Package 'squire'

Duration of mild infectiousness (days)

Delay between symptom onset and requiring hospitalisation (days)

Probability of hospitilisation by ages

Mixing matrix

Number of observed deaths

Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by deaths

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Contact matrices used in simulation. Default = NULL, which will generate this based on the country.

Age groups for seeding

Minimum seeding cases

Maximum seeding cases

R0 to be passed to run_explicit_SEEIR_model. Default = 3

Vector or R0 values to sample from to introduce uncertainty in predictions. Default = NULL, which will not scan. If provided, the first value in R0 will be drawn from R0_scan

Replicates to be passed to run_explicit_SEEIR_model. Default = 100

Further aguments for run_explicit_SEEIR_model()

A squire model. Default = explicit_SEEIR()

Squire model parameters, from a call to generate_parameters().

Basic reproduction number

Duration of mild infectiousness (days)

Delay between symptom onset and requiring hospitalisation (days)

Probability of hospitilisation by ages

Mixing matrix

Basic reproduction number

Duration of infectiousness (days)

Mixing matrix

Basic reproduction number

Deaths data to fit to. See example_deaths.csv and particle_filter_data()

Minimum value of R0 in the search

Maximum value of R0 in the search

Step to increment R0 between min and max

Prior for R0. Default = NULL, which is a flat prior. Should be provided as a list with first argument the distribution function and the second the function arguments (excluding quantiles which are worked out based on R0_min and R0_max), e.g. 'list("func" = dnorm, args = list("mean"= 3.5, "sd"= 3))'.

Earliest start date as 'yyyy-mm-dd'

Latest start date as 'yyyy-mm-dd'

Step to increment date in days

Minimum value of Meff (Movement effect size) in the search. Default = 1, which is the same as Meff_max. If Meff_max and Meff_min are the same then only a 2d grid search is performed based on R0 and start_date

Maximum value of Meff (Movement effect size) in the search. Default = 1, which is the same as Meff_min. If Meff_max and Meff_min are the same then only a 2d grid search is performed based on R0 and start_date

Step to increment Meff (Movement effect size) between min and max. Default = 0.1

A squire model. Default = explicit_SEIR()

Function for converting R0, Meff and R0_change. Function must have names arguments of R0, Meff and R0_change. Default is linear relationship on the log scale given by exp(log(R0) - Meff*(1-R0_change)).

list of parameters to use for the comparison function.

Number of days to forecast forward. Default = 0

Number of particles. Positive Integer. Default = 100

Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by data$deaths

Boolean for whether likelihood is based only on deaths that occur from healthcare systems, i.e. are treated. Default = FALSE, which uses all deaths.

Replicates to be run. Default = 100

Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0

Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0

Calendar dates at which ICU bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

The starting number of ICU beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 3/100 of hospital beds

Number of ICU beds at each date specified in date_ICU_bed_capacity_change. Must be same length as date_ICU_bed_capacity_change.

Calendar dates at which hospital bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

The starting number of hospital beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 5/1000 of the population

Number of hospital beds at each date specified in date_hosp_bed_capacity_change. Must be same length as date_hosp_bed_capacity_change.

Calendar dates at which the contact matrices set in model_params change. Defaut = NULL, i.e. no change

The starting contact matrix prior to any changes due to interventions or otherwise. Default = NULL, which will use the contact matrix associated with the coutnry provided.

List of contact matrices to be used from the dates provided in date_contact_matrix_set_change.Must be same length as date_contact_matrix_set_change

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Time Step. Default = 0.1

Further aguments for the model parameter function. If using the explicit_model (default) this will be parameters_explicit_SEEIR.

an onject

Time change points

Length of simulation

An odin_model object

Parameters for the observations

The data to be compared against

The class of the model. At the moment this can only be explicit_SEIR but as more models come online we can use this parameter to control the type of comparison function generated.

Simulation replicate number

Output of pmcmc or calibrate

List of time steps to be run for each replicate

Numeric vector for R0 from t = 0 in the calibration. E.g. R0 = c(2, 1). Default = NULL, which will use R0_change to alter R0 if provided.

Numeric vector for relative changes in R0 relative to the final R0 used in the calibration (i.e. at t = 0 in the calibration) E.g. R0 = c(0.8, 0.5). Default = NULL, which will use R0 to parameterise changes in R0 if provided.

Change time points for R0

Contact matrices used in simulation. Default = NULL, which will use contact_matrix_set_change to alter the contact matrix if provided.

Numeric vector for relative changes in the contact matrix realtive to the final contact matrix used in the calibration (i.e. at t = 0 in the calibration). E.g. contact_matrix_set_change = c(0.8, 0.5). Default = NULL, which will use contact_matrix_set to parameterise changes in contact matrices if if provided.

