar indicating efficacy of partial vaccination against duration (between 0-1) |
ves_p |
scalar indicating efficacy of partial vaccination against symptoms (between 0-1) |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
r1 |
scalar or numeric vector with same length as 'gono_params' giving proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
scalar or numeric vector with same length as 'gono_params' giving proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r2_p |
scalar or numeric vector with same length as 'gono_params' giving proportion of partially vaccinated individuals who later receive a second dose when returning to the clinic due to screening or illness |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned. Defaults to supplied value of r1 * r2 |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
t_stop |
time at which vaccination should stop (years) |
hes |
Proportion of individuals in the population who are vaccine hesitant |
n_erlang |
integer giving the number of erlang vaccination transitions through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
A list of transformed model outputs
run model with a two-dose vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are eligible for revaccination (R), and return to the R stratum, those with waned partial vaccines return to the unvaccinated stratum (X) and considered immunologically naive. A user defined proportion of the population is vaccine hesitant and is never vaccinated. Full acciantion (V) with 2 doses gives maximum protection whereas partial vaccination with 1 dose (P) gives less. Individuals can get 2 doses either by committing in X or upon visiting a clinic in P.
run_onevax_xpvwrh_trackvt( tt, gono_params, init_params = NULL, dur_va = 1000, dur_vb = 1, dur_p = dur_va, vea = 0, vei = 0, ved = 0, ves = 0, dur_revaxa = dur_va, dur_revaxb = 1, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, vbe = 0, r1 = 0, r2 = 0, r1_p = 0, r2_p = 0, booster_uptake = (r1 * r2), strategy = NULL, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )run_onevax_xpvwrh_trackvt( tt, gono_params, init_params = NULL, dur_va = 1000, dur_vb = 1, dur_p = dur_va, vea = 0, vei = 0, ved = 0, ves = 0, dur_revaxa = dur_va, dur_revaxb = 1, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, vbe = 0, r1 = 0, r2 = 0, r1_p = 0, r2_p = 0, booster_uptake = (r1 * r2), strategy = NULL, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur_va |
duration of time spent in Va stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_vb |
duration of time spent in Vb stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_p |
duration of time spent in the P stratum, partially vaccinated (accepting only the first dose) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
dur_revaxa |
duration of time spent in Ra stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_revaxb |
duration of time spent in Rb stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
vea_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against acquisition, default to same as primary |
vei_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against infectiousness, default to same as primary |
ved_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against duration of infection, default to same as primary |
ves_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against symptoms, default to same as primary |
vea_p |
scalar indicating efficacy of partial vaccination against acquisition (between 0-1) |
vei_p |
scalar indicating efficacy of partial vaccination against infectiousness (between 0-1) |
ved_p |
scalar indicating efficacy of partial vaccination against duration (between 0-1) |
ves_p |
scalar indicating efficacy of partial vaccination against symptoms (between 0-1) |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
r1 |
scalar or numeric vector with same length as 'gono_params' giving proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
scalar or numeric vector with same length as 'gono_params' giving proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r1_p |
proportion of partially vaccinated individuals who receive >= one dose when returning to the clinic due to screening or illness |
r2_p |
proportion of partially vaccinated individuals who receive two doses when returning to the clinic due to screening or illness |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned. Defaults to supplied value of r1 * r2 |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
t_stop |
time at which vaccination should stop (years) |
hes |
Proportion of individuals in the population who are vaccine hesitant |
n_erlang |
integer giving the number of erlang vaccination transitions through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
A list of transformed model outputs
Run model with single vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are not eligible for revaccination.
run_onevax_xvw( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, vbe = coverage, n_diag_rec = 1, uptake = 0, strategy = NULL, coverage = 0, t_stop = 99 )run_onevax_xvw( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, vbe = coverage, n_diag_rec = 1, uptake = 0, strategy = NULL, coverage = 0, t_stop = 99 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur |
scalar or numeric vector with same length as 'gono_params' giving duration of the vaccine (in years) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
vbe |
scalar giving uptake of vaccination before entry into population (i.e. adolescent vaccination) defaults to same as @param n_diag_rec integer for the number of diagnosis history substrata 'coverage' |
n_diag_rec |
integer for the number of diagnosis history substrata |
uptake |
scalar or numeric vector with same length as 'gono_params' giving pc of population vaccinated as part of strategy |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
coverage |
scalar giving initial coverage of vaccination, default 0. |
t_stop |
time at which vaccination should stop (years) |
Run model with single vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are not eligible for re-vaccination.
