![[Maturing]](figures/lifecycle-maturing.svg)
Used to produce summary output either internally in regional_epinow or
externally.
Usage
regional_summary(
regional_output = NULL,
reported_cases,
results_dir = NULL,
summary_dir = NULL,
target_date = NULL,
region_scale = "Region",
all_regions = TRUE,
return_output = FALSE,
plot = TRUE,
max_plot = 10,
...
)Arguments
- regional_output
A list of output as produced by
regional_epinowand stored in theregionallist.- reported_cases
A data frame of confirmed cases (confirm) by date (date), and region (
region).- results_dir
An optional character string indicating the location of the results directory to extract results from.
- summary_dir
A character string giving the directory in which to store summary of results.
- target_date
A character string giving the target date for which to extract results (in the format "yyyy-mm-dd"). Defaults to latest available estimates.
- region_scale
A character string indicating the name to give the regions being summarised.
- all_regions
Logical, defaults to
TRUE. Should summary plots for all regions be returned rather than just regions of interest.- return_output
Logical, defaults to FALSE. Should output be returned, this automatically updates to TRUE if no directory for saving is specified.
- plot
Logical, defaults to
TRUE. Should regional summary plots be produced.- max_plot
Numeric, defaults to 10. A multiplicative upper bound on the\ number of cases shown on the plot. Based on the maximum number of reported cases.
- ...
Additional arguments passed to
report_plots.
Examples
# \donttest{
# example delays
generation_time <- get_generation_time(
disease = "SARS-CoV-2", source = "ganyani"
)
incubation_period <- get_incubation_period(
disease = "SARS-CoV-2", source = "lauer"
)
reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 30)
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
# example case vector from EpiSoon
cases <- example_confirmed[1:30]
cases <- data.table::rbindlist(list(
data.table::copy(cases)[, region := "testland"],
cases[, region := "realland"]
))
# run basic nowcasting pipeline
out <- regional_epinow(
reported_cases = cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
output = "region",
rt = NULL
)
#> INFO [2023-09-26 16:08:11] Producing following optional outputs: regions
#> Logging threshold set at INFO for the EpiNow2 logger
#> Writing EpiNow2 logs to the console and: /tmp/RtmpH53zkW/regional-epinow/2020-03-22.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
#> Writing EpiNow2.epinow logs to: /tmp/RtmpH53zkW/epinow/2020-03-22.log
#> INFO [2023-09-26 16:08:11] Reporting estimates using data up to: 2020-03-22
#> INFO [2023-09-26 16:08:11] No target directory specified so returning output
#> INFO [2023-09-26 16:08:11] Producing estimates for: testland, realland
#> INFO [2023-09-26 16:08:11] Regions excluded: none
#> INFO [2023-09-26 16:08:25] Completed estimates for: testland
#> INFO [2023-09-26 16:08:41] Completed estimates for: realland
#> INFO [2023-09-26 16:08:41] Completed regional estimates
#> INFO [2023-09-26 16:08:41] Regions with estimates: 2
#> INFO [2023-09-26 16:08:41] Regions with runtime errors: 0
#> INFO [2023-09-26 16:08:41] No target directory specified so returning timings
regional_summary(
regional_output = out$regional,
reported_cases = cases
)
#> INFO [2023-09-26 16:08:41] No summary directory specified so returning summary output
#> $latest_date
#> [1] "2020-03-22"
#>
#> $results
#> $results$estimates
#> $results$estimates$summarised
#> region date variable strat type median
#> 1: testland 2020-02-22 R <NA> estimate 1.7147429
#> 2: testland 2020-02-23 R <NA> estimate 1.7310053
#> 3: testland 2020-02-24 R <NA> estimate 1.6240258
#> 4: testland 2020-02-25 R <NA> estimate 1.6285029
#> 5: testland 2020-02-26 R <NA> estimate 1.5682007
#> ---
#> 422: realland 2020-03-26 reported_cases <NA> forecast 1107.5000000
#> 423: realland 2020-03-27 reported_cases <NA> forecast 1256.5000000
#> 424: realland 2020-03-28 reported_cases <NA> forecast 1101.0000000
#> 425: realland 2020-03-29 reported_cases <NA> forecast 1244.0000000
#> 426: realland <NA> reporting_overdispersion <NA> <NA> 0.8096661
#> mean sd lower_90 lower_50 lower_20 upper_20
#> 1: 1.7245497 1.196592e-01 1.5495735 1.6470019 1.690455 1.7418062
#> 2: 1.7393635 1.173238e-01 1.5678749 1.6635142 1.706809 1.7567130
#> 3: 1.6320314 1.092045e-01 1.4735103 1.5615379 1.601101 1.6476549
#> 4: 1.6343665 1.034627e-01 1.4810787 1.5675213 1.604814 1.6503125
#> 5: 1.5734779 9.646081e-02 1.4320509 1.5106362 1.545274 1.5878998
#> ---
#> 422: 1716.6750000 1.902262e+03 102.9500000 498.0000000 843.000000 1457.8000000
#> 423: 1973.6790000 2.096796e+03 97.8500000 539.0000000 932.600000 1682.2000000
#> 424: 1722.4890000 1.895179e+03 99.8000000 477.7500000 825.800000 1474.6000000
#> 425: 1898.2515000 2.022643e+03 106.9000000 546.5000000 963.000000 1659.0000000
#> 426: 0.8709724 3.618674e-01 0.3976072 0.6097767 0.730065 0.8974375
#> upper_50 upper_90
#> 1: 1.786356 1.928958
#> 2: 1.801276 1.939129
#> 3: 1.690130 1.824005
#> 4: 1.689537 1.816406
#> 5: 1.626956 1.743139
#> ---
#> 422: 2233.000000 5316.200000
#> 423: 2662.250000 6204.500000
#> 424: 2304.500000 5302.550000
#> 425: 2463.750000 6091.700000
#> 426: 1.074113 1.537369
#>
#>
#>
#> $summarised_results
#> $summarised_results$table
#> Region New confirmed cases by infection date
#> 1: realland 1143 (1060 -- 1277)
#> 2: testland 1142 (1065 -- 1287)
#> Expected change in daily cases Effective reproduction no.
#> 1: Decreasing 0.79 (0.75 -- 0.84)
#> 2: Decreasing 0.8 (0.74 -- 0.84)
#> Rate of growth Doubling/halving time (days)
#> 1: -0.047 (-0.056 -- -0.04) -15 (-17 -- -12)
#> 2: -0.046 (-0.056 -- -0.04) -15 (-17 -- -12)
#>
#> $summarised_results$data
#> region estimate median mean sd lower_90 lower_50
#> 1: realland 1143 (1060 -- 1277) 1143.00 1154.00 68.000 1060.00 1124.00
#> 2: testland 1142 (1065 -- 1287) 1142.00 1156.00 76.000 1065.00 1125.00
#> 3: testland 0.8 (0.74 -- 0.84) 0.80 0.80 0.030 0.74 0.78
#> 4: realland 0.79 (0.75 -- 0.84) 0.79 0.79 0.029 0.75 0.78
#> lower_20 upper_20 upper_50 upper_90 metric
#> 1: 1138.00 1152.0 1177.00 1277.00 New confirmed cases by infection date
#> 2: 1138.00 1151.0 1176.00 1287.00 New confirmed cases by infection date
#> 3: 0.79 0.8 0.81 0.84 Effective reproduction no.
#> 4: 0.79 0.8 0.81 0.84 Effective reproduction no.