Time change points for matrix change. Default = 0

Numeric vector for hospital bed capacity from t = 0 in the calibration. Default = NULL, which will use hosp_bed_capacity_change to alter hosp_bed_capacity if provided.

Numeric vector for relative changes in hospital bed capacity relative to the final hospital bed capacity used in the calibration (i.e. at t = 0 in the calibration). E.g. hosp_bed_capacity_change = c(0.8, 0.5). Default = NULL, which will use hosp_bed_capacity to parameterise changes in hospital bed capacity if provided.

Change time points for hosp_bed_capacity

Numeric vector for ICU bed capacity from t = 0 in the calibration. Default = NULL, which will use ICU_bed_capacity_change to alter ICU_bed_capacity if provided.

Numeric vector for relative changes in ICU bed capacity relative to the final ICU bed capacity used in the calibration (i.e. at t = 0 in the calibration). E.g. ICU_bed_capacity_change = c(0.8, 0.5). Default = NULL, which will use ICU_bed_capacity to parameterise changes in ICU bed capacity if provided.

Change time points for ICU_bed_capacity

List of other parameters to be passed to the model to be run. Default = NULL. An example would be:

list( list( "prob_severe" = runif(17), "tt_dur_get_ox_survive" = c(0, 10), "gamma_get_ox_survive" = 0.2), list( "prob_severe" = runif(17), "tt_dur_get_mv_survive" = c(0, 5), "gamma_get_mv_survive" = 0.1) )

The list should be the same length as the number of replicates in the simulations. Each list element should then be a list with elements named to match the arguments expected by the odin model with r. Above would be suitable to set the model parameters for a simulation with 2 replicates. You do not have to have the same arguments in each list.

a pmcmc_list object

an integer denoting the number of samples to discard from each chain

Matrix

Population vector

Data to fit to. This must be constructed with particle_filter_data

number of mcmc mcmc iterations to perform

function to calculate log likelihood, must take named parameter vector as input, allow passing of implicit arguments corresponding to the main function arguments. Returns a named list, with entries: - $log_likelihood, a single numeric - $sample_state, a numeric vector corresponding to the state of a single particle, chosen at random, at the final time point for which we have data. If NULL, calculated using the function calc_loglikelihood.

function to calculate log prior, must take named parameter vector as input, returns a single numeric. If NULL, uses uninformative priors which do not affect the posterior

Number of particles (considered for both the PMCMC fit and sampling from posterior)

Number of steps per day

Logical indicating whether proposed parameter jumps should be output along with results

number of MCMC chains to run

A squire model to use

list of parameters to use in comparison with compare. Must be a list containing, e.g. list(phi_cases = 0.1, k_cases = 2, phi_death = 1, k_death = 2, exp_noise = 1e6)

named list of initial inputs for parameters being sampled

named list of lower reflecting boundaries for parameter proposals

named list of upper reflecting boundaries for parameter proposals

named list of logicals, indicating if proposed jump should be discrete

Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by data$deaths

Boolean for whether likelihood is based only on deaths that occur from healthcare systems, i.e. are treated. Default = FALSE, which uses all deaths.

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

List of contact matrices to be used from the dates provided in date_contact_matrix_set_change.Must be same length as date_contact_matrix_set_change

The starting contact matrix prior to any changes due to interventions or otherwise. Default = NULL, which will use the contact matrix associated with the coutnry provided.

Calendar dates at which the contact matrices set in model_params change. Defaut = NULL, i.e. no change

Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0

Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0

Number of hospital beds at each date specified in date_hosp_bed_capacity_change. Must be same length as date_hosp_bed_capacity_change.

The starting number of hospital beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 5/1000 of the population

Calendar dates at which hospital bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Number of ICU beds at each date specified in date_ICU_bed_capacity_change. Must be same length as date_ICU_bed_capacity_change.

The starting number of ICU beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 3/100 of hospital beds

Calendar dates at which ICU bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Date that vaccine doses per day change. Default = NULL.

Baseline vaccine doses per day. Default = NULL

Time varying maximum vaccine doeses per day. Default = NULL.

Date that vaccine efficacy against infection changes. Default = NULL.

Baseline vaccine effacy against infection. Default = NULL

Time varying vaccine efficacy against infection. Default = NULL.

Date that vaccine efficacy against disease changes. Default = NULL.

Baseline vaccine efficacy against disease Default = NULL

Time varying vaccine efficacy against infection. Default = NULL.