run_onevax_xvw_trial( tt, gono_params, initial_params_trial = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, p_v = 0.5, n_erlang = 1, stochastic = FALSE, N = 6e+05, n_diag_rec = 1, asymp_recorded = TRUE )run_onevax_xvw_trial( tt, gono_params, initial_params_trial = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, p_v = 0.5, n_erlang = 1, stochastic = FALSE, N = 6e+05, n_diag_rec = 1, asymp_recorded = TRUE )
tt |
a numeric vector of times at which the model state is output in years |
gono_params |
list of gono params for a vaccination trial |
initial_params_trial |
list of initial conditions for model trial. Set default as NULL. |
dur |
scalar or numeric vector with same length as 'gono_params' giving duration of the vaccine (in years) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
p_v |
scalar giving proportion of the trial cohort vaccinated, default is 0.5. |
n_erlang |
integer giving the number of transitions that need to be made |
stochastic |
logical indicating if the run should be made with the default deterministic trial model in continuous time or stochastic trial model in discrete time |
N |
integer to assign the total number of individuals in the trial (split equally across the two arms) |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
asymp_recorded |
logical indicating if the trial screens for and records asymptomatic diagnosis. If FALSE, asymptomatic infected individuals undergoing treatment do not move diagnosis history stratum |
run model with single vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are eligible for revaccination (R), and return to the R stratum
run_onevax_xvwr( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vbe = 0, n_diag_rec = 1, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, t_stop = 99 )run_onevax_xvwr( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vbe = 0, n_diag_rec = 1, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, t_stop = 99 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur |
scalar or numeric vector with same length as 'gono_params' giving duration of the vaccine (in years) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
dur_revax |
scalar or numeric vector with same length as 'gono_params' giving duration of protection for revaccination, default to same as primary |
vea_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against acquisition, default to same as primary |
vei_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against infectiousness, default to same as primary |
ved_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against duration of infection, default to same as primary |
ves_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against symptoms, default to same as primary |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
n_diag_rec |
integer for the number of diagnosis history substrata |
primary_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking primary vaccination as part of strategy |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned. Defaults to supplied value of 'primary_uptake'. @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
t_stop |
time at which vaccination should stop (years) |
A list of transformed model outputs
run model with single vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are eligible for revaccination (R), and return to the R stratum
run_onevax_xvwrh( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vbe = 0, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, t_stop = 99, hes = 0, n_diag_rec = 1 )run_onevax_xvwrh( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, dur_revax = dur, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vbe = 0, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, t_stop = 99, hes = 0, n_diag_rec = 1 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur |
scalar or numeric vector with same length as 'gono_params' giving duration of the vaccine (in years) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
dur_revax |
scalar or numeric vector with same length as 'gono_params' giving duration of protection for revaccination, default to same as primary |
vea_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against acquisition, default to same as primary |
vei_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against infectiousness, default to same as primary |
ved_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against duration of infection, default to same as primary |
ves_revax |
scalar or numeric vector with same length as 'gono_params' giving efficacy of revaccination against symptoms, default to same as primary |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