#> Expected change in daily cases prob_control
#> 1: Decreasing 1
#> 2: Decreasing 1
#> 3: Decreasing 1
#> 4: Decreasing 1
#>
#> $summarised_results$regions_by_inc
#> [1] "realland" "testland"
#>
#>
#> $summary_plot
#>
#> $summarised_measures
#> $summarised_measures$rt
#> region date strat type median
#> 1: realland 2020-02-22 <NA> estimate 1.7197107
#> 2: realland 2020-02-23 <NA> estimate 1.7346862
#> 3: realland 2020-02-24 <NA> estimate 1.6272136
#> 4: realland 2020-02-25 <NA> estimate 1.6310429
#> 5: realland 2020-02-26 <NA> estimate 1.5697385
#> 6: realland 2020-02-27 <NA> estimate 1.5409441
#> 7: realland 2020-02-28 <NA> estimate 1.5425465
#> 8: realland 2020-02-29 <NA> estimate 1.5456320
#> 9: realland 2020-03-01 <NA> estimate 1.5279052
#> 10: realland 2020-03-02 <NA> estimate 1.3060227
#> 11: realland 2020-03-03 <NA> estimate 1.1575705
#> 12: realland 2020-03-04 <NA> estimate 1.0812899
#> 13: realland 2020-03-05 <NA> estimate 0.9973976
#> 14: realland 2020-03-06 <NA> estimate 0.9287066
#> 15: realland 2020-03-07 <NA> estimate 0.8803822
#> 16: realland 2020-03-08 <NA> estimate 0.8178520
#> 17: realland 2020-03-09 <NA> estimate based on partial data 0.8896857
#> 18: realland 2020-03-10 <NA> estimate based on partial data 0.8659154
#> 19: realland 2020-03-11 <NA> estimate based on partial data 0.8468010
#> 20: realland 2020-03-12 <NA> estimate based on partial data 0.8324072
#> 21: realland 2020-03-13 <NA> estimate based on partial data 0.8213627
#> 22: realland 2020-03-14 <NA> estimate based on partial data 0.8132288
#> 23: realland 2020-03-15 <NA> estimate based on partial data 0.8081611
#> 24: realland 2020-03-16 <NA> estimate based on partial data 0.8049249
#> 25: realland 2020-03-17 <NA> estimate based on partial data 0.8017415
#> 26: realland 2020-03-18 <NA> estimate based on partial data 0.7993248
#> 27: realland 2020-03-19 <NA> estimate based on partial data 0.7981856
#> 28: realland 2020-03-20 <NA> estimate based on partial data 0.7968135
#> 29: realland 2020-03-21 <NA> estimate based on partial data 0.7964032
#> 30: realland 2020-03-22 <NA> estimate based on partial data 0.7947365
#> 31: realland 2020-03-23 <NA> forecast 0.7946219
#> 32: realland 2020-03-24 <NA> forecast 0.7943545
#> 33: realland 2020-03-25 <NA> forecast 0.7936359
#> 34: realland 2020-03-26 <NA> forecast 0.7934417
#> 35: realland 2020-03-27 <NA> forecast 0.7947391
#> 36: realland 2020-03-28 <NA> forecast 0.7951600
#> 37: realland 2020-03-29 <NA> forecast 0.7956642
#> 38: testland 2020-02-22 <NA> estimate 1.7147429
#> 39: testland 2020-02-23 <NA> estimate 1.7310053
#> 40: testland 2020-02-24 <NA> estimate 1.6240258
#> 41: testland 2020-02-25 <NA> estimate 1.6285029
#> 42: testland 2020-02-26 <NA> estimate 1.5682007
#> 43: testland 2020-02-27 <NA> estimate 1.5395330
#> 44: testland 2020-02-28 <NA> estimate 1.5400546
#> 45: testland 2020-02-29 <NA> estimate 1.5429743
#> 46: testland 2020-03-01 <NA> estimate 1.5252243
#> 47: testland 2020-03-02 <NA> estimate 1.3024611
#> 48: testland 2020-03-03 <NA> estimate 1.1565422
#> 49: testland 2020-03-04 <NA> estimate 1.0805704
#> 50: testland 2020-03-05 <NA> estimate 0.9972795
#> 51: testland 2020-03-06 <NA> estimate 0.9288910
#> 52: testland 2020-03-07 <NA> estimate 0.8801649
#> 53: testland 2020-03-08 <NA> estimate 0.8178085
#> 54: testland 2020-03-09 <NA> estimate based on partial data 0.8906514
#> 55: testland 2020-03-10 <NA> estimate based on partial data 0.8663872
#> 56: testland 2020-03-11 <NA> estimate based on partial data 0.8471865
#> 57: testland 2020-03-12 <NA> estimate based on partial data 0.8328953
#> 58: testland 2020-03-13 <NA> estimate based on partial data 0.8220120
#> 59: testland 2020-03-14 <NA> estimate based on partial data 0.8139539
#> 60: testland 2020-03-15 <NA> estimate based on partial data 0.8088776
#> 61: testland 2020-03-16 <NA> estimate based on partial data 0.8056314
#> 62: testland 2020-03-17 <NA> estimate based on partial data 0.8028717
#> 63: testland 2020-03-18 <NA> estimate based on partial data 0.8006768
#> 64: testland 2020-03-19 <NA> estimate based on partial data 0.7995274
#> 65: testland 2020-03-20 <NA> estimate based on partial data 0.7977083
#> 66: testland 2020-03-21 <NA> estimate based on partial data 0.7970709
#> 67: testland 2020-03-22 <NA> estimate based on partial data 0.7951755
#> 68: testland 2020-03-23 <NA> forecast 0.7948765
#> 69: testland 2020-03-24 <NA> forecast 0.7948007
#> 70: testland 2020-03-25 <NA> forecast 0.7943913
#> 71: testland 2020-03-26 <NA> forecast 0.7943493
#> 72: testland 2020-03-27 <NA> forecast 0.7952521
#> 73: testland 2020-03-28 <NA> forecast 0.7947032
#> 74: testland 2020-03-29 <NA> forecast 0.7947445
#> region date strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> 1: 1.7280049 0.11865996 1.5604003 1.6547353 1.6962867 1.7454499 1.7925617
#> 2: 1.7425339 0.11624571 1.5765875 1.6712109 1.7115500 1.7609534 1.8044120
#> 3: 1.6349725 0.10835679 1.4826967 1.5675864 1.6061875 1.6512738 1.6929554
#> 4: 1.6368867 0.10287884 1.4884446 1.5741599 1.6104569 1.6540229 1.6925516
#> 5: 1.5758309 0.09622864 1.4362036 1.5167207 1.5515201 1.5919004 1.6276439
#> 6: 1.5462531 0.09041921 1.4140124 1.4922214 1.5233991 1.5611620 1.5946361
#> 7: 1.5473367 0.08714603 1.4194434 1.4957205 1.5243835 1.5615435 1.5943894
#> 8: 1.5505993 0.08588888 1.4252014 1.4984227 1.5282467 1.5638048 1.5976376
#> 9: 1.5332540 0.08480364 1.4093045 1.4814724 1.5105747 1.5457113 1.5786585
#> 10: 1.3099489 0.07202883 1.2052295 1.2658052 1.2905173 1.3200521 1.3493884
#> 11: 1.1600302 0.05645849 1.0740518 1.1259912 1.1456076 1.1701937 1.1917534
#> 12: 1.0826469 0.04602728 1.0116889 1.0542929 1.0718747 1.0915283 1.1090385
#> 13: 0.9984467 0.03744508 0.9415747 0.9749783 0.9887097 1.0055381 1.0196170
#> 14: 0.9305400 0.03140696 0.8829154 0.9116713 0.9221275 0.9362723 0.9471596
#> 15: 0.8815722 0.02788146 0.8396409 0.8655963 0.8746782 0.8859940 0.8950964
#> 16: 0.8186196 0.02526275 0.7809182 0.8046789 0.8129408 0.8220767 0.8300163
#> 17: 0.8899522 0.02894090 0.8454041 0.8744188 0.8836305 0.8947225 0.9038973
#> 18: 0.8669966 0.02628693 0.8285660 0.8523461 0.8611314 0.8702274 0.8793126
#> 19: 0.8483989 0.02572636 0.8104517 0.8341869 0.8421393 0.8518648 0.8612795