List of arguments to be passed to evaluate_Rt_pmcmc for calculating Rt. Current arguments are available in Rt_args_list

number of iterations to discard from the start of MCMC run when sampling from the posterior for trajectories

number of trajectories (replicates) to be returned that are being sampled from the posterior probability results produced by run_mcmc_chain to select parameter set. For each parmater set sampled, run particle filter with n_particles and sample 1 trajectory

Number of days to forecast forward. Default = 0

List of arguments to pass to [[drjacoby::run_mcmc]] that are not data, df_params, misc, loglike, logprior, burnin or samples

Further aguments for the model parameter function. If using the explicit_model (default) this will be parameters_explicit_SEEIR.

squire_simulation object

Collapse age-dimension

Date of time 0, if specified a date column will be added

squire_simulation object

Collapse age-dimension

Date of time 0, if specified a date column will be added

squire_simulation object

Collapse age-dimension

Date of time 0, if specified a date column will be added

squire_simulation object

Collapse age-dimension

Date of time 0, if specified a date column will be added

squire_simulation object

squire_simulation object

Vector of compartment names, e.g. c("S", "R"). In addition a number of summary compartment can be requested. These include:

• "deaths" Daily Deaths

• "infections" Daily Infections

• "hospital_occupancy" Occupied Hospital Beds

• "ICU_occupancy" Occupied ICU Beds

• "hospital_demand Required Hospital Beds

• "ICU_demand Required ICU Beds

Collapse age-dimension, calculating the total in the compartment.

Collapse compartments of same type together (e.g. E1 and E2 -> E)

Date of time 0, if specified a date column will be added

squire_simulation object

Vector of compartment names, e.g. c("S", "R")

Collapse age-dimension

Collapse compartments of same type together (e.g. E1 and E2 -> E)

Date of time 0, if specified a date column will be added

Country name

ISO 3C Country Code

Logical. Is the population for the simple_SEIR. Default = FALSE

Country name

Logical. Is the population for the simple_SEIR. Default = FALSE

Country name

ISO 3C Country Code

Country name

ISO 3C Country Code

Logical. Is the population for the simple_SEIR. Default = FALSE

Data.frame of initial conditions

Population vector (for each age group)

Data.frame of initial conditions. Default = NULL

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Initial number of cases seeding the epidemic

Dates (or ISO-formatted strings for conversion with as.Date at which intervention changes.

Variable that is changing at each of dates.

The date to start the simulation from..

The number of steps per day

The first value to use for change in the case that all provided dates are after start_date

whether or not the most recent parameter proposal was accepted

the iteration number

the current scaling factor

running average of the MCMC parameters

current parameters

current covariance matrix

required acceptance ratio

Population vector (for each age group)

Contact matrices used in simulation

Set of contact matrices

Vector of populaion by age

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Time change points for matrix change. Default = 0

Contact matrices used in simulation. Default = NULL, which will generate this based on the country.

Basic Reproduction Number. Default = 3

Change time points for R0. Default = 0

Alternative parameterisation via beta rather than R0. Default = NULL, which causes beta to be estimated from R0

Length of simulation. Default = 365

Time Step. Default = 0.1

Data.frame of initial conditions. Default = NULL

Initial number of cases seeding the epidemic

Probability of hospitalisation by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of developing severe symptoms by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of death from non severe treated infection. Default, NULL, will use c(0.181354223, 0.181354223, 0.181354223, 0.137454906, 0.121938236, 0.122775613, 0.136057441, 0.160922182, 0.196987378, 0.242011054, 0.289368845, 0.326537862, 0.337229819, 0.309082553, 0.243794865, 0.160480254, 0.057084366)

Probability of death in non severe hospital inections that aren't treated. Default, NULL, will use rep(0.5, 17)

Probability of death from severe infection that is treated. Default, NULL, will use c(0.226668959, 0.252420241, 0.281097009, 0.413005389, 0.518451493, 0.573413613, 0.576222065, 0.54253573, 0.493557696, 0.447376527, 0.416666608, 0.411186639, 0.443382594, 0.538718871, 0.570434076, 0.643352843, 0.992620047)

Probability of death from severe infection that is not treated. Default, NULL, will use rep(0.95, 17)

Preferentiality of age group receiving treatment relative to other age groups when demand exceeds healthcare capacity.

Boolean for using parameters in Walker et al. Default = FALSE, which uses parameter update as of November 2020. For full information see parameters vignette

Mean duration of incubation period (days). Default = 4.6

Mean duration of mild infection (days). Default = 2.1

Mean duration from symptom onset to hospitil admission (days). Default = 4.5

Mean duration of oxygen given survive. Default = 9

Times at which dur_get_ox_survive changes (Default = 0 = doesn't change)

Mean duration of oxygen given death. Default = 9

Times at which dur_get_ox_die changes (Default = 0 = doesn't change)

Mean duration without oxygen given survive. Default = 4.5

Mean duration without oxygen given death. Default = 4.5

Mean duration of ventilation given survive. Default = 14.8

Times at which dur_get_mv_survive changes (Default = 0 = doesn't change)

Mean duration of ventilation given death. Default = 11.1

Times at which dur_get_mv_die changes (Default = 0 = doesn't change)

Mean duration without ventilation given survive. Default = 7.4

Mean duration without ventilation given death. Default = 1

Duration of recovery after coming off ventilation. Default = 3

Mean duration of immunity (days). Default = Inf

General bed capacity. Can be single number or vector if capacity time-varies.