primary_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking primary vaccination as part of strategy |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned. Defaults to supplied value of 'primary_uptake'. |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
t_stop |
time at which vaccination should stop (years) |
hes |
Proportion of individuals in the population who are vaccine hesitant |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list of transformed model outputs
run model with single vaccine for input parameter sets, either from initialisation or from equilibrium, those with waned vaccines are eligible for revaccination, and return to the V compartment
run_onevax_xvwv( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, vbe = 0, n_diag_rec = 1, uptake = 0, strategy = NULL, t_stop = 99 )run_onevax_xvwv( tt, gono_params, init_params = NULL, dur = 1000, vea = 0, vei = 0, ved = 0, ves = 0, vbe = 0, n_diag_rec = 1, uptake = 0, strategy = NULL, t_stop = 99 )
tt |
a numeric vector of times at which the model state is output |
gono_params |
list of gono params |
init_params |
A list of starting conditions |
dur |
scalar or numeric vector with same length as 'gono_params' giving duration of the vaccine (in years) |
vea |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against duration (between 0-1) |
ves |
scalar or numeric vector with same length as 'gono_params' giving efficacy of the vaccine against symptoms (between 0-1) |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
n_diag_rec |
integer for the number of diagnosis history substrata |
uptake |
scalar or numeric vector with same length as 'gono_params' giving pc of population vaccinated as part of strategy @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
t_stop |
time at which vaccination should stop (years) |
Run odin model of gonorrhoea vaccine trial with or without vaccination
run_trial( tt, gono_params, init_params = NULL, vax_params = NULL, transform = TRUE, stochastic = FALSE, N = 6e+05 )run_trial( tt, gono_params, init_params = NULL, vax_params = NULL, transform = TRUE, stochastic = FALSE, N = 6e+05 )
tt |
a numeric vector of times at which the model state is output in years |
gono_params |
a data frame of parameters |
init_params |
= NULL |
vax_params |
= NULL |
transform |
= TRUE |
stochastic |
logical indicating if the run should be made with the default deterministic trial model in continuous time or stochastic trial model in discrete time |
N |
integer to assign the total number of individuals in the trial (split equally across the two arms) |
Run odin model of gonorrhoea with or without vaccination
run_xpvwrh( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1, transform = TRUE )run_xpvwrh( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1, transform = TRUE )
tt |
a numeric vector of times at which the model state is output |
gono_params |
a data frame of parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
transform |
= TRUE |
Run odin model of gonorrhoea with or without vaccination
run_xpvwrh_trackvt( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1, transform = TRUE )run_xpvwrh_trackvt( tt, gono_params, demographic_params = NULL, init_params = NULL, vax_params = NULL, n_erlang = 1, n_diag_rec = 1, years_history = 1, transform = TRUE )
tt |
a numeric vector of times at which the model state is output |
gono_params |
a data frame of parameters |
demographic_params |
A dataframe of demographic parameters |
init_params |
A list of starting conditions |
vax_params |
A vector of vaccination params |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
years_history |
number of years that diagnosis history is recorded for |
transform |
= TRUE |
generates vector which tells the model which strata are under vaccine protection and what the value of protection for that strata is
set_protection(i_v, idx, n_vax, ve_p, ve, ve_revax)set_protection(i_v, idx, n_vax, ve_p, ve, ve_revax)
i_v |
indices of strata receiving protection through vaccination |
idx |
list containing indices of all X, P, V, W, R & H strata and n_vax through vaccine-protected strata until that protection has waned through vaccine-protected strata until that protection has waned |
n_vax |
integer denoting total number of strata |
ve_p |
scalar 0-1 with degree of partial primary protection of the P(N) strata, can take vea_p, vei_p, ves_p, ved_p |
ve |
scalar 0-1 with degree of full primary protection of the V(N) strata, can take vea, vei, ves, ved |
ve_revax |
scalar 0-1 with degree of re-vaccinated protection of the R(N) strata, can take vea_revax, vei_revax, ves_revax, ved_revax |
vector of length n_vax with zeros corresponding to the indices of strata with no protection, and the supplied degree of partial, full, and boosted protection corresponding to the indices of strata with partial, full and boosted vaccination status
