Reference

Model configuration
covidestim()	Configure a Covidestim run on a set of priors and input data
get_pop()	Population estimates for US states and counties
Input data Covidestim expects two data sources to be present - case data, and death data. These functions allow adding these data sources to the model configuration.
input_cases() input_deaths() input_rr() input_hosp() input_boost()	Input observational data
get_imm_init()	Immunity and cumulative infections (fraction) estimates for US states and counties
Custom priors These functions allow customization of model priors. By default, Covidestim comes with a complete set of priors, which have been tailed for estimation of U.S. counties and states. However, these functions make it possible to override the default parameterizations of the prior distributions, within certain constraints. This allows you to conduct exploratory modeling of other geographies, or test the effect of different priors on estimates. Prior Type Description priors_diagnosis(): rr_diag_asy_vs_sym Beta(α,β) Rate ratio of diagnosis, asymptomatic vs. symptomatic,non-severe rr_diag_sym_vs_sev Beta(α,β) RR diagnosis, symptomatic vs. severe p_diag_if_sev Beta(α,β) P(diagnosed | severe) priors_diagnosis_delays_scale(): dx_delay_sym Beta(α,β) Time to diagnosis as fraction of time spent symptomatic dx_delay_sev Beta(α,β) Time to diagnosis as fraction of time spent severe priors_progression(): inf_prg_delay Gamma(α,β) T(infection → symptoms) sym_prg_delay Gamma(α,β) T(symptoms → severe) sev_prg_delay Gamma(α,β) T(severe → dead) asy_rec_delay Gamma(α,β) T(infection → recovery), asymptomatics pri_serial_i Gamma(α,β) Serial interval infect_dist Gamma(α,β) Change in infectiousness seropos_dist Gamma(α,β) Change in seropositivity priors_reporting_delays(): cas_rep_delay Gamma(α,β) T(test → report) die_rep_delay Gamma(α,β) T(death → report) priors_transitions(): p_sym_if_inf Beta(α,β) P(symptoms | infected) p_sev_if_sym Beta(α,β) P(severe | symptoms) p_die_if_sev Beta(α,β) P(die | severe) p_die_if_inf Beta(α,β) P(die | infection) (IFR) ifr_decl_OR Gamma(α,β) ~IFR decline Helper functions:
gamma_shape() gamma_rate()	Convert mean/variance stats to shape/rate parameters of gamma distribution
Model execution
run(<covidestim>)	Run the Covidestim model using NUTS
runOptimizer(<covidestim>)	Run the Covidestim model using BFGS
Results
summary(<covidestim_result>)	Summarize a Covidestim run
viz(<covidestim_result>)	Visualize results of a Covidestim run
Example data Covidestim provides an example dataset to try running the model with.
example_ct_data()	Example CT case/death data
build_priors()	Helper function for manipulation of prior-related list-structures
covidestim-package	The 'covidestim' package.
covidestim_add(<priors>)	When adding priors, we want to be sure that a new 'modelconfig' object is created, in order to check these priors
defaultConfig()	High level description of the function
get_ifr_raw()	Estimated IFR for US counties and states
modelconfig()	Model configuration object. It can be modified through overloading the addition operator
modelconfig_add(<priors>)	Specialization for 'priors' classes.
`+`(<modelconfig>)	An overloaded addition operator, dispatched on the type of the lhs argument.
priors_diagnosis()	Priors on probability of diagnosis
priors_diagnosis_delays_scale()	Priors on diagnostic delays
priors_progression()	Fixed values on delay to progression
priors_reporting_delays()	Fixed distributions on reporting delays
priors_transitions()	Priors for transitions
summaryEpi()	Summarize internal parameters of a Covidestim run