#> 20: 0.8335138 0.02579344 0.7941614 0.8186526 0.8273452 0.8374739 0.8476137
#> 21: 0.8222127 0.02627505 0.7818127 0.8069414 0.8162179 0.8267442 0.8371725
#> 22: 0.8137939 0.02694744 0.7722181 0.7977360 0.8077236 0.8185307 0.8292681
#> 23: 0.8083628 0.02756291 0.7648959 0.7916738 0.8021946 0.8138045 0.8242120
#> 24: 0.8045700 0.02791076 0.7616174 0.7877967 0.7984455 0.8101759 0.8205653
#> 25: 0.8015389 0.02816487 0.7582579 0.7846305 0.7957501 0.8073494 0.8179734
#> 26: 0.7992028 0.02834346 0.7552003 0.7824035 0.7934478 0.8056145 0.8156390
#> 27: 0.7976756 0.02844957 0.7525102 0.7804171 0.7919734 0.8043474 0.8145634
#> 28: 0.7962195 0.02855220 0.7504343 0.7788955 0.7903450 0.8032351 0.8133668
#> 29: 0.7955449 0.02872325 0.7492947 0.7785685 0.7896069 0.8022348 0.8129410
#> 30: 0.7943671 0.02889915 0.7469139 0.7777849 0.7883693 0.8011198 0.8117526
#> 31: 0.7941467 0.02918807 0.7457501 0.7773486 0.7878730 0.8009392 0.8118347
#> 32: 0.7941441 0.02922723 0.7447469 0.7770904 0.7878663 0.8008560 0.8118973
#> 33: 0.7939374 0.02920371 0.7452740 0.7774546 0.7877812 0.8004581 0.8113906
#> 34: 0.7936817 0.02912392 0.7454994 0.7771049 0.7876445 0.8001603 0.8114946
#> 35: 0.7944603 0.02904422 0.7462887 0.7776155 0.7889105 0.8008766 0.8123416
#> 36: 0.7944472 0.02889573 0.7456629 0.7777313 0.7888681 0.8010763 0.8115925
#> 37: 0.7948368 0.02876075 0.7467142 0.7780210 0.7895176 0.8014574 0.8119871
#> 38: 1.7245497 0.11965916 1.5495735 1.6470019 1.6904554 1.7418062 1.7863558
#> 39: 1.7393635 0.11732379 1.5678749 1.6635142 1.7068090 1.7567130 1.8012760
#> 40: 1.6320314 0.10920451 1.4735103 1.5615379 1.6011012 1.6476549 1.6901298
#> 41: 1.6343665 0.10346271 1.4810787 1.5675213 1.6048145 1.6503125 1.6895366
#> 42: 1.5734779 0.09646081 1.4320509 1.5106362 1.5452739 1.5878998 1.6269560
#> 43: 1.5441680 0.09042182 1.4113919 1.4851779 1.5177858 1.5584500 1.5930653
#> 44: 1.5453992 0.08698625 1.4179064 1.4880369 1.5201339 1.5584372 1.5921707
#> 45: 1.5486438 0.08557459 1.4246365 1.4917818 1.5229939 1.5605228 1.5938355
#> 46: 1.5312575 0.08444806 1.4083493 1.4752926 1.5059339 1.5423684 1.5750470
#> 47: 1.3082647 0.07182532 1.2046486 1.2602241 1.2870172 1.3178899 1.3450904
#> 48: 1.1592656 0.05672890 1.0742574 1.1227564 1.1435592 1.1684458 1.1887241
#> 49: 1.0825968 0.04675879 1.0100542 1.0533177 1.0704014 1.0904916 1.1071569
#> 50: 0.9987799 0.03859646 0.9408482 0.9756798 0.9884776 1.0054203 1.0180425
#> 51: 0.9311706 0.03279312 0.8862139 0.9121552 0.9230943 0.9350460 0.9470658
#> 52: 0.8824239 0.02921382 0.8428340 0.8663187 0.8753960 0.8852553 0.8948147
#> 53: 0.8195657 0.02635507 0.7823847 0.8064126 0.8138207 0.8222201 0.8304535
#> 54: 0.8911631 0.02984417 0.8447911 0.8756298 0.8850473 0.8956263 0.9042848
#> 55: 0.8679300 0.02709206 0.8269062 0.8539364 0.8617187 0.8708548 0.8791543
#> 56: 0.8492358 0.02630699 0.8105038 0.8354574 0.8428416 0.8522774 0.8614067
#> 57: 0.8343438 0.02630429 0.7941711 0.8202061 0.8280548 0.8379847 0.8473145
#> 58: 0.8230512 0.02682222 0.7820901 0.8085659 0.8169389 0.8273909 0.8370582
#> 59: 0.8146369 0.02753932 0.7723213 0.7994961 0.8087527 0.8194722 0.8295666
#> 60: 0.8092171 0.02819780 0.7649635 0.7936547 0.8034760 0.8145193 0.8252684
#> 61: 0.8054317 0.02860973 0.7601828 0.7894853 0.7998661 0.8118094 0.8218667
#> 62: 0.8023932 0.02894882 0.7575361 0.7860606 0.7972684 0.8089811 0.8192363
#> 63: 0.8000354 0.02923084 0.7537172 0.7837107 0.7945412 0.8068707 0.8177721
#> 64: 0.7984747 0.02944470 0.7501021 0.7822033 0.7928278 0.8055960 0.8165282
#> 65: 0.7969731 0.02963642 0.7463790 0.7800924 0.7913659 0.8041708 0.8152109
#> 66: 0.7962443 0.02986330 0.7462844 0.7790696 0.7904101 0.8034372 0.8147476
#> 67: 0.7950027 0.03005489 0.7439444 0.7776485 0.7889539 0.8021082 0.8140882
#> 68: 0.7947137 0.03033015 0.7420148 0.7772643 0.7886723 0.8015406 0.8142176
#> 69: 0.7946416 0.03034289 0.7417258 0.7772924 0.7884708 0.8013363 0.8141485
#> 70: 0.7943656 0.03030487 0.7443505 0.7769974 0.7883285 0.8012307 0.8138374
#> 71: 0.7940447 0.03024956 0.7445155 0.7769106 0.7874809 0.8009561 0.8133867
#> 72: 0.7947653 0.03025581 0.7456896 0.7777284 0.7882149 0.8011541 0.8135513
#> 73: 0.7946992 0.03025709 0.7456268 0.7773717 0.7882394 0.8013671 0.8130999
#> 74: 0.7950475 0.03032360 0.7460373 0.7774104 0.7881072 0.8019274 0.8133769
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
#> 1: 1.9236001
#> 2: 1.9348256
#> 3: 1.8149009
#> 4: 1.8074839
#> 5: 1.7350185
#> 6: 1.6944312
#> 7: 1.6905081
#> 8: 1.6910602
#> 9: 1.6727734
#> 10: 1.4258202
#> 11: 1.2499594
#> 12: 1.1578718
#> 13: 1.0593898
#> 14: 0.9827742
#> 15: 0.9292485
#> 16: 0.8620519
#> 17: 0.9384889
#> 18: 0.9110012
#> 19: 0.8904789
#> 20: 0.8748301
#> 21: 0.8649185
#> 22: 0.8572502
#> 23: 0.8531695
#> 24: 0.8502321
#> 25: 0.8467884
#> 26: 0.8443166
#> 27: 0.8415846
#> 28: 0.8410479
#> 29: 0.8407603
#> 30: 0.8395328
#> 31: 0.8400171
#> 32: 0.8401072
#> 33: 0.8398663
#> 34: 0.8393401
#> 35: 0.8398379
#> 36: 0.8398833
#> 37: 0.8388745
#> 38: 1.9289584
#> 39: 1.9391294
#> 40: 1.8240048
#> 41: 1.8164065
#> 42: 1.7431391
#> 43: 1.7058686
#> 44: 1.7006045
#> 45: 1.7010490
#> 46: 1.6816287
#> 47: 1.4364808
#> 48: 1.2590013
#> 49: 1.1604040
#> 50: 1.0617649
#> 51: 0.9873247
#> 52: 0.9322015
#> 53: 0.8652288
#> 54: 0.9409222
#> 55: 0.9130283
#> 56: 0.8921948
#> 57: 0.8754280
#> 58: 0.8645186
#> 59: 0.8583653
#> 60: 0.8526785
#> 61: 0.8493924
#> 62: 0.8459563
#> 63: 0.8444319
#> 64: 0.8443206
#> 65: 0.8431263
#> 66: 0.8440391
#> 67: 0.8428994
#> 68: 0.8431807
#> 69: 0.8410045
#> 70: 0.8410264
#> 71: 0.8411236
#> 72: 0.8418966
#> 73: 0.8427092
#> 74: 0.8434842
#> upper_90
#>
#> $summarised_measures$growth_rate
#> region date strat type median
#> 1: realland 2020-02-22 <NA> estimate 0.1257667416
#> 2: realland 2020-02-23 <NA> estimate 0.1278821643
#> 3: realland 2020-02-24 <NA> estimate 0.1117397313
#> 4: realland 2020-02-25 <NA> estimate 0.1121793872
#> 5: realland 2020-02-26 <NA> estimate 0.1028060820
#> 6: realland 2020-02-27 <NA> estimate 0.0982036531
#> 7: realland 2020-02-28 <NA> estimate 0.0982246833
#> 8: realland 2020-02-29 <NA> estimate 0.0985958228
#> 9: realland 2020-03-01 <NA> estimate 0.0958184852
#> 10: realland 2020-03-02 <NA> estimate 0.0587471868