generates vector which tells the model which strata are under vaccine protection and what the value of protection for that strata is
set_protection_trackvt(i_v, idx, n_vax, ve_p, ve, ve_revax)set_protection_trackvt(i_v, idx, n_vax, ve_p, ve, ve_revax)
i_v |
indices of strata receiving protection through vaccination |
idx |
list containing indices of all X, P, V, W, R & H strata and n_vax through vaccine-protected strata until that protection has waned through vaccine-protected strata until that protection has waned |
n_vax |
integer denoting total number of strata |
ve_p |
scalar 0-1 with degree of partial primary protection of the P(N) strata, can take vea_p, vei_p, ves_p, ved_p |
ve |
scalar 0-1 with degree of full primary protection of the V(N) strata, can take vea, vei, ves, ved |
ve_revax |
scalar 0-1 with degree of re-vaccinated protection of the R(N) strata, can take vea_revax, vei_revax, ves_revax, ved_revax |
vector of length n_vax with zeros corresponding to the indices of strata with no protection, and the supplied degree of partial, full, and boosted protection corresponding to the indices of strata with partial, full and boosted vaccination status
Translate each named vaccine strategy into a format interpretable by 'create_vax_map'
set_strategy(strategy = NULL, include_vbe = FALSE)set_strategy(strategy = NULL, include_vbe = FALSE)
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)", "VoS" describing who is offered vaccination. defaults to no vaccination. |
include_vbe |
single logical indicating whether vaccination before entry is offered. Defaults to FALSE. |
list with entries 'vod', 'vos' and 'vbe' containing 0-1 vectors of length two indicating whether group L and/or H are offered vaccination via each potential vaccination route (i.e. screeing, diagnosis, entry)
Generate the indices of all xpvwrh strata
stratum_index_xpvwrh(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xpvwrh(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Generate the indices of all xpvwrh strata trcaking time since vacc
stratum_index_xpvwrh_trackvt(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xpvwrh_trackvt(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Generate the indices of all xvwv trial strata
stratum_index_xvw(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xvw(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Generate the indices of all xvw trial strata
stratum_index_xvw_trial(n_erlang, n_diag_rec = 1)stratum_index_xvw_trial(n_erlang, n_diag_rec = 1)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
A list of strata with their indices
Generate the indices of all xvwr trial strata
stratum_index_xvwr(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xvwr(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Generate the indices of all xvwrh trial strata
stratum_index_xvwrh(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xvwrh(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Generate the indices of all xvwv trial strata
stratum_index_xvwv(n_erlang = 1, n_diag_rec = 1, strategy = NULL)stratum_index_xvwv(n_erlang = 1, n_diag_rec = 1, strategy = NULL)
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
integer for the number of diagnosis history substrata |
strategy |
string of vaccination strategy being considered |
A list of strata with their indicies
Transform fitted parameters into gonovax params
transform(pars, fix_par_t = TRUE)transform(pars, fix_par_t = TRUE)
pars |
list of fitted parameters |
fix_par_t |
logical indicating whether parameters with inferred trends are held at their 2020 values after that date. |
A list of parameters for use in the model
Transform fitted parameters into non-time-varying gonovax params
transform_fixed(pars)transform_fixed(pars)
pars |
list of fitted parameters |
A list of parameters for use in the model
create vaccination parameters for use in onevax_xpvwrh model
vax_params_xpvwrh( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, dur_v = 1000, dur_p = dur_v, dur_revax = dur_v, r1 = 0, r2 = 0, r2_p = 0, booster_uptake = r1 * r2, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )vax_params_xpvwrh( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, dur_v = 1000, dur_p = dur_v, dur_revax = dur_v, r1 = 0, r2 = 0, r2_p = 0, booster_uptake = r1 * r2, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
vea_revax |
scalar indicating efficacy of revaccination against acquisition (between 0-1) |
vei_revax |
scalar indicating efficacy of revaccination against infectiousness (between 0-1) |
ved_revax |
scalar indicating efficacy of revaccination against duration of infection (between 0-1) |
ves_revax |
scalar indicating efficacy of revaccination against symptoms (between 0-1) |