ICU bed capacity. Can be single number or vector if capacity time-varies.

Times at which hospital bed capacity changes (Default = 0 = doesn't change)

Times at which ICU bed capacity changes (Default = 0 = doesn't change)

Basic reproduction number

Change time points for R0

Time step

Data.frame of initial conditions

Mean duration of incubation period (days)

Mean duration of infectious period (days) in simple model

Logical, do we want to return outut after each day rather than each dt. Default = FALSE

Population vector (for each age group)

Contact matrices used in simulation

Time change points for matrix change

Length of simulation

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Probability of hospitalisation by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of developing severe symptoms by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of death from non severe treated infection. Default, NULL, will use c(0.181354223, 0.181354223, 0.181354223, 0.137454906, 0.121938236, 0.122775613, 0.136057441, 0.160922182, 0.196987378, 0.242011054, 0.289368845, 0.326537862, 0.337229819, 0.309082553, 0.243794865, 0.160480254, 0.057084366)

Probability of death from severe infection that is treated. Default, NULL, will use c(0.226668959, 0.252420241, 0.281097009, 0.413005389, 0.518451493, 0.573413613, 0.576222065, 0.54253573, 0.493557696, 0.447376527, 0.416666608, 0.411186639, 0.443382594, 0.538718871, 0.570434076, 0.643352843, 0.992620047)

Probability of death in non severe hospital inections that aren't treated. Default, NULL, will use rep(0.5, 17)

Probability of death from severe infection that is not treated. Default, NULL, will use rep(0.95, 17)

Boolean for using parameters in Walker et al. Default = FALSE, which uses parameter update as of November 2020. For full information see parameters vignette

Data to fit to. This must be constructed with particle_filter_data

An odin model, used to generate stochastic samples

A function to generate log-weights

Number of particles

Number of days to forecast forward from end states. Requires that save_particles is TRUE.

Logical, indicating if we save full particle histories (this is slower).

Logical, indicating whether the full model output, including the state and the declared outputs are returned. Deafult = FALSE

Logical, indicating whether we should save a single particle, chosen at random, at the final time point for which we have data

Logical, indicating whether we should save all particles at the final time point for which we have data

A data.frame of observed data. There must be a column date, containing dates (or ISO-formatted strings for conversion with as.Date.

The date to start the simulation from. Must be earlier than the first date in data.

The number of steps per day

An squire_scan object

additional arguments affecting the plot produced.

What scan outputs are we plotting of "likelihood" vs "probability"

Should the axes by plotted on log scale

Which dimensions of the scan to show. Default = c(1, 2)

An squire_simulation object

Vector of variable names to plot (default is all)

Plot replicates

Logical, add summary line

logical add confidence interval ribbon

Quantiles for upper and lower of interval ribbon

Function to summarise each compartment passed to the fun argument of stat_summary

X variable to use for plotting (default is "t", but can be set to, "date", if date_0 provided), which will cause the date to be plotted rather than time.

If the squire_simulation provided is the result of running the particle filter, do we want to just plot the fit. Default = FALSE

additional arguments affecting the plot produced.

Data to fit to. This must be constructed with particle_filter_data

number of mcmc mcmc iterations to perform

function to calculate log likelihood, must take named parameter vector as input, allow passing of implicit arguments corresponding to the main function arguments. Returns a named list, with entries: - $log_likelihood, a single numeric - $sample_state, a numeric vector corresponding to the state of a single particle, chosen at random, at the final time point for which we have data. If NULL, calculated using the function calc_loglikelihood.

function to calculate log prior, must take named parameter vector as input, returns a single numeric. If NULL, uses uninformative priors which do not affect the posterior

Number of particles (considered for both the PMCMC fit and sampling from posterior)

Number of steps per day

Logical indicating whether proposed parameter jumps should be output along with results

number of MCMC chains to run

A squire model to use

list of parameters to use in comparison with compare. Must be a list containing, e.g. list(phi_cases = 0.1, k_cases = 2, phi_death = 1, k_death = 2, exp_noise = 1e6)

named list of initial inputs for parameters being sampled

named list of lower reflecting boundaries for parameter proposals

named list of upper reflecting boundaries for parameter proposals

named list of logicals, indicating if proposed jump should be discrete

named matrix of proposal covariance for parameters

numeric for starting scaling factor for covariance matrix. Default = 1

Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by data$deaths

Boolean for whether likelihood is based only on deaths that occur from healthcare systems, i.e. are treated. Default = FALSE, which uses all deaths.