#> 11: realland 2020-03-03 <NA> estimate 0.0316091130
#> 12: realland 2020-03-04 <NA> estimate 0.0168174523
#> 13: realland 2020-03-05 <NA> estimate -0.0005669345
#> 14: realland 2020-03-06 <NA> estimate -0.0153773441
#> 15: realland 2020-03-07 <NA> estimate -0.0266774506
#> 16: realland 2020-03-08 <NA> estimate -0.0416340893
#> 17: realland 2020-03-09 <NA> estimate based on partial data -0.0244622389
#> 18: realland 2020-03-10 <NA> estimate based on partial data -0.0301579492
#> 19: realland 2020-03-11 <NA> estimate based on partial data -0.0346435755
#> 20: realland 2020-03-12 <NA> estimate based on partial data -0.0380943676
#> 21: realland 2020-03-13 <NA> estimate based on partial data -0.0406577915
#> 22: realland 2020-03-14 <NA> estimate based on partial data -0.0425432534
#> 23: realland 2020-03-15 <NA> estimate based on partial data -0.0437428745
#> 24: realland 2020-03-16 <NA> estimate based on partial data -0.0445127149
#> 25: realland 2020-03-17 <NA> estimate based on partial data -0.0451090737
#> 26: realland 2020-03-18 <NA> estimate based on partial data -0.0455885201
#> 27: realland 2020-03-19 <NA> estimate based on partial data -0.0458618688
#> 28: realland 2020-03-20 <NA> estimate based on partial data -0.0461686281
#> 29: realland 2020-03-21 <NA> estimate based on partial data -0.0462945402
#> 30: realland 2020-03-22 <NA> estimate based on partial data -0.0465364629
#> 31: realland 2020-03-23 <NA> forecast -0.0465693872
#> 32: realland 2020-03-24 <NA> forecast -0.0465806600
#> 33: realland 2020-03-25 <NA> forecast -0.0466873477
#> 34: realland 2020-03-26 <NA> forecast -0.0467492278
#> 35: realland 2020-03-27 <NA> forecast -0.0465785778
#> 36: realland 2020-03-28 <NA> forecast -0.0466265284
#> 37: realland 2020-03-29 <NA> forecast -0.0465203133
#> 38: testland 2020-02-22 <NA> estimate 0.1255064402
#> 39: testland 2020-02-23 <NA> estimate 0.1276603513
#> 40: testland 2020-02-24 <NA> estimate 0.1114438715
#> 41: testland 2020-02-25 <NA> estimate 0.1119594367
#> 42: testland 2020-02-26 <NA> estimate 0.1024838976
#> 43: testland 2020-02-27 <NA> estimate 0.0979140965
#> 44: testland 2020-02-28 <NA> estimate 0.0980616402
#> 45: testland 2020-02-29 <NA> estimate 0.0984139825
#> 46: testland 2020-03-01 <NA> estimate 0.0955428600
#> 47: testland 2020-03-02 <NA> estimate 0.0583302527
#> 48: testland 2020-03-03 <NA> estimate 0.0314644093
#> 49: testland 2020-03-04 <NA> estimate 0.0165769877
#> 50: testland 2020-03-05 <NA> estimate -0.0005474693
#> 51: testland 2020-03-06 <NA> estimate -0.0154014501
#> 52: testland 2020-03-07 <NA> estimate -0.0265448778
#> 53: testland 2020-03-08 <NA> estimate -0.0415154915
#> 54: testland 2020-03-09 <NA> estimate based on partial data -0.0245601654
#> 55: testland 2020-03-10 <NA> estimate based on partial data -0.0302176897
#> 56: testland 2020-03-11 <NA> estimate based on partial data -0.0346567313
#> 57: testland 2020-03-12 <NA> estimate based on partial data -0.0380821473
#> 58: testland 2020-03-13 <NA> estimate based on partial data -0.0406538774
#> 59: testland 2020-03-14 <NA> estimate based on partial data -0.0425190072
#> 60: testland 2020-03-15 <NA> estimate based on partial data -0.0436460357
#> 61: testland 2020-03-16 <NA> estimate based on partial data -0.0443964397
#> 62: testland 2020-03-17 <NA> estimate based on partial data -0.0450087171
#> 63: testland 2020-03-18 <NA> estimate based on partial data -0.0454262954
#> 64: testland 2020-03-19 <NA> estimate based on partial data -0.0457380023
#> 65: testland 2020-03-20 <NA> estimate based on partial data -0.0460260720
#> 66: testland 2020-03-21 <NA> estimate based on partial data -0.0461705739
#> 67: testland 2020-03-22 <NA> estimate based on partial data -0.0464663278
#> 68: testland 2020-03-23 <NA> forecast -0.0465327438
#> 69: testland 2020-03-24 <NA> forecast -0.0465310662
#> 70: testland 2020-03-25 <NA> forecast -0.0465967746
#> 71: testland 2020-03-26 <NA> forecast -0.0466744519
#> 72: testland 2020-03-27 <NA> forecast -0.0465288090
#> 73: testland 2020-03-28 <NA> forecast -0.0466075555
#> 74: testland 2020-03-29 <NA> forecast -0.0465917211
#> region date strat type median
#> mean sd lower_90 lower_50 lower_20
#> 1: 0.1257915796 0.005825119 0.116760587 0.122445668 0.124493451
#> 2: 0.1279967392 0.005433967 0.119535649 0.124963380 0.126746838
#> 3: 0.1119569838 0.006315592 0.102220648 0.108317884 0.110458169
#> 4: 0.1123866326 0.005751424 0.103825849 0.109111256 0.111067715
#> 5: 0.1030204503 0.006017412 0.094162972 0.099617512 0.101544760
#> 6: 0.0984596966 0.005911614 0.089844199 0.095113440 0.097004436
#> 7: 0.0987150015 0.005662821 0.090860043 0.095546547 0.097173886
#> 8: 0.0992679924 0.005593311 0.091757062 0.096214195 0.097648344
#> 9: 0.0965179299 0.005769257 0.089022929 0.093389807 0.094827023
#> 10: 0.0589754280 0.007720473 0.047647354 0.054675575 0.057189796
#> 11: 0.0314351084 0.008313318 0.017841833 0.026861688 0.029949237
#> 12: 0.0163810749 0.008194064 0.002926733 0.011843422 0.014947551
#> 13: -0.0009377540 0.008126376 -0.014497681 -0.005613762 -0.002409506
#> 14: -0.0156363738 0.007844712 -0.028569543 -0.020469765 -0.017196161
#> 15: -0.0266659077 0.007373098 -0.038642433 -0.031209663 -0.028197491
#> 16: -0.0415742451 0.007072582 -0.052759243 -0.046058454 -0.043113381
#> 17: -0.0243573794 0.006153457 -0.033696043 -0.027860845 -0.025760668
#> 18: -0.0296420757 0.005223919 -0.036637645 -0.032263798 -0.030955374
#> 19: -0.0340159972 0.004902274 -0.040701914 -0.036186952 -0.035226533
#> 20: -0.0375669382 0.004753750 -0.044079686 -0.039491041 -0.038587827
#> 21: -0.0402861237 0.004697281 -0.047153494 -0.042077549 -0.041172343
#> 22: -0.0423234479 0.004696966 -0.049339508 -0.044034062 -0.043078497
#> 23: -0.0436415952 0.004719459 -0.051113997 -0.045364775 -0.044295012
#> 24: -0.0445680605 0.004730602 -0.051781479 -0.046279547 -0.045067537
#> 25: -0.0453103211 0.004732070 -0.052906884 -0.047015427 -0.045717876
#> 26: -0.0458834757 0.004728284 -0.053506566 -0.047559630 -0.046163810
#> 27: -0.0462590718 0.004727613 -0.054020304 -0.047978303 -0.046518134
#> 28: -0.0466190779 0.004738855 -0.054479466 -0.048349352 -0.046832325
#> 29: -0.0467845147 0.004774969 -0.055129625 -0.048433422 -0.046950920
