The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Backcalculating Reporting Delays
Distributions from Linelist Data
This R Markdown document walks through the steps for imputing a
reporting delay distribution from linelist data with missing disease
onset data, adapted in STAN from Li
and White
There are two starting points for this vignette: either you have
caseCount data, which are aggregated case counts by
day, or you have Line-list data means you have a single
row for each case, that has dates for: infection, symptom onset,
positive test, and when this was reported to public health agencies.
Example: lineList data
Step 1. Load data
data("sample_report_dates")
data("sample_onset_dates")
Step 2. Creating linelist object
my_linelist <- create_linelist(report_dates = sample_report_dates,
onset_dates = sample_onset_dates)
#> Warning in create_linelist(report_dates = sample_report_dates, onset_dates =
#> sample_onset_dates): Some onset dates are NA
head(my_linelist)
#> report_date delay_int onset_date is_weekend report_int week_int
#> 1 2020-01-01 4 2019-12-28 0 1 1
#> 2 2020-01-01 4 2019-12-28 0 1 1
#> 3 2020-01-01 NA <NA> 0 1 1
#> 4 2020-01-01 NA <NA> 0 1 1
#> 5 2020-01-01 NA <NA> 0 1 1
#> 6 2020-01-01 8 2019-12-24 0 1 1
Step 3. Define the serial interval. The
si() function creates a vector of length 14 with shape and
rate for a gamma distribution. Note, this has a leading
0 to indicate no infections on the day of disease onset.
sip <- si(14, 4.29, 1.18)
plot(sip, type = 'l')
Step 5. Run the back-calculation algorithm. The
default is an R(t) sliding window of 7 days. Additional options to STAN
can be specified in the last argument (e.g., chains, cores,
control).
out_list_demo <- run_backnow(my_linelist, sip = sip, chains = 1)
Plot outputs. The points are aggregated reported
cases, and the red line (and shaded confidence interval) represent the
back-calculated case onsets that lead to the reported data.
data("out_list_demo")
plot(out_list_demo, "est")
You can also plot the R(t) curve over time. In this
case, the red line (and shaded confidence interval) represent the
time-varying r(t). See Li and White for description.
data("out_list_demo")
plot(out_list_demo, "rt")
Example: Case Count data
You can also do the same from case count data, although at some point
you will have to assume a reporting delay distribution, so this would be
a little circular.
Step 1. Load data
data("sample_dates")
data("sample_location")
data("sample_cases")
head(sample_dates)
#> [1] "2020-01-01" "2020-01-02" "2020-01-03" "2020-01-04" "2020-01-05"
#> [6] "2020-01-06"
head(sample_cases)
#> [1] 10 1 4 11 8 10
head(sample_location)
#> [1] "Tatooine" "Tatooine" "Tatooine" "Tatooine" "Tatooine" "Tatooine"
Step 2. Creating case-counts
caseCounts <- create_caseCounts(date_vec = sample_dates,
location_vec = sample_location,
cases_vec = sample_cases)
head(caseCounts)
#> date cases location
#> 1 2020-01-01 10 Tatooine
#> 2 2020-01-02 1 Tatooine
#> 3 2020-01-03 4 Tatooine
#> 4 2020-01-04 11 Tatooine
#> 5 2020-01-05 8 Tatooine
#> 6 2020-01-06 10 Tatooine
Step 3. Convert to linelist data. You can specify
the distribution for my_linelist in
convert_to_linelist. reportF is the
distribution function, _args lists the distribution params
that are not x, and _missP is the percent
missing. This must be between \({0 < x <
1}\). Both ‘caseCounts’ and ‘caseCounts_line’ objects can be fed
into run_backnow. The implied onset distribution is
rnbinom() with size = 3 and
mu = 9, with reportF_missP = 0.6 .
my_linelist <- convert_to_linelist(caseCounts,
reportF = rnbinom,
reportF_args = list(size = 3, mu = 9),
reportF_missP = 0.6)
head(my_linelist)
#> report_date delay_int onset_date is_weekend report_int week_int
#> 1 2020-01-01 NA <NA> 0 1 1
#> 2 2020-01-01 12 2019-12-20 0 1 1
#> 3 2020-01-01 NA <NA> 0 1 1
#> 4 2020-01-01 NA <NA> 0 1 1
#> 5 2020-01-01 5 2019-12-27 0 1 1
#> 6 2020-01-01 NA <NA> 0 1 1
Step 4. Define the serial interval. The
si() function creates a vector of length 14 with alpha and
beta as defined in Li and White, for COVID-19.
sip <- si(14, 4.29, 1.18)
Step 5. Run the back-calculation algorithm. The
defaults are 2000 iterations and an R(t) sliding window of 7 days.
options(mc.cores = 4)
out_list_demo <- run_backnow(my_linelist, sip = sip)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.