data.frame summarizing a Covidestim model run. Note that if
runOptimizer.covidestim is used, all
variables will be
NA-valued, because BFGS does not generate
# S3 method for covidestim_result summary(ccr, include.before = TRUE, index = FALSE)
A logical scalar. Include estimations that fall in the
period before the first day of input data? (This period is of length
ndays_before as passed to
elements of variables which do not have values for this "before" period
will be represented as
A logical scalar. If
TRUE, will include a variable
index in the output, with range
1:(ndays_before + ndays).
data.frame with the following variables:
Date as a
Estimate of the effective reproductive number (\(R_t\)). Median and 95% interval, ℝ.
The number of modeled infections that occurred on date
This includes infections that may never cause symptoms, as well as
infections which will never show up in case reports (will never be
diagnosed). Being indexed by date-of-occurrence, reporting lag is
absent from this outcome. Median and 95% interval, ℝ.
The number of modeled cumulative infections (not cases, or diagnoses)
that have occurred by the end of date
date. Median and
95% interval, ℝ.
The number of modeled diagnoses that occurred on date
This is the sum of:
New asmptomatic diagnoses on date
New diagnoses of symptomatic, non-severe individuals on date
New diagnoses of severe individuals on date
Median and 95% interval, ℝ.
The number of modeled case reports for date
date. This will
always differ from the number of observed cases: it is the
model's approximation of how many case reports should have been filed
that day. This outcome reflects underascertainment, and the full delay
structure downstream of infection events. Note: infection events are
tabulated by the
infections[.lo/.hi] outcomes. Median
and 95% interval, ℝ.
The number of modeled transitions of infected individuals into the
infected, symptomatic health state on date
date. This takes
into account the probability of becoming symptomatic and the delay
between infection and presentation of symptoms. Median and 95%
The number of modeled diagnoses of symptomatic individuals occurring
date. The difference between this outcome and
diagnoses outcome is that
modeled diagnoses of asymptomatic individuals. Median and 95%
The number of transitions into the "severe" health state on date
date. The "severe" state is defined as disease that would merit
hospitalization. This outcome is not intended to model observational
data detailing the number of COVID-positive hospital admissions or
COVID-primary-cause hospital admissions. Median and 95%
The number of modeled deaths for date
date. The number of
deaths estimated to have occurred on date
date and does not
account for reporting delays. Median and 95% interval, ℝ.
The number of modeled death reports for a date
will always differ from the number of observed deaths for that same
deaths.fitted is approximating how many death
reports should exist for that date. Median and 95% interval,
Was there input data available for date
date? This should be
TRUE, except for the first month of estimates. logical.
The number of individuals in the population who are modeled as being seropositive. This is an unreliable outcome that we don't recommend using. Median and 95% interval, ℝ.
Meant to be an estimate of infectiousness of the population for comparison with wastewater data. This is an unreliable outcome that we don't recommend using. Median and 95% interval, ℝ.