#> 30: -0.0470770159 0.004819754 -0.055753129 -0.048766651 -0.047218848
#> 31: -0.0471278399 0.004882257 -0.056083145 -0.048872105 -0.047266281
#> 32: -0.0471299945 0.004911418 -0.056247692 -0.048829341 -0.047214430
#> 33: -0.0471826980 0.004915093 -0.056655487 -0.048889260 -0.047299654
#> 34: -0.0472465954 0.004891036 -0.056426172 -0.048963973 -0.047415127
#> 35: -0.0470497346 0.004851640 -0.055648965 -0.048740567 -0.047248050
#> 36: -0.0470510525 0.004785875 -0.055454696 -0.048794568 -0.047232562
#> 37: -0.0469507592 0.004712214 -0.055277317 -0.048663281 -0.047182471
#> 38: 0.1256846949 0.006105155 0.116560449 0.122249618 0.124279787
#> 39: 0.1279443384 0.005729924 0.119679707 0.124778730 0.126516157
#> 40: 0.1118622275 0.006546213 0.102146734 0.107933528 0.110213489
#> 41: 0.1123630621 0.005997667 0.103561079 0.108960745 0.110813262
#> 42: 0.1029853807 0.006205429 0.093754846 0.099319726 0.101307010
#> 43: 0.0984495976 0.006078974 0.089627940 0.094833619 0.096836209
#> 44: 0.0987327736 0.005827254 0.090716876 0.095342858 0.097083342
#> 45: 0.0992884907 0.005735186 0.091974596 0.095937555 0.097499036
#> 46: 0.0965193774 0.005876547 0.089047037 0.093016156 0.094621068
#> 47: 0.0588596189 0.007711548 0.047503741 0.054275167 0.056862439
#> 48: 0.0313746857 0.008408365 0.018050400 0.026528247 0.029651674
#> 49: 0.0164101624 0.008363930 0.002596746 0.011801815 0.014865393
#> 50: -0.0008759248 0.008321933 -0.014891143 -0.005484841 -0.002534181
#> 51: -0.0155456289 0.008074001 -0.028640461 -0.020144855 -0.017148819
#> 52: -0.0265485841 0.007631262 -0.038525367 -0.031051484 -0.028101138
#> 53: -0.0414578822 0.007351853 -0.052675425 -0.045869000 -0.043043125
#> 54: -0.0241604306 0.006392657 -0.033277936 -0.027542832 -0.025614480
#> 55: -0.0295138002 0.005535256 -0.036629997 -0.032085699 -0.030887212
#> 56: -0.0339188063 0.005208044 -0.040402445 -0.036106133 -0.035195535
#> 57: -0.0374788380 0.005073132 -0.044150407 -0.039353491 -0.038522661
#> 58: -0.0401988776 0.005025619 -0.047047088 -0.041974581 -0.041089831
#> 59: -0.0422354087 0.005018596 -0.049048457 -0.043953396 -0.042972441
#> 60: -0.0435494832 0.005025796 -0.050515491 -0.045256320 -0.044113586
#> 61: -0.0444719398 0.005022858 -0.051992760 -0.046174860 -0.044885701
#> 62: -0.0452131036 0.005011154 -0.053058121 -0.046871055 -0.045541826
#> 63: -0.0457880421 0.004994959 -0.053917577 -0.047397554 -0.046024814
#> 64: -0.0461676672 0.004980754 -0.054463879 -0.047817149 -0.046385767
#> 65: -0.0465344395 0.004974505 -0.055029576 -0.048171336 -0.046728446
#> 66: -0.0467085134 0.004987172 -0.055278270 -0.048307115 -0.046807834
#> 67: -0.0470125126 0.005001989 -0.055937621 -0.048617737 -0.047056368
#> 68: -0.0470762213 0.005031631 -0.056054480 -0.048744553 -0.047146126
#> 69: -0.0470922371 0.005027823 -0.056236938 -0.048762022 -0.047167387
#> 70: -0.0471599938 0.005007993 -0.056470303 -0.048790355 -0.047215865
#> 71: -0.0472393410 0.004981144 -0.056212412 -0.048858275 -0.047333784
#> 72: -0.0470572433 0.004972002 -0.055840054 -0.048648202 -0.047193571
#> 73: -0.0470738718 0.004972185 -0.055716163 -0.048634095 -0.047231769
#> 74: -0.0469872489 0.004998265 -0.055480394 -0.048588604 -0.047244322
#> mean sd lower_90 lower_50 lower_20
#> upper_20 upper_50 upper_90
#> 1: 0.126974319 0.129181006 0.135125020
#> 2: 0.129016295 0.131073872 0.136825751
#> 3: 0.113193896 0.115619247 0.122048566
#> 4: 0.113391919 0.115376367 0.122041742
#> 5: 0.104089038 0.106126406 0.112941966
#> 6: 0.099391063 0.101393921 0.108299266
#> 7: 0.099314720 0.101251512 0.108351411
#> 8: 0.099676803 0.101538755 0.109191955
#> 9: 0.096879017 0.098810553 0.106359698
#> 10: 0.060266924 0.063124917 0.071144610
#> 11: 0.033278800 0.035928334 0.044093728
#> 12: 0.018343257 0.021156422 0.028618675
#> 13: 0.001133955 0.003945332 0.011326944
#> 14: -0.013516731 -0.010846177 -0.003369874
#> 15: -0.024808863 -0.022300552 -0.014773523
#> 16: -0.039824815 -0.037504539 -0.030282111
#> 17: -0.023427276 -0.021427922 -0.013597791
#> 18: -0.029338310 -0.027807864 -0.020228882
#> 19: -0.033904057 -0.032404734 -0.025244117
#> 20: -0.037444621 -0.036007528 -0.029384585
#> 21: -0.040092223 -0.038622996 -0.032273859
#> 22: -0.041964317 -0.040637534 -0.034670993
#> 23: -0.043098917 -0.041839547 -0.036206513
#> 24: -0.043858659 -0.042787576 -0.037305233
#> 25: -0.044504811 -0.043405910 -0.038266067
#> 26: -0.044949563 -0.043783173 -0.038932890
#> 27: -0.045240486 -0.044041203 -0.039638999
#> 28: -0.045580007 -0.044290954 -0.040138782
#> 29: -0.045654326 -0.044423955 -0.040161580
#> 30: -0.045976105 -0.044719186 -0.040187516
#> 31: -0.045998107 -0.044728084 -0.040434647
#> 32: -0.045928736 -0.044753046 -0.040498853
#> 33: -0.045987744 -0.044778938 -0.040473195
#> 34: -0.046030226 -0.044894465 -0.040650252
#> 35: -0.045852536 -0.044694214 -0.040576889
#> 36: -0.045915761 -0.044690509 -0.040655877
#> 37: -0.045857279 -0.044690151 -0.040563540
#> 38: 0.126667235 0.128976387 0.135803129
#> 39: 0.128711273 0.130926990 0.137441479
#> 40: 0.112763761 0.115390983 0.123067738
#> 41: 0.113069937 0.115395890 0.122571590
#> 42: 0.103755058 0.106163355 0.113956773
#> 43: 0.099081580 0.101412030 0.109249469
#> 44: 0.099151530 0.101310886 0.108950826
#> 45: 0.099580078 0.101605352 0.109403957
#> 46: 0.096737404 0.098979233 0.106905316
#> 47: 0.059927752 0.062932801 0.071813930
#> 48: 0.033215668 0.035774758 0.044761953
#> 49: 0.018328441 0.020977888 0.029202975
#> 50: 0.001109841 0.003651730 0.012035207
#> 51: -0.013720296 -0.011128168 -0.002670470
#> 52: -0.025078514 -0.022442363 -0.014121950
#> 53: -0.040020260 -0.037550004 -0.029241437
#> 54: -0.023527833 -0.021390682 -0.013127634
#> 55: -0.029501696 -0.027827931 -0.019490153
#> 56: -0.034049136 -0.032588941 -0.024925715
#> 57: -0.037543469 -0.036181137 -0.029403019
#> 58: -0.040123114 -0.038850537 -0.032735777
#> 59: -0.041966367 -0.040641479 -0.034674348
#> 60: -0.043067648 -0.041818593 -0.036347562
#> 61: -0.043826184 -0.042610434 -0.037393286
#> 62: -0.044414029 -0.043232374 -0.038184412
#> 63: -0.044875718 -0.043705700 -0.038818732
#> 64: -0.045133195 -0.044050961 -0.039466600
#> 65: -0.045431604 -0.044345883 -0.039782672
#> 66: -0.045545412 -0.044420758 -0.039948566