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

List of contact matrices to be used from the dates provided in date_contact_matrix_set_change.Must be same length as date_contact_matrix_set_change

The starting contact matrix prior to any changes due to interventions or otherwise. Default = NULL, which will use the contact matrix associated with the coutnry provided.

Calendar dates at which the contact matrices set in model_params change. Defaut = NULL, i.e. no change

Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0

Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0

Number of hospital beds at each date specified in date_hosp_bed_capacity_change. Must be same length as date_hosp_bed_capacity_change.

The starting number of hospital beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 5/1000 of the population

Calendar dates at which hospital bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Number of ICU beds at each date specified in date_ICU_bed_capacity_change. Must be same length as date_ICU_bed_capacity_change.

The starting number of ICU beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 3/100 of hospital beds

Calendar dates at which ICU bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Date that vaccine doses per day change. Default = NULL.

Baseline vaccine doses per day. Default = NULL

Time varying maximum vaccine doeses per day. Default = NULL.

Date that vaccine efficacy against infection changes. Default = NULL.

Baseline vaccine effacy against infection. Default = NULL

Time varying vaccine efficacy against infection. Default = NULL.

Date that vaccine efficacy against disease changes. Default = NULL.

Baseline vaccine efficacy against disease Default = NULL

Time varying vaccine efficacy against infection. Default = NULL.

List of arguments to be passed to evaluate_Rt_pmcmc for calculating Rt. Current arguments are available in Rt_args_list

number of iterations to discard from the start of MCMC run when sampling from the posterior for trajectories

number of trajectories (replicates) to be returned that are being sampled from the posterior probability results produced by run_mcmc_chain to select parameter set. For each parmater set sampled, run particle filter with n_particles and sample 1 trajectory

Number of days to forecast forward. Default = 0

Desired MCMC acceptance ratio

Iteration number to begin RM optimisation of scaling factor at

Whether or not to use the Gibbs Sampler for start_date

Number of days either side of the start_date parameter to evaluate likelihood at

Further aguments for the model parameter function. If using the explicit_model (default) this will be parameters_explicit_SEEIR.

argument

Name of argument

An iccm_simulation object

additional arguments affecting the summary produced.

A contact matrix

Vector of population by age

A contact matrix

Vector of population by age

List of different projection runs from projections

Character vector describing the different scenarios.

Logical. Should the parameters used for the projection runs be added to scenarios. Default = TRUE

Vector of variable names to plot (default is all)

Plot replicates

Logical, add summary line

logical add confidence interval ribbon

Quantiles for upper and lower of interval ribbon

Function to summarise each compartment passed to the fun argument of stat_summary

Date of time 0, if specified a date column will be added

X variable to use for plotting (default is "t", but can be set to, "date", if date_0 provided), which will cause the date to be plotted rather than time.

additional arguments passed to format_output

Calibrated {squire_simulation} object.

How many days is the projection. Deafult = NULL, which will carry the projection forward from t = 0 in the calibration (i.e. the number of days set in calibrate using forecast)

Numeric vector for R0 from t = 0 in the calibration. E.g. R0 = c(2, 1). Default = NULL, which will use R0_change to alter R0 if provided.

Numeric vector for relative changes in R0 relative to the final R0 used in the calibration (i.e. at t = 0 in the calibration) E.g. R0 = c(0.8, 0.5). Default = NULL, which will use R0 to parameterise changes in R0 if provided.

Change time points for R0

Contact matrices used in simulation. Default = NULL, which will use contact_matrix_set_change to alter the contact matrix if provided.

Numeric vector for relative changes in the contact matrix realtive to the final contact matrix used in the calibration (i.e. at t = 0 in the calibration). E.g. contact_matrix_set_change = c(0.8, 0.5). Default = NULL, which will use contact_matrix_set to parameterise changes in contact matrices if if provided.

Time change points for matrix change. Default = 0

Numeric vector for hospital bed capacity from t = 0 in the calibration. Default = NULL, which will use hosp_bed_capacity_change to alter hosp_bed_capacity if provided.

Numeric vector for relative changes in hospital bed capacity relative to the final hospital bed capacity used in the calibration (i.e. at t = 0 in the calibration). E.g. hosp_bed_capacity_change = c(0.8, 0.5). Default = NULL, which will use hosp_bed_capacity to parameterise changes in hospital bed capacity if provided.

Change time points for hosp_bed_capacity

Numeric vector for ICU bed capacity from t = 0 in the calibration. Default = NULL, which will use ICU_bed_capacity_change to alter ICU_bed_capacity if provided.