vea_p |
scalar indicating efficacy of partial vaccination against acquisition (between 0-1) |
vei_p |
scalar indicating efficacy of partial vaccination against infectiousness (between 0-1) |
ved_p |
scalar indicating efficacy of partial vaccination against duration (between 0-1) |
ves_p |
scalar indicating efficacy of partial vaccination against symptoms (between 0-1) |
dur_v |
duration of time spent in V stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_p |
duration of time spent in the P stratum, partially vaccinated (accepting only the first dose) |
dur_revax |
duration of protection for revaccination, default to same as primary |
r1 |
proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r2_p |
proportion of partially vaccinated individuals who receive a second dose when returning to the clinic due to screening or illness |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
hes |
proportion of population vaccine hesitant |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
number of diagnosis history strata |
years_history |
number of years that diagnosis history is recorded for |
A list parameters in the model input format
create vaccination parameters for use in onevax_xpvwrh model
vax_params_xpvwrh_trackvt( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, dur_va = 1000, dur_vb = 1, dur_p = dur_va, dur_revaxa = dur_va, dur_revaxb = 1, r1 = 0, r2 = 0, r1_p = 0, r2_p = 0, booster_uptake = r1 * r2, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )vax_params_xpvwrh_trackvt( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, vea_p = vea, vei_p = vei, ved_p = ved, ves_p = ves, dur_va = 1000, dur_vb = 1, dur_p = dur_va, dur_revaxa = dur_va, dur_revaxb = 1, r1 = 0, r2 = 0, r1_p = 0, r2_p = 0, booster_uptake = r1 * r2, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_erlang = 1, n_diag_rec = 1, years_history = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
vea_revax |
scalar indicating efficacy of revaccination against acquisition (between 0-1) |
vei_revax |
scalar indicating efficacy of revaccination against infectiousness (between 0-1) |
ved_revax |
scalar indicating efficacy of revaccination against duration of infection (between 0-1) |
ves_revax |
scalar indicating efficacy of revaccination against symptoms (between 0-1) |
vea_p |
scalar indicating efficacy of partial vaccination against acquisition (between 0-1) |
vei_p |
scalar indicating efficacy of partial vaccination against infectiousness (between 0-1) |
ved_p |
scalar indicating efficacy of partial vaccination against duration (between 0-1) |
ves_p |
scalar indicating efficacy of partial vaccination against symptoms (between 0-1) |
dur_va |
duration of time spent in Va stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_vb |
duration of time spent in Vb stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_p |
duration of time spent in the P stratum, partially vaccinated (accepting only the first dose) |
dur_revaxa |
duration of time spent in Ra stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
dur_revaxb |
duration of time spent in Rb stratum after completing a round of primary vaccination (fully vaccinated, accepting first and second dose) |
r1 |
proportion of population offered vaccine only accepting the first dose, becoming partially vaccinated |
r2 |
proportion of the population who accepted the first dose of the vaccine who go on to accept the second dose, becoming fully vaccinated |
r1_p |
proportion of partially vaccinated individuals who receive >= one dose when returning to the clinic due to screening or illness |
r2_p |
proportion of partially vaccinated individuals who receive two doses when returning to the clinic due to screening or illness |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
hes |
proportion of population vaccine hesitant |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
n_diag_rec |
number of diagnosis history strata |
years_history |
number of years that diagnosis history is recorded for |
A list parameters in the model input format
Create vaccination parameters for use in onevax_xvw model
vax_params_xvw( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, uptake = 0, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )vax_params_xvw( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, uptake = 0, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
dur |
scalar indicating duration of the vaccine (in years) |
uptake |
scalar indicating pc of those offered who accept vaccination |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list parameters in the model input format
Create vaccination parameters for use in onevax_xvw_trial model, assign who experiences vaccine effects, and how waning occurs.