#> 67: -0.045862923 -0.044718085 -0.040195048
#> 68: -0.045836561 -0.044657230 -0.040145324
#> 69: -0.045840903 -0.044629348 -0.040119595
#> 70: -0.045903824 -0.044793955 -0.040373969
#> 71: -0.046009350 -0.044892403 -0.040673892
#> 72: -0.045878094 -0.044720211 -0.040515216
#> 73: -0.045965006 -0.044871416 -0.040553684
#> 74: -0.045989411 -0.044743823 -0.040590654
#> upper_20 upper_50 upper_90
#>
#> $summarised_measures$cases_by_infection
#> region date strat type median mean
#> 1: realland 2020-02-22 <NA> estimate 1190.6 1175.9
#> 2: realland 2020-02-23 <NA> estimate 1423.1 1406.7
#> 3: realland 2020-02-24 <NA> estimate 1585.3 1568.8
#> 4: realland 2020-02-25 <NA> estimate 1846.5 1829.8
#> 5: realland 2020-02-26 <NA> estimate 2065.0 2050.0
#> 6: realland 2020-02-27 <NA> estimate 2325.0 2312.5
#> 7: realland 2020-02-28 <NA> estimate 2651.6 2642.5
#> 8: realland 2020-02-29 <NA> estimate 3025.3 3021.7
#> 9: realland 2020-03-01 <NA> estimate 3406.9 3410.5
#> 10: realland 2020-03-02 <NA> estimate 3302.9 3314.7
#> 11: realland 2020-03-03 <NA> estimate 3176.4 3194.9
#> 12: realland 2020-03-04 <NA> estimate 3109.9 3134.6
#> 13: realland 2020-03-05 <NA> estimate 2946.0 2975.1
#> 14: realland 2020-03-06 <NA> estimate 2758.3 2790.6
#> 15: realland 2020-03-07 <NA> estimate 2577.8 2611.6
#> 16: realland 2020-03-08 <NA> estimate 2328.1 2361.5
#> 17: realland 2020-03-09 <NA> estimate based on partial data 2418.9 2456.9
#> 18: realland 2020-03-10 <NA> estimate based on partial data 2285.3 2322.6
#> 19: realland 2020-03-11 <NA> estimate based on partial data 2157.9 2194.3
#> 20: realland 2020-03-12 <NA> estimate based on partial data 2036.1 2072.1
#> 21: realland 2020-03-13 <NA> estimate based on partial data 1922.8 1956.4
#> 22: realland 2020-03-14 <NA> estimate based on partial data 1814.9 1846.1
#> 23: realland 2020-03-15 <NA> estimate based on partial data 1714.1 1742.6
#> 24: realland 2020-03-16 <NA> estimate based on partial data 1618.5 1644.0
#> 25: realland 2020-03-17 <NA> estimate based on partial data 1527.5 1550.3
#> 26: realland 2020-03-18 <NA> estimate based on partial data 1441.0 1461.7
#> 27: realland 2020-03-19 <NA> estimate based on partial data 1360.4 1378.2
#> 28: realland 2020-03-20 <NA> estimate based on partial data 1283.3 1299.0
#> 29: realland 2020-03-21 <NA> estimate based on partial data 1212.0 1225.0
#> 30: realland 2020-03-22 <NA> estimate based on partial data 1143.5 1154.3
#> 31: realland 2020-03-23 <NA> forecast 1079.8 1089.0
#> 32: realland 2020-03-24 <NA> forecast 1019.1 1026.9
#> 33: realland 2020-03-25 <NA> forecast 961.7 968.1
#> 34: realland 2020-03-26 <NA> forecast 907.1 912.5
#> 35: realland 2020-03-27 <NA> forecast 857.3 861.2
#> 36: realland 2020-03-28 <NA> forecast 808.9 812.2
#> 37: realland 2020-03-29 <NA> forecast 763.8 766.3
#> 38: testland 2020-02-22 <NA> estimate 1190.3 1174.5
#> 39: testland 2020-02-23 <NA> estimate 1422.9 1405.2
#> 40: testland 2020-02-24 <NA> estimate 1585.5 1567.4
#> 41: testland 2020-02-25 <NA> estimate 1847.4 1828.5
#> 42: testland 2020-02-26 <NA> estimate 2066.2 2048.8
#> 43: testland 2020-02-27 <NA> estimate 2326.6 2311.5
#> 44: testland 2020-02-28 <NA> estimate 2653.0 2641.8
#> 45: testland 2020-02-29 <NA> estimate 3027.0 3021.5
#> 46: testland 2020-03-01 <NA> estimate 3407.7 3410.7
#> 47: testland 2020-03-02 <NA> estimate 3302.8 3315.3
#> 48: testland 2020-03-03 <NA> estimate 3176.4 3195.9
#> 49: testland 2020-03-04 <NA> estimate 3109.3 3136.0
#> 50: testland 2020-03-05 <NA> estimate 2945.6 2976.7
#> 51: testland 2020-03-06 <NA> estimate 2757.9 2792.4
#> 52: testland 2020-03-07 <NA> estimate 2577.8 2613.4
#> 53: testland 2020-03-08 <NA> estimate 2327.6 2363.3
#> 54: testland 2020-03-09 <NA> estimate based on partial data 2420.0 2459.0
#> 55: testland 2020-03-10 <NA> estimate based on partial data 2286.2 2324.7
#> 56: testland 2020-03-11 <NA> estimate based on partial data 2158.7 2196.5
#> 57: testland 2020-03-12 <NA> estimate based on partial data 2038.3 2074.4
#> 58: testland 2020-03-13 <NA> estimate based on partial data 1924.6 1958.7
#> 59: testland 2020-03-14 <NA> estimate based on partial data 1816.3 1848.6
#> 60: testland 2020-03-15 <NA> estimate based on partial data 1714.5 1745.1
#> 61: testland 2020-03-16 <NA> estimate based on partial data 1618.3 1646.5
#> 62: testland 2020-03-17 <NA> estimate based on partial data 1527.1 1552.8
#> 63: testland 2020-03-18 <NA> estimate based on partial data 1440.8 1464.2
#> 64: testland 2020-03-19 <NA> estimate based on partial data 1359.8 1380.7
#> 65: testland 2020-03-20 <NA> estimate based on partial data 1283.1 1301.4
#> 66: testland 2020-03-21 <NA> estimate based on partial data 1211.7 1227.3
#> 67: testland 2020-03-22 <NA> estimate based on partial data 1143.0 1156.5
#> 68: testland 2020-03-23 <NA> forecast 1079.4 1090.9
#> 69: testland 2020-03-24 <NA> forecast 1018.9 1028.7
#> 70: testland 2020-03-25 <NA> forecast 961.4 969.7
#> 71: testland 2020-03-26 <NA> forecast 907.2 913.9
#> 72: testland 2020-03-27 <NA> forecast 857.0 862.4
#> 73: testland 2020-03-28 <NA> forecast 808.9 813.2
#> 74: testland 2020-03-29 <NA> forecast 763.6 767.1
#> region date strat type median mean
#> sd lower_90 lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 70.1 1049.3 1148.9 1180.1 1197.0 1207.0 1270.4
#> 2: 83.0 1255.6 1375.9 1410.8 1430.9 1442.5 1516.4
#> 3: 91.4 1404.0 1533.2 1572.1 1593.2 1608.0 1692.0
#> 4: 105.1 1639.5 1789.4 1831.0 1854.9 1873.8 1969.0
#> 5: 116.0 1849.5 2005.1 2048.1 2074.0 2097.0 2207.3
#> 6: 128.8 2095.1 2261.0 2307.3 2334.7 2363.7 2496.1
#> 7: 145.1 2407.8 2583.1 2631.8 2663.1 2697.0 2856.9
#> 8: 164.0 2763.2 2956.2 3006.2 3038.1 3082.7 3273.4
#> 9: 183.9 3131.8 3334.7 3388.6 3421.3 3474.7 3706.3
#> 10: 178.7 3058.8 3238.4 3288.3 3318.8 3375.5 3603.2
#> 11: 173.7 2953.6 3120.9 3163.2 3193.7 3251.2 3480.7
#> 12: 173.2 2899.8 3057.8 3097.3 3128.2 3188.5 3425.6
#> 13: 168.2 2754.5 2899.2 2933.4 2966.3 3025.9 3269.0
#> 14: 162.1 2584.4 2716.9 2745.8 2781.6 2836.0 3088.3
#> 15: 155.9 2417.8 2537.8 2565.6 2598.7 2653.6 2903.5
#> 16: 144.6 2190.6 2291.4 2315.7 2348.1 2400.3 2638.7
#> 17: 153.7 2277.2 2382.7 2406.2 2441.5 2499.0 2754.1
#> 18: 147.7 2150.9 2249.7 2273.0 2307.3 2362.7 2610.2