Numeric vector for relative changes in ICU bed capacity relative to the final ICU bed capacity used in the calibration (i.e. at t = 0 in the calibration). E.g. ICU_bed_capacity_change = c(0.8, 0.5). Default = NULL, which will use ICU_bed_capacity to parameterise changes in ICU bed capacity if provided.

Change time points for ICU_bed_capacity

List of logicals for whether each replicate should be run. Default = TRUE, which causes all replictes to be run.

List of other parameters to be passed to the model to be run. Default = NULL. An example would be:

list( list( "prob_severe" = runif(17), "tt_dur_get_ox_survive" = c(0, 10), "gamma_get_ox_survive" = 0.2), list( "prob_severe" = runif(17), "tt_dur_get_mv_survive" = c(0, 5), "gamma_get_mv_survive" = 0.1) )

The list should be the same length as the number of replicates in the simulations. Each list element should then be a list with elements named to match the arguments expected by the odin model with r. Above would be suitable to set the model parameters for a simulation with 2 replicates. You do not have to have the same arguments in each list.

to fit to.

A squire model to use

Squire model parameters. Created from a call to one of the parameters_<type>_model functions.

Date to run model simulations from

List of parameters used for comparing model to data

Days ahead to include in output

Whether to save full history. Default = FALSE

Set return depending on what is needed. 'full' and "sample" gives the entire output, 'll' gives the log-likelihood

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Time change points for matrix change. Default = 0

Contact matrices used in simulation. Default = NULL, which will generate this based on the country.

Basic Reproduction Number. Default = 3

Change time points for R0. Default = 0

Alternative parameterisation via beta rather than R0. Default = NULL, which causes beta to be estimated from R0

Length of simulation. Default = 365

Time Step. Default = 0.1

Logical, do we want to return outut after each day rather than each dt. Default = FALSE

Number of replicates

Data.frame of initial conditions. Default = NULL

Random Number Seed.

Probability of hospitalisation by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of developing severe symptoms by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of death from non severe treated infection. Default, NULL, will use c(0.181354223, 0.181354223, 0.181354223, 0.137454906, 0.121938236, 0.122775613, 0.136057441, 0.160922182, 0.196987378, 0.242011054, 0.289368845, 0.326537862, 0.337229819, 0.309082553, 0.243794865, 0.160480254, 0.057084366)

Probability of death in non severe hospital inections that aren't treated. Default, NULL, will use rep(0.5, 17)

Probability of death from severe infection that is treated. Default, NULL, will use c(0.226668959, 0.252420241, 0.281097009, 0.413005389, 0.518451493, 0.573413613, 0.576222065, 0.54253573, 0.493557696, 0.447376527, 0.416666608, 0.411186639, 0.443382594, 0.538718871, 0.570434076, 0.643352843, 0.992620047)

Probability of death from severe infection that is not treated. Default, NULL, will use rep(0.95, 17)

Preferentiality of age group receiving treatment relative to other age groups when demand exceeds healthcare capacity.

Boolean for using parameters in Walker et al. Default = FALSE, which uses parameter update as of November 2020. For full information see parameters vignette

Mean duration of incubation period (days). Default = 4.6

Mean duration of mild infection (days). Default = 2.1

Mean duration from symptom onset to hospitil admission (days). Default = 4.5

Mean duration of oxygen given survive. Default = 9

Times at which dur_get_ox_survive changes (Default = 0 = doesn't change)

Mean duration of oxygen given death. Default = 9

Times at which dur_get_ox_die changes (Default = 0 = doesn't change)

Mean duration without oxygen given survive. Default = 4.5

Mean duration without oxygen given death. Default = 4.5

Mean duration of ventilation given survive. Default = 14.8

Times at which dur_get_mv_survive changes (Default = 0 = doesn't change)

Mean duration of ventilation given death. Default = 11.1

Times at which dur_get_mv_die changes (Default = 0 = doesn't change)

Mean duration without ventilation given survive. Default = 7.4

Mean duration without ventilation given death. Default = 1

Duration of recovery after coming off ventilation. Default = 3

Mean duration of immunity (days). Default = Inf

General bed capacity. Can be single number or vector if capacity time-varies.

ICU bed capacity. Can be single number or vector if capacity time-varies.

Times at which hospital bed capacity changes (Default = 0 = doesn't change)

Times at which ICU bed capacity changes (Default = 0 = doesn't change)

Initial number of cases seeding the epidemic

An odin model generation function. Default: 'explicit_SEIR_deterministic'

Character for country beign simulated. WIll be used to generate population and contact_matrix_set if unprovided. Either country or population and contact_matrix_set must be provided.

