Skip to content

Update EpiNow2 vignette #1389

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
github-actions wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update EpiNow2 vignette #1389

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file modified vignettes/EpiNow2-plot_estimates-1.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file modified vignettes/EpiNow2-plot_regional_epinow_summary-1.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
72 changes: 36 additions & 36 deletions vignettes/EpiNow2.Rmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ library(EpiNow2)

Distributions can be supplied in two ways. First, one can supply delay data to `estimate_delay()`, where a subsampled bootstrapped lognormal will be fit to account for uncertainty in the observed data without being biased by changes in incidence (see `?EpiNow2::estimate_delay()`).

Second, one can specify predetermined delays with uncertainty using the distribution functions such as `Gamma` or `Lognormal`. An arbitrary number of delay distributions are supported in `dist_spec()` with a common use case being an incubation period followed by a reporting delay. For more information on specifying distributions see (see `?EpiNow2::Distributions`).
Second, one can specify predetermined delays with uncertainty using the distribution functions such as `Gamma` or `Lognormal`. An arbitrary number of delay distributions are supported in `dist_spec()` with a common use case being an incubation period followed by a reporting delay. For more information on specifying distributions see `?EpiNow2::Distributions` or the [delays vignette](delays.html).

For example if data on the delay between onset and infection was available we could fit a distribution to it, using `estimate_delay()`, with appropriate uncertainty as follows (note this is a synthetic example),

Expand Down Expand Up @@ -156,13 +156,13 @@ knitr::kable(summary(estimates))



|measure |estimate |
|:----------------------------|:------------------------|
|New infections per day |2236 (1364 -- 3747) |
|Expected change in reports |Likely decreasing |
|Effective reproduction no. |0.89 (0.71 -- 1.1) |
|Rate of growth |-0.029 (-0.098 -- 0.037) |
|Doubling/halving time (days) |-24 (19 -- -7.1) |
|measure |estimate |
|:----------------------------|:-----------------------|
|New infections per day |2238 (1361 -- 3776) |
|Expected change in reports |Likely decreasing |
|Effective reproduction no. |0.9 (0.72 -- 1.1) |
|Rate of growth |-0.029 (-0.095 -- 0.04) |
|Doubling/halving time (days) |-24 (17 -- -7.3) |



Expand All @@ -173,43 +173,43 @@ Summarised parameter estimates can also easily be returned, either filtered for
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
#> <Date> <char> <int> <char> <num> <num> <num> <num>
#> 1: 2020-02-22 R NA estimate 2.189648 2.196213 0.11336185 2.023501
#> 2: 2020-02-23 R NA estimate 2.153710 2.159198 0.10259863 1.998873
#> 3: 2020-02-24 R NA estimate 2.118107 2.120663 0.09411365 1.974120
#> 4: 2020-02-25 R NA estimate 2.078305 2.080785 0.08768269 1.941711
#> 5: 2020-02-26 R NA estimate 2.037962 2.039743 0.08296362 1.909825
#> 6: 2020-02-27 R NA estimate 1.995351 1.997715 0.07954817 1.874942
#> 1: 2020-02-22 R NA estimate 2.168650 2.177295 0.11655839 1.993084
#> 2: 2020-02-23 R NA estimate 2.131079 2.139736 0.10617133 1.969911
#> 3: 2020-02-24 R NA estimate 2.093210 2.100701 0.09805302 1.947125
#> 4: 2020-02-25 R NA estimate 2.054677 2.060418 0.09188230 1.918718
#> 5: 2020-02-26 R NA estimate 2.013701 2.019119 0.08723416 1.882894
#> 6: 2020-02-27 R NA estimate 1.970974 1.977023 0.08365191 1.847897
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> <num> <num> <num> <num> <num>
#> 1: 2.116083 2.163518 2.221263 2.267635 2.391763
#> 2: 2.086574 2.128094 2.182408 2.224640 2.333380
#> 3: 2.054704 2.092117 2.140637 2.180685 2.279482
#> 4: 2.019448 2.055093 2.099769 2.136280 2.230685
#> 5: 1.981584 2.015820 2.057302 2.089886 2.182033
#> 6: 1.941424 1.975040 2.014929 2.046460 2.135327
#> 1: 2.101110 2.139373 2.199382 2.250761 2.378719
#> 2: 2.068130 2.106184 2.158555 2.208929 2.322276
#> 3: 2.033855 2.070210 2.118975 2.164187 2.272569
#> 4: 1.997565 2.032288 2.077399 2.120360 2.220921
#> 5: 1.958820 1.991759 2.035872 2.077043 2.168067
#> 6: 1.919210 1.951074 1.991899 2.031857 2.118908
```

Reported cases can be extracted using `get_predictions()` which returns summarised estimates by default.


``` r
head(get_predictions(estimates))
#> date median mean sd lower_90 lower_50 lower_20 upper_20
#> <Date> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 35 36.3825 9.99996 22 29.75 33 38
#> 2: 2020-02-23 52 53.0555 13.09439 34 44.00 49 55
#> 3: 2020-02-24 65 65.6955 15.56759 42 55.00 60 69
#> 4: 2020-02-25 72 72.8700 16.93146 48 61.00 68 76
#> 5: 2020-02-26 83 83.5495 18.01854 54 72.00 78 87
#> 6: 2020-02-27 120 121.3125 25.71222 82 103.00 113 126
#> date median mean sd lower_90 lower_50 lower_20 upper_20
#> <Date> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 35 35.6935 9.701475 21 29 32.0 37
#> 2: 2020-02-23 53 53.5045 13.230249 34 45 49.0 56
#> 3: 2020-02-24 64 65.5110 15.668204 42 54 60.6 68
#> 4: 2020-02-25 72 73.2385 16.400613 48 62 68.0 76
#> 5: 2020-02-26 82 83.3325 18.092833 56 71 78.0 87
#> 6: 2020-02-27 118 120.4175 25.729384 82 103 112.0 125
#> upper_50 upper_90
#> <num> <num>
#> 1: 42 54
#> 2: 61 77
#> 3: 75 93
#> 4: 83 104
#> 5: 95 115
#> 6: 137 167
#> 1: 42 53.00
#> 2: 61 77.00
#> 3: 75 93.00
#> 4: 84 102.00
#> 5: 94 115.00
#> 6: 136 166.05
```

A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following `plot` method.
Expand Down Expand Up @@ -281,8 +281,8 @@ knitr::kable(estimates$summary$summarised_results$table)

|Region |New infections per day |Expected change in reports |Effective reproduction no. |Rate of growth |Doubling/halving time (days) |
|:--------|:----------------------|:--------------------------|:--------------------------|:-----------------------|:----------------------------|
|realland |2002 (1078 -- 3830) |Likely decreasing |0.85 (0.63 -- 1.1) |-0.041 (-0.11 -- 0.035) |-17 (20 -- -6.3) |
|testland |3977 (2069 -- 7977) |Likely decreasing |0.85 (0.61 -- 1.2) |-0.043 (-0.11 -- 0.042) |-16 (16 -- -6.1) |
|realland |1965 (1068 -- 3835) |Likely decreasing |0.84 (0.62 -- 1.1) |-0.044 (-0.11 -- 0.037) |-16 (19 -- -6.4) |
|testland |4111 (2130 -- 7803) |Likely decreasing |0.86 (0.63 -- 1.1) |-0.039 (-0.11 -- 0.038) |-18 (18 -- -6.3) |



Expand Down