#> 19: 141.1 2029.4 2125.2 2146.1 2179.7 2232.0 2467.9
#> 20: 134.0 1914.1 2008.2 2025.8 2057.1 2109.4 2337.9
#> 21: 126.7 1807.7 1895.4 1912.4 1944.2 1989.4 2200.8
#> 22: 119.4 1706.8 1788.4 1805.5 1836.1 1877.7 2074.3
#> 23: 112.1 1611.5 1689.3 1705.3 1732.7 1773.2 1955.9
#> 24: 105.0 1516.5 1594.2 1609.9 1635.0 1673.7 1846.8
#> 25: 98.1 1430.1 1505.1 1519.7 1542.3 1579.8 1737.6
#> 26: 91.5 1348.5 1419.7 1434.7 1455.8 1489.2 1635.1
#> 27: 85.2 1273.3 1338.9 1354.4 1374.0 1403.2 1532.4
#> 28: 79.2 1200.1 1263.8 1277.9 1295.8 1324.0 1439.9
#> 29: 73.7 1128.7 1191.7 1206.6 1222.4 1248.1 1357.6
#> 30: 68.6 1060.8 1124.2 1138.4 1152.7 1177.0 1277.9
#> 31: 64.0 999.9 1061.6 1074.9 1088.2 1111.2 1200.4
#> 32: 59.9 942.3 1001.5 1014.9 1027.0 1048.7 1129.0
#> 33: 56.0 886.0 944.8 957.7 968.8 988.3 1065.0
#> 34: 52.5 834.6 891.7 903.4 914.1 931.7 1004.4
#> 35: 49.3 786.4 841.9 853.5 863.4 878.5 944.9
#> 36: 46.3 741.8 794.1 805.5 814.8 828.4 890.9
#> 37: 43.4 698.8 748.8 760.5 768.9 781.3 840.4
#> 38: 73.4 1040.0 1151.2 1179.7 1197.2 1206.4 1265.0
#> 39: 86.5 1251.9 1378.3 1410.9 1430.4 1442.8 1512.9
#> 40: 95.0 1399.2 1536.4 1571.7 1592.8 1607.2 1684.9
#> 41: 109.0 1638.7 1790.9 1831.2 1854.3 1872.8 1971.1
#> 42: 120.1 1844.8 2007.3 2050.3 2073.8 2096.4 2211.8
#> 43: 133.3 2092.1 2264.9 2309.3 2334.4 2361.3 2504.0
#> 44: 150.2 2403.7 2586.6 2637.0 2661.9 2695.9 2862.3
#> 45: 169.9 2758.6 2958.5 3011.8 3039.7 3078.1 3282.5
#> 46: 190.5 3125.7 3336.6 3392.3 3423.5 3472.8 3717.2
#> 47: 185.2 3045.4 3244.4 3289.6 3322.2 3373.1 3621.6
#> 48: 180.0 2944.6 3123.2 3164.1 3194.8 3250.1 3513.3
#> 49: 179.4 2904.9 3059.3 3097.6 3130.0 3189.8 3457.4
#> 50: 174.1 2762.0 2899.7 2934.7 2966.7 3027.8 3290.1
#> 51: 167.5 2593.5 2716.9 2746.9 2780.6 2837.5 3107.4
#> 52: 160.8 2430.3 2539.3 2565.2 2600.5 2653.6 2909.5
#> 53: 148.8 2194.3 2292.8 2316.3 2348.6 2402.5 2647.6
#> 54: 157.9 2279.9 2385.0 2407.0 2441.8 2499.5 2764.6
#> 55: 151.7 2155.7 2253.9 2273.4 2308.6 2363.4 2611.3
#> 56: 145.3 2032.7 2128.0 2146.7 2179.6 2231.8 2475.9
#> 57: 138.8 1920.8 2009.9 2026.7 2058.2 2108.1 2349.7
#> 58: 132.4 1808.1 1897.6 1913.6 1942.1 1986.9 2226.2
#> 59: 126.1 1704.8 1791.2 1806.3 1832.5 1877.4 2098.7
#> 60: 119.9 1609.1 1691.8 1705.8 1729.4 1771.2 1981.0
#> 61: 113.7 1518.6 1596.0 1610.3 1632.1 1672.8 1867.4
#> 62: 107.4 1434.6 1506.1 1520.1 1539.6 1576.8 1755.1
#> 63: 101.0 1352.3 1421.4 1434.8 1452.3 1487.8 1652.4
#> 64: 94.7 1276.0 1340.8 1354.5 1371.5 1403.3 1549.9
#> 65: 88.4 1202.3 1265.1 1278.0 1293.2 1323.8 1462.0
#> 66: 82.4 1132.1 1193.7 1207.1 1221.1 1247.9 1372.2
#> 67: 76.5 1065.1 1125.4 1138.9 1151.3 1176.3 1287.5
#> 68: 71.1 1003.9 1063.2 1075.6 1087.1 1110.5 1208.1
#> 69: 65.9 944.1 1002.9 1015.1 1026.2 1047.1 1138.2
#> 70: 61.1 887.7 946.0 957.9 968.1 987.5 1070.8
#> 71: 56.4 836.4 891.7 903.7 913.1 931.1 1005.3
#> 72: 52.2 788.9 841.5 853.6 862.0 878.9 946.7
#> 73: 48.1 745.9 794.5 805.5 813.1 829.0 893.4
#> 74: 44.4 703.3 749.7 760.7 767.5 781.7 841.9
#> sd lower_90 lower_50 lower_20 upper_20 upper_50 upper_90
#>
#> $summarised_measures$cases_by_report
#> region date strat type median mean
#> 1: realland 2020-02-22 <NA> estimate 160.0 244.0
#> 2: realland 2020-02-23 <NA> estimate 245.0 348.0
#> 3: realland 2020-02-24 <NA> estimate 173.0 266.9
#> 4: realland 2020-02-25 <NA> estimate 248.0 385.1
#> 5: realland 2020-02-26 <NA> estimate 238.0 379.5
#> 6: realland 2020-02-27 <NA> estimate 390.5 590.5
#> 7: realland 2020-02-28 <NA> estimate 579.0 869.2
#> 8: realland 2020-02-29 <NA> estimate 610.5 910.2
#> 9: realland 2020-03-01 <NA> estimate 793.5 1217.5
#> 10: realland 2020-03-02 <NA> estimate 559.0 888.1
#> 11: realland 2020-03-03 <NA> estimate 778.0 1176.6
#> 12: realland 2020-03-04 <NA> estimate 736.5 1137.6
#> 13: realland 2020-03-05 <NA> estimate 1053.0 1661.8
#> 14: realland 2020-03-06 <NA> estimate 1485.5 2235.1
#> 15: realland 2020-03-07 <NA> estimate 1466.5 2262.4
#> 16: realland 2020-03-08 <NA> estimate 1975.5 2922.8
#> 17: realland 2020-03-09 <NA> estimate based on partial data 1151.0 1803.7
#> 18: realland 2020-03-10 <NA> estimate based on partial data 1392.5 2176.8
#> 19: realland 2020-03-11 <NA> estimate based on partial data 1250.0 1962.5
#> 20: realland 2020-03-12 <NA> estimate based on partial data 1597.5 2455.6
#> 21: realland 2020-03-13 <NA> estimate based on partial data 2048.0 3006.0
#> 22: realland 2020-03-14 <NA> estimate based on partial data 1777.5 2676.8
#> 23: realland 2020-03-15 <NA> estimate based on partial data 2113.5 3073.6
#> 24: realland 2020-03-16 <NA> estimate based on partial data 1194.5 1925.7
#> 25: realland 2020-03-17 <NA> estimate based on partial data 1379.5 2136.3
#> 26: realland 2020-03-18 <NA> estimate based on partial data 1133.0 1762.0
#> 27: realland 2020-03-19 <NA> estimate based on partial data 1502.5 2280.7
#> 28: realland 2020-03-20 <NA> estimate based on partial data 1787.0 2626.4
#> 29: realland 2020-03-21 <NA> estimate based on partial data 1553.0 2340.4
#> 30: realland 2020-03-22 <NA> estimate based on partial data 1668.0 2533.5
#> 31: realland 2020-03-23 <NA> forecast 1020.5 1509.0
#> 32: realland 2020-03-24 <NA> forecast 1121.5 1734.8
#> 33: realland 2020-03-25 <NA> forecast 930.5 1469.2
#> 34: realland 2020-03-26 <NA> forecast 1107.5 1716.7
#> 35: realland 2020-03-27 <NA> forecast 1256.5 1973.7
#> 36: realland 2020-03-28 <NA> forecast 1101.0 1722.5
#> 37: realland 2020-03-29 <NA> forecast 1244.0 1898.3
#> 38: testland 2020-02-22 <NA> estimate 160.0 248.1
#> 39: testland 2020-02-23 <NA> estimate 229.0 349.1
#> 40: testland 2020-02-24 <NA> estimate 188.0 277.7
#> 41: testland 2020-02-25 <NA> estimate 254.0 389.9
#> 42: testland 2020-02-26 <NA> estimate 244.5 390.4
#> 43: testland 2020-02-27 <NA> estimate 400.5 597.4
#> 44: testland 2020-02-28 <NA> estimate 562.5 845.9
#> 45: testland 2020-02-29 <NA> estimate 591.5 890.4
#> 46: testland 2020-03-01 <NA> estimate 829.5 1195.4
#> 47: testland 2020-03-02 <NA> estimate 581.5 898.4
#> 48: testland 2020-03-03 <NA> estimate 765.5 1147.3
#> 49: testland 2020-03-04 <NA> estimate 734.5 1157.3
#> 50: testland 2020-03-05 <NA> estimate 1063.5 1594.7
#> 51: testland 2020-03-06 <NA> estimate 1445.5 2175.0