Population vector (for each age group). Default = NULL, which will cause population to be sourced from country

Time change points for matrix change. Default = 0

Contact matrices used in simulation. Default = NULL, which will generate this based on the country.

Basic Reproduction Number. Default = 3

Change time points for R0. Default = 0

Alternative parameterisation via beta rather than R0. Default = NULL, which causes beta to be estimated from R0

Length of simulation. Default = 365

Time Step. Default = 0.1

Logical, do we want to return outut after each day rather than each dt. Default = FALSE

Number of replicates

Data.frame of initial conditions. Default = NULL

Random Number Seed.

Probability of hospitalisation by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of developing severe symptoms by age. Default, NULL, will use c(0.000840764, 0.001182411, 0.001662887, 0.002338607, 0.003288907, 0.004625365, 0.006504897, 0.009148183, 0.012865577, 0.018093546, 0.025445917, 0.035785947, 0.050327683, 0.0707785, 0.099539573, 0.1399878, 0.233470395)

Probability of death from non severe treated infection. Default, NULL, will use c(0.181354223, 0.181354223, 0.181354223, 0.137454906, 0.121938236, 0.122775613, 0.136057441, 0.160922182, 0.196987378, 0.242011054, 0.289368845, 0.326537862, 0.337229819, 0.309082553, 0.243794865, 0.160480254, 0.057084366)

Probability of death in non severe hospital inections that aren't treated. Default, NULL, will use rep(0.5, 17)

Probability of death from severe infection that is treated. Default, NULL, will use c(0.226668959, 0.252420241, 0.281097009, 0.413005389, 0.518451493, 0.573413613, 0.576222065, 0.54253573, 0.493557696, 0.447376527, 0.416666608, 0.411186639, 0.443382594, 0.538718871, 0.570434076, 0.643352843, 0.992620047)

Probability of death from severe infection that is not treated. Default, NULL, will use rep(0.95, 17)

Preferentiality of age group receiving treatment relative to other age groups when demand exceeds healthcare capacity.

Boolean for using parameters in Walker et al. Default = FALSE, which uses parameter update as of November 2020. For full information see parameters vignette

Mean duration of incubation period (days). Default = 4.6

Mean duration of mild infection (days). Default = 2.1

Mean duration from symptom onset to hospitil admission (days). Default = 4.5

Mean duration of oxygen given survive. Default = 9

Times at which dur_get_ox_survive changes (Default = 0 = doesn't change)

Mean duration of oxygen given death. Default = 9

Times at which dur_get_ox_die changes (Default = 0 = doesn't change)

Mean duration without oxygen given survive. Default = 4.5

Mean duration without oxygen given death. Default = 4.5

Mean duration of ventilation given survive. Default = 14.8

Times at which dur_get_mv_survive changes (Default = 0 = doesn't change)

Mean duration of ventilation given death. Default = 11.1

Times at which dur_get_mv_die changes (Default = 0 = doesn't change)

Mean duration without ventilation given survive. Default = 7.4

Mean duration without ventilation given death. Default = 1

Duration of recovery after coming off ventilation. Default = 3

Mean duration of immunity (days). Default = Inf

General bed capacity. Can be single number or vector if capacity time-varies.

ICU bed capacity. Can be single number or vector if capacity time-varies.

Times at which hospital bed capacity changes (Default = 0 = doesn't change)

Times at which ICU bed capacity changes (Default = 0 = doesn't change)

Initial number of cases seeding the epidemic

to fit to.

A squire model to use

Squire model parameters. Created from a call to one of the parameters_<type>_model functions.

Date to run model simulations from

List of parameters used for comparing model to data in the particle filter t

Number of particles

Days ahead to include in output

Whether to save trajectories

Logical, indicating whether the full model output, including the state and the declared outputs are returned. Deafult = FALSE

Set return depending on what is needed. 'full' gives the entire particle filter output, 'll' gives the log-likelihood, 'sample' gives a sampled particle's trace, 'single' gives the final state

Basic reproduction number

Change time points for R0

Time step

Data.frame of initial conditions

Mean duration of incubation period (days)

Mean duration of infectious period (days)

Logical, do we want to return outut after each day rather than each dt. Default = FALSE

Population vector (for each age group)

Contact matrices used in simulation

Time change points for matrix change

Length of simulation

Number of replicates

Output of scan_R0_date_Meff.

Number of parameter pairs to be sampled. This will determine how many trajectories are returned. Integer. Default = 10. This will determine how many trajectories are returned.

