covidestim
returns a base configuration of the model with the default
set of priors, and no input data. This configuration, after adding input
data (see input_cases
, input_deaths
,
priors_*
), represents a valid model configuration that can be passed
to run.covidestim
(for NUTS) or
runOptimizer.covidestim
(for BFGS).
covidestim( ndays, ndays_before = 28, pop_size = 1e+12, chains = 3, iter = 1500, thin = 1, seed = 42, adapt_delta = 0.92, max_treedepth = 12, window.length = 7, region )
ndays  A positive integer. The number of days of input data being modeled. This should always be set to the number of days in your input data. 

ndays_before  A positive integer. How many days before the first day of model data should be modeled? A higher number will produce estimates that go farther back in time, however, those estimates will contain more and more uncertainty. 
pop_size  A positive integer What is the population in the geography being modelled? This sets the max susceptible population and becomes important as the population ever infected approaches the population size. 
chains  The number of chains to use during NUTS sampling, as passed to

iter  The number of iterations to run during NUTS sampling, as passed
to 
thin  A positive integer to specify period for saving samples, as
passed to 
seed  A number. The random number generator seed for use in NUTS sampling or in BFGS. 
region  A string. The FIPS code (for U.S. counties) or state name
(e.g. 
An S3 object of type covidestim
. This can be passed to
run.covidestim
or runOptimizer.covidestim
to execute the model, as
long as input data has been added (using the addition operator, see
example). This object can also be saved to disk using
saveRDS
to enable reproducibility across platforms or
sessions. However, note that Stan runs are only reproducible under very
specific conditions due to Stan's multilanguage architecture. The
print
method is overloaded to return to the user a summary of the
configuration, including prior values and the presence or absence of input
data.
# Note that this configuration is improper as it uses New York City # case/death data, but uses Manhattan's FIPS code and population size. # (for demonstration purposes only!) covidestim(ndays = 120, seed = 42, region = '36061', pop_size = 1.63e6) + input_cases(example_nyc_data('cases')) + input_deaths(example_nyc_data('deaths'))#> Covidestim Configuration: #> #> Seed: 42 #> Chains: 3 #> Iterations: 2000 #> Warmup runs: 1333 #> Priors: Valid #> ndays: 120 #> #> Priors: #> #> log_new_inf_0_mu 0 #> log_new_inf_0_sd 10 #> logRt_mu 0 #> logRt_sd 3 #> inf_imported_mu 0 #> inf_imported_sd 0.6265664160401 #> deriv1_spl_par_sd 0.5 #> deriv2_spl_par_sd 0.1 #> p_sym_if_inf [alpha] 5.143 #> p_sym_if_inf [beta] 3.536 #> p_sev_if_sym [alpha] 1.8854 #> p_sev_if_sym [beta] 20.002 #> p_die_if_sev [alpha] 28.239 #> p_die_if_sev [beta] 162.3 #> p_die_if_inf [alpha] 15.915 #> p_die_if_inf [beta] 3167.1 #> ifr_decl_OR [alpha] 12.031 #> ifr_decl_OR [beta] 8.999 #> inf_prg_delay [shape] 3.413 #> inf_prg_delay [rate] 0.6051 #> sym_prg_delay [shape] 1.624 #> sym_prg_delay [rate] 0.2175 #> sev_prg_delay [shape] 2.061 #> sev_prg_delay [rate] 0.2277 #> serial_i [shape] 129.1 #> serial_i [rate] 22.25 #> rr_diag [alpha]sy_vs_sym_a 2 #> rr_diag [alpha]sy_vs_sym [beta] 18 #> rr_diag_sym_vs_sev [alpha] 2 #> rr_diag_sym_vs_sev [beta] 2 #> p_diag_if_sev [alpha] 20 #> p_diag_if_sev [beta] 5 #> #> Inputs: #> #> [ 120] Cases #> [ 120] Deaths