This applies a lognormal convolution with given, potentially time-varying
parameters representing the parameters of the lognormal distribution used for
the convolution and an optional scaling factor. This is akin to the model
used in estimate_secondary() and simulate_secondary().
Arguments
- data
A
<data.frame>containing thedateof report andprimarycases as a numeric vector.- type
A character string indicating the type of observation the secondary reports are. Options include:
"incidence": Assumes that secondary reports equal a convolution of previously observed primary reported cases. An example application is deaths from an infectious disease predicted by reported cases of that disease (or estimated infections).
"prevalence": Assumes that secondary reports are cumulative and are defined by currently observed primary reports minus a convolution of secondary reports. An example application is hospital bed usage predicted by hospital admissions.
- family
Character string defining the observation model. Options are Negative binomial ("negbin"), the default, Poisson ("poisson"), and "none" meaning the expectation is returned.
- delay_max
Integer, defaulting to 30 days. The maximum delay used in the convolution model.
- ...
Additional parameters to pass to the observation model (i.e
rnbinomorrpois).
Value
A <data.frame> containing simulated data in the format required by
estimate_secondary().
Details
Up to version 1.4.0 this function was called simulate_secondary().
Examples
# load data.table for manipulation
library(data.table)
#### Incidence data example ####
# make some example secondary incidence data
cases <- example_confirmed
cases <- as.data.table(cases)[, primary := confirm]
# Assume that only 40 percent of cases are reported
cases[, scaling := 0.4]
#> date confirm primary scaling
#> <Date> <num> <num> <num>
#> 1: 2020-02-22 14 14 0.4
#> 2: 2020-02-23 62 62 0.4
#> 3: 2020-02-24 53 53 0.4
#> 4: 2020-02-25 97 97 0.4
#> 5: 2020-02-26 93 93 0.4
#> ---
#> 126: 2020-06-26 296 296 0.4
#> 127: 2020-06-27 255 255 0.4
#> 128: 2020-06-28 175 175 0.4
#> 129: 2020-06-29 174 174 0.4
#> 130: 2020-06-30 126 126 0.4
# Parameters of the assumed log normal delay distribution
cases[, meanlog := 1.8][, sdlog := 0.5]
#> date confirm primary scaling meanlog sdlog
#> <Date> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 14 14 0.4 1.8 0.5
#> 2: 2020-02-23 62 62 0.4 1.8 0.5
#> 3: 2020-02-24 53 53 0.4 1.8 0.5
#> 4: 2020-02-25 97 97 0.4 1.8 0.5
#> 5: 2020-02-26 93 93 0.4 1.8 0.5
#> ---
#> 126: 2020-06-26 296 296 0.4 1.8 0.5
#> 127: 2020-06-27 255 255 0.4 1.8 0.5
#> 128: 2020-06-28 175 175 0.4 1.8 0.5
#> 129: 2020-06-29 174 174 0.4 1.8 0.5
#> 130: 2020-06-30 126 126 0.4 1.8 0.5
# Simulate secondary cases
cases <- convolve_and_scale(cases, type = "incidence")
cases
#> date confirm primary scaling meanlog sdlog index scaled conv
#> <Date> <num> <num> <num> <num> <num> <int> <num> <num>
#> 1: 2020-02-22 14 14 0.4 1.8 0.5 1 5.6 5.600000
#> 2: 2020-02-23 62 62 0.4 1.8 0.5 2 24.8 5.827560
#> 3: 2020-02-24 53 53 0.4 1.8 0.5 3 21.2 8.801043
#> 4: 2020-02-25 97 97 0.4 1.8 0.5 4 38.8 12.938343
#> 5: 2020-02-26 93 93 0.4 1.8 0.5 5 37.2 16.590082
#> ---
#> 126: 2020-06-26 296 296 0.4 1.8 0.5 126 118.4 91.432820
#> 127: 2020-06-27 255 255 0.4 1.8 0.5 127 102.0 95.786381
#> 128: 2020-06-28 175 175 0.4 1.8 0.5 128 70.0 103.811968
#> 129: 2020-06-29 174 174 0.4 1.8 0.5 129 69.6 109.093575
#> 130: 2020-06-30 126 126 0.4 1.8 0.5 130 50.4 109.029626
#> secondary
#> <int>
#> 1: 5
#> 2: 5
#> 3: 8
#> 4: 12
#> 5: 16
#> ---
#> 126: 91
#> 127: 95
#> 128: 103
#> 129: 109
#> 130: 109
#### Prevalence data example ####
# make some example prevalence data
cases <- example_confirmed
cases <- as.data.table(cases)[, primary := confirm]
# Assume that only 30 percent of cases are reported
cases[, scaling := 0.3]
#> date confirm primary scaling
#> <Date> <num> <num> <num>
#> 1: 2020-02-22 14 14 0.3
#> 2: 2020-02-23 62 62 0.3
#> 3: 2020-02-24 53 53 0.3
#> 4: 2020-02-25 97 97 0.3
#> 5: 2020-02-26 93 93 0.3
#> ---
#> 126: 2020-06-26 296 296 0.3
#> 127: 2020-06-27 255 255 0.3
#> 128: 2020-06-28 175 175 0.3
#> 129: 2020-06-29 174 174 0.3
#> 130: 2020-06-30 126 126 0.3
# Parameters of the assumed log normal delay distribution
cases[, meanlog := 1.6][, sdlog := 0.8]
#> date confirm primary scaling meanlog sdlog
#> <Date> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 14 14 0.3 1.6 0.8
#> 2: 2020-02-23 62 62 0.3 1.6 0.8
#> 3: 2020-02-24 53 53 0.3 1.6 0.8
#> 4: 2020-02-25 97 97 0.3 1.6 0.8
#> 5: 2020-02-26 93 93 0.3 1.6 0.8
#> ---
#> 126: 2020-06-26 296 296 0.3 1.6 0.8
#> 127: 2020-06-27 255 255 0.3 1.6 0.8
#> 128: 2020-06-28 175 175 0.3 1.6 0.8
#> 129: 2020-06-29 174 174 0.3 1.6 0.8
#> 130: 2020-06-30 126 126 0.3 1.6 0.8
# Simulate secondary cases
cases <- convolve_and_scale(cases, type = "prevalence")
cases
#> date confirm primary scaling meanlog sdlog index scaled conv
#> <Date> <num> <num> <num> <num> <num> <int> <num> <num>
#> 1: 2020-02-22 14 14 0.3 1.6 0.8 1 4.2 4.200000
#> 2: 2020-02-23 62 62 0.3 1.6 0.8 2 18.6 6.749663
#> 3: 2020-02-24 53 53 0.3 1.6 0.8 3 15.9 10.939618
#> 4: 2020-02-25 97 97 0.3 1.6 0.8 4 29.1 13.765588
#> 5: 2020-02-26 93 93 0.3 1.6 0.8 5 27.9 17.347381
#> ---
#> 126: 2020-06-26 296 296 0.3 1.6 0.8 126 88.8 77.941892
#> 127: 2020-06-27 255 255 0.3 1.6 0.8 127 76.5 82.996181
#> 128: 2020-06-28 175 175 0.3 1.6 0.8 128 52.5 83.459485
#> 129: 2020-06-29 174 174 0.3 1.6 0.8 129 52.2 80.163424
#> 130: 2020-06-30 126 126 0.3 1.6 0.8 130 37.8 75.330670
#> secondary
#> <int>
#> 1: 4
#> 2: 18
#> 3: 23
#> 4: 38
#> 5: 49
#> ---
#> 126: 273
#> 127: 266
#> 128: 235
#> 129: 207
#> 130: 170