Number of particles. Positive Integer. Default = 100

Number of days being forecast. Default = 0

Logical, indicating whether the full model output, including the state and the declared outputs are returned. Deafult = FALSE

output of run_mcmc_chain; The results from the PMCMC run – can have mutliple chains.

integer; Number of iterations to discard from the start of MCMC run. Default = 0

number of chains that considered. Should inherent from pmcmc.

function to calculate log likelihood, must take named parameter vector as input, allow passing of implicit arguments corresponding to the main function arguments. Returns a named list, with entries: - $log_likelihood, a single numeric - $sample_state, a numeric vector corresponding to the state of a single particle, chosen at random, at the final time point for which we have data. If NULL, calculated using the function calc_loglikelihood.

interger; Number of trajectories to be returned. Integer. Default = 10.

integer; Number of particles to be considered in the particle filter. Default = 100

integer; number of days being forecast. Default = 0

Output of scan_R0_date.

Number of parameter pairs to be sampled. This will determine how many trajectories are returned. Integer. Default = 10. This will determine how many trajectories are returned.

Number of particles. Positive Integer. Default = 100

Number of days being forecast. Default = 0

Logical, indicating whether the full model output, including the state and the declared outputs are returned. Deafult = FALSE

output of run_mcmc_chain; The results from the PMCMC run – can have mutliple chains.

integer; Number of iterations to discard from the start of MCMC run. Default = 0

number of chains that considered. Should inherent from pmcmc.

function to calculate log likelihood, must take named parameter vector as input, allow passing of implicit arguments corresponding to the main function arguments. Returns a named list, with entries: - $log_likelihood, a single numeric - $sample_state, a numeric vector corresponding to the state of a single particle, chosen at random, at the final time point for which we have data. If NULL, calculated using the function calc_loglikelihood.

interger; Number of trajectories to be returned. Integer. Default = 10.

integer; Number of particles to be considered in the particle filter. Default = 100

integer; number of days being forecast. Default = 0

Minimum value of R0 in the search

Maximum value of R0 in the search

Step to increment R0 between min and max

Earliest start date as 'yyyy-mm-dd'

Latest start date as 'yyyy-mm-dd'

Step to increment date in days

Deaths data to fit to. See example_deaths.csv and particle_filter_data()

Squire model parameters. Created from a call to one of the parameters_<type>_model functions.

Function for converting R0, Meff and R0_change. Function must have names arguments of R0, Meff and R0_change. Default is linear relationship on the log scale given by exp(log(R0) - Meff*(1-R0_change)).

Prior for R0. Default = NULL, which is a flat prior. Should be provided as a list with first argument the distribution function and the second the function arguments (excluding quantiles which are worked out based on R0_min and R0_max), e.g. 'list("func" = dnorm, args = list("mean"= 3.5, "sd"= 3))'.

Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0

Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0

Calendar dates at which the contact matrices set in model_params change. Defaut = NULL, i.e. no change

Calendar dates at which ICU bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Calendar dates at which hospital bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

A squire model. Default = explicit_SEIR()

list of parameters to use for the comparison function.

Number of particles. Positive Integer. Default = 100

Minimum value of R0 in the search

Maximum value of R0 in the search

Step to increment R0 between min and max

Earliest start date as 'yyyy-mm-dd'

Latest start date as 'yyyy-mm-dd'

Step to increment date in days

Minimum value of Meff (Movement effect size) in the search

Maximum value of Meff (Movement effect size) in the search

Step to increment Meff (Movement effect size) between min and max

Deaths data to fit to. See example_deaths.csv and particle_filter_data()

Squire model parameters. Created from a call to one of the parameters_<type>_model functions.

Function for converting R0, Meff and R0_change. Function must have names arguments of R0, Meff and R0_change. Default is linear relationship on the log scale given by exp(log(R0) - Meff*(1-R0_change)).

Prior for R0. Default = NULL, which is a flat prior. Should be provided as a list with first argument the distribution function and the second the function arguments (excluding quantiles which are worked out based on R0_min and R0_max), e.g. list("func" = dnorm, args = list("mean"= 3.5, "sd"= 3)).

Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0

Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0

Calendar dates at which the contact matrices set in model_params change. Defaut = NULL, i.e. no change

Calendar dates at which ICU bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

Calendar dates at which hospital bed capacity changes set in model_params change. Defaut = NULL, i.e. no change

A squire model. Default = explicit_SEIR()

list of parameters to use for the comparison function.

Number of particles. Positive Integer. Default = 100

An squire_simulation object

additional arguments affecting the summary produced.

Output of pmcmc or calibrate

Name of model output to tigger by. Must be one accepted by format_output

Value of trigger_metric at which triggering occurs

Vector of R0 values to be used for each lockdown. Default = c(0.5, 0.5, 0.5, 0.5)

Vector of lengths of each lockdown in days. Default = c(28, 42, 28, 42)

Maximum number of lockdowns. Default = 4

RNG seed to be used. Default = 931L