`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 = 2000,
thin = 1,
seed = 42,
adapt_delta = 0.98,
max_treedepth = 14,
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

`sampling`

.- iter
The number of iterations to run during NUTS sampling, as passed to

`sampling`

.- thin
A positive integer to specify period for saving samples, as passed to

`sampling`

. Modify this only if you intend to inspect raw iterations data returned by`sampling`

.- 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.

`New York`

) being modeled. Required.

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 multi-language 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
#> [ x ] Vaccines
```