vax_params_xvw_trial( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, n_erlang = 1, stochastic = FALSE, n_diag_rec = 1, asymp_recorded = TRUE )vax_params_xvw_trial( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, n_erlang = 1, stochastic = FALSE, n_diag_rec = 1, asymp_recorded = TRUE )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
dur |
scalar indicating duration of the vaccine (in years) |
n_erlang |
integer giving the number of transitions that need to be made through vaccine-protected strata until that protection has waned |
stochastic |
logical indicating if the parameters are for the default deterministic trial model in continuous time or stochastic trial model in discrete time |
n_diag_rec |
integer giving the number of each X, V(erlang), and W stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
asymp_recorded |
logical indicating if the trial screens for and records asymptomatic diagnosis. If FALSE, asymptomatic infected individuals undergoing treatment do not move diagnosis history stratum |
A list of parameters in the model input format
create vaccination parameters for use in onevax_xvwr model
vax_params_xvwr( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, dur = 1000, dur_revax = dur, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )vax_params_xvwr( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, dur = 1000, dur_revax = dur, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
vea_revax |
scalar indicating efficacy of revaccination against acquisition (between 0-1) |
vei_revax |
scalar indicating efficacy of revaccination against infectiousness (between 0-1) |
ved_revax |
scalar indicating efficacy of revaccination against duration of infection (between 0-1) |
ves_revax |
scalar indicating efficacy of revaccination against symptoms (between 0-1) |
dur |
scalar indicating duration of the vaccine (in years) |
dur_revax |
duration of protection for revaccination, default to same as primary |
primary_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking primary vaccination as part of strategy |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list parameters in the model input format
create vaccination parameters for use in onevax_xvwrh model
vax_params_xvwrh( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, dur = 1000, dur_revax = dur, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_diag_rec = 1 )vax_params_xvwrh( vea = 0, vei = 0, ved = 0, ves = 0, vea_revax = vea, vei_revax = vei, ved_revax = ved, ves_revax = ves, dur = 1000, dur_revax = dur, primary_uptake = 0, booster_uptake = primary_uptake, strategy = NULL, vbe = 0, t_stop = 99, hes = 0, n_diag_rec = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
vea_revax |
scalar indicating efficacy of revaccination against acquisition (between 0-1) |
vei_revax |
scalar indicating efficacy of revaccination against infectiousness (between 0-1) |
ved_revax |
scalar indicating efficacy of revaccination against duration of infection (between 0-1) |
ves_revax |
scalar indicating efficacy of revaccination against symptoms (between 0-1) |
dur |
scalar indicating duration of the vaccine (in years) |
dur_revax |
duration of protection for revaccination, default to same as primary |
primary_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking primary vaccination as part of strategy |
booster_uptake |
scalar or numeric vector with same length as 'gono_params' giving proportion of population undertaking booster vaccination after primary vaccination protection has waned @param n_diag_rec integer for the number of diagnosis history substrata |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
hes |
proportion of population vaccine hesitant |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list parameters in the model input format
create vaccination parameters for use in onevax_xvwv model
vax_params_xvwv( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, uptake = 0, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )vax_params_xvwv( vea = 0, vei = 0, ved = 0, ves = 0, dur = 1000, uptake = 0, strategy = NULL, vbe = 0, t_stop = 99, n_diag_rec = 1 )
vea |
scalar indicating efficacy of the vaccine against acquisition (between 0-1) |
vei |
scalar indicating efficacy of the vaccine against infectiousness (between 0-1) |
ved |
scalar indicating efficacy of the vaccine against duration (between 0-1) |
ves |
scalar indicating efficacy of the vaccine against symptoms (between 0-1) |
dur |
scalar indicating duration of the vaccine (in years) |
uptake |
scalar indicating pc of those offered who accept vaccination |
strategy |
single character string in "VoD", "VoD(H)", "VoA", "VoA(H)", "VoD(L)+VoA(H)". Defaults to NULL i.e. no vaccination |
vbe |
scalar indicating pc of population vaccinated before entry (between 0-1) |
t_stop |
time at which vaccination should stop (years) |
n_diag_rec |
integer for the number of diagnosis history substrata |
A list parameters in the model input format
create vaccination parameters for use in novax model (null)
vax_params0(n_diag_rec = 1, years_history = 1)vax_params0(n_diag_rec = 1, years_history = 1)
n_diag_rec |
integer giving the number of each X, V(erlang), and W |
years_history |
number of years that diagnosis history is recorded for stratum, allowing tracking of diagnosis history. e.g for a n_diag_rec = 2 and erlang = 1, there will be X.I, X.II, V1.I, V1.II, W.I, W.II strata. Where '.I' corresponds to never-diagnosed individuals and '.II' is for individuals diagnosed at least once. |
A list parameters in the model input format