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_vaccines()

Input observational data

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 NYC dataset to try running the model with.

example_nyc_data()

Example NYC 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