#> 52: testland 2020-03-07 <NA> estimate 1456.5 2218.6
#> 53: testland 2020-03-08 <NA> estimate 1866.5 2781.4
#> 54: testland 2020-03-09 <NA> estimate based on partial data 1158.5 1826.5
#> 55: testland 2020-03-10 <NA> estimate based on partial data 1494.5 2251.4
#> 56: testland 2020-03-11 <NA> estimate based on partial data 1183.5 1855.8
#> 57: testland 2020-03-12 <NA> estimate based on partial data 1711.0 2499.6
#> 58: testland 2020-03-13 <NA> estimate based on partial data 1980.0 2979.0
#> 59: testland 2020-03-14 <NA> estimate based on partial data 1882.5 2735.5
#> 60: testland 2020-03-15 <NA> estimate based on partial data 2143.0 3139.8
#> 61: testland 2020-03-16 <NA> estimate based on partial data 1245.5 1881.8
#> 62: testland 2020-03-17 <NA> estimate based on partial data 1344.5 2196.7
#> 63: testland 2020-03-18 <NA> estimate based on partial data 1130.5 1791.4
#> 64: testland 2020-03-19 <NA> estimate based on partial data 1515.5 2215.8
#> 65: testland 2020-03-20 <NA> estimate based on partial data 1733.0 2695.4
#> 66: testland 2020-03-21 <NA> estimate based on partial data 1547.0 2310.0
#> 67: testland 2020-03-22 <NA> estimate based on partial data 1785.0 2578.2
#> 68: testland 2020-03-23 <NA> forecast 1079.0 1639.7
#> 69: testland 2020-03-24 <NA> forecast 1166.5 1826.7
#> 70: testland 2020-03-25 <NA> forecast 917.0 1461.6
#> 71: testland 2020-03-26 <NA> forecast 1059.5 1662.7
#> 72: testland 2020-03-27 <NA> forecast 1293.5 2052.4
#> 73: testland 2020-03-28 <NA> forecast 1112.5 1672.4
#> 74: testland 2020-03-29 <NA> forecast 1242.0 1862.8
#> region date strat type median mean
#> sd lower_90 lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 258.9 13.0 69.0 119.0 208.4 327.0 750.0
#> 2: 352.7 19.0 101.0 173.0 316.0 484.0 1028.3
#> 3: 296.5 15.0 71.0 126.6 234.0 362.0 799.3
#> 4: 442.9 20.0 107.0 183.0 330.4 506.2 1208.0
#> 5: 441.7 17.0 102.0 178.0 315.0 498.0 1212.1
#> 6: 670.1 29.9 164.8 284.6 505.0 768.0 1838.8
#> 7: 937.5 48.9 242.8 427.6 761.0 1143.2 2763.3
#> 8: 1011.2 44.9 255.8 447.6 814.0 1193.0 2782.1
#> 9: 1368.9 67.9 357.0 608.6 1051.4 1591.8 3650.7
#> 10: 1032.0 43.9 236.0 419.6 746.8 1157.0 2792.2
#> 11: 1265.7 63.0 368.8 593.6 1033.2 1585.2 3542.2
#> 12: 1258.7 51.9 302.8 544.0 956.8 1511.0 3601.8
#> 13: 1843.6 83.9 475.5 817.2 1422.0 2166.8 5457.0
#> 14: 2368.7 122.7 619.8 1092.2 1988.0 2994.8 7018.3
#> 15: 2538.5 123.9 625.0 1104.6 1883.4 2956.2 7004.5
#> 16: 3145.3 144.9 817.5 1475.0 2580.6 3982.2 9027.5
#> 17: 2070.6 84.9 488.0 852.8 1534.2 2425.2 5475.5
#> 18: 2398.1 119.0 623.8 1059.0 1877.0 2808.8 7071.3
#> 19: 2169.5 97.9 546.0 933.2 1693.8 2560.0 6285.4
#> 20: 2722.6 127.0 640.8 1167.0 2132.0 3216.8 8069.1
#> 21: 3111.3 176.0 891.8 1515.6 2680.6 3947.0 8961.4
#> 22: 2867.5 141.0 753.0 1300.6 2364.8 3591.0 7952.0
#> 23: 3295.6 173.8 902.0 1539.6 2710.2 4136.8 9318.9
#> 24: 2305.7 102.0 482.0 906.2 1583.4 2471.5 6229.3
#> 25: 2415.5 108.0 570.5 991.6 1796.4 2842.8 6678.5
#> 26: 1921.8 90.0 468.8 824.0 1548.0 2348.0 5709.1
#> 27: 2512.2 131.0 646.0 1116.6 1934.4 2936.2 7194.5
#> 28: 2744.0 127.9 777.5 1314.2 2360.4 3492.0 8022.5
#> 29: 2635.8 112.0 647.0 1187.8 2026.6 3029.0 7423.6
#> 30: 2679.9 149.7 696.0 1223.0 2269.4 3476.2 7615.6
#> 31: 1624.6 81.0 405.0 747.8 1334.0 2007.2 4585.4
#> 32: 1883.3 90.9 478.5 824.8 1497.4 2277.0 5582.8
#> 33: 1614.9 64.9 384.0 699.0 1245.0 1979.2 4609.2
#> 34: 1902.3 102.9 498.0 843.0 1457.8 2233.0 5316.2
#> 35: 2096.8 97.8 539.0 932.6 1682.2 2662.2 6204.5
#> 36: 1895.2 99.8 477.8 825.8 1474.6 2304.5 5302.6
#> 37: 2022.6 106.9 546.5 963.0 1659.0 2463.8 6091.7
#> 38: 290.2 12.0 68.0 114.0 214.0 336.0 781.1
#> 39: 378.3 16.9 98.0 168.6 301.0 456.2 1152.0
#> 40: 308.5 12.0 76.0 131.6 242.0 363.0 891.1
#> 41: 435.0 20.9 105.0 186.0 327.4 514.0 1191.3
#> 42: 459.9 21.0 108.0 179.6 321.4 522.0 1253.5
#> 43: 655.9 26.0 162.0 279.0 522.0 793.0 1912.7
#> 44: 923.4 46.0 253.0 417.0 732.8 1107.0 2665.1
#> 45: 982.6 45.0 243.8 437.6 762.0 1148.2 2770.3
#> 46: 1260.3 70.0 367.5 611.0 1061.4 1582.5 3505.5
#> 47: 1040.0 57.0 263.8 440.6 766.4 1128.2 2820.8
#> 48: 1223.4 58.0 324.5 555.6 1048.4 1529.0 3513.1
#> 49: 1317.5 61.0 320.0 545.6 977.2 1536.2 3698.1
#> 50: 1723.8 86.9 443.5 768.6 1425.4 2088.8 4901.8
#> 51: 2381.1 133.9 617.2 1075.8 1931.4 2847.8 6717.4
#> 52: 2316.1 131.9 631.8 1116.6 1968.6 2996.0 7010.4
#> 53: 2986.8 147.8 736.2 1338.2 2452.8 3705.8 8520.9
#> 54: 2109.4 93.9 496.0 861.4 1521.2 2390.5 5856.7
#> 55: 2546.5 109.9 649.8 1068.0 1934.4 2945.8 6900.4
#> 56: 2146.8 90.9 492.8 881.2 1610.2 2461.0 5765.5
#> 57: 2710.8 118.0 711.8 1256.2 2230.0 3341.2 7638.6
#> 58: 3188.0 160.9 786.8 1433.2 2646.0 4068.8 8987.8
#> 59: 3039.8 153.0 770.8 1406.0 2432.8 3590.5 8260.2
#> 60: 3475.7 159.9 889.2 1587.2 2782.4 4153.0 9577.7
#> 61: 2123.1 107.9 535.0 908.4 1612.0 2501.0 5705.0
#> 62: 2533.3 99.9 549.8 968.6 1832.4 2880.5 7330.3
#> 63: 2149.3 85.9 473.8 848.0 1489.0 2323.8 5640.3
#> 64: 2315.6 138.8 623.5 1104.4 1950.4 2976.2 6774.2
#> 65: 3039.8 144.8 704.0 1263.6 2316.4 3613.2 8517.5
#> 66: 2546.3 106.0 634.8 1109.6 2065.4 3004.5 7172.0
#> 67: 2649.3 135.9 802.0 1336.2 2319.8 3431.5 7686.3
#> 68: 1855.7 88.9 439.8 795.2 1448.0 2133.5 5065.2
#> 69: 2051.9 93.9 506.2 853.6 1573.8 2476.2 5659.4
#> 70: 1690.9 70.9 363.2 660.2 1222.8 1932.8 4530.0
#> 71: 2029.2 99.0 460.0 805.2 1397.4 2098.2 5474.6
#> 72: 2362.7 100.9 535.8 914.0 1715.8 2788.0 6479.1
#> 73: 1829.2 81.0 462.0 806.2 1436.4 2204.0 5238.8
#> 74: 2074.2 105.9 538.0 916.6 1603.4 2430.8 5676.3
#> sd lower_90 lower_50 lower_20 upper_20 upper_50 upper_90
#>
#>
#> $reported_cases
#> date confirm region
#> 1: 2020-02-22 14 testland
#> 2: 2020-02-23 62 testland
#> 3: 2020-02-24 53 testland
#> 4: 2020-02-25 97 testland
#> 5: 2020-02-26 93 testland
#> 6: 2020-02-27 78 testland
#> 7: 2020-02-28 250 testland
#> 8: 2020-02-29 238 testland
#> 9: 2020-03-01 240 testland
#> 10: 2020-03-02 561 testland
#> 11: 2020-03-03 347 testland
#> 12: 2020-03-04 466 testland
#> 13: 2020-03-05 587 testland
#> 14: 2020-03-06 769 testland
#> 15: 2020-03-07 778 testland
#> 16: 2020-03-08 1247 testland
#> 17: 2020-03-09 1492 testland
#> 18: 2020-03-10 1797 testland
#> 19: 2020-03-11 977 testland
#> 20: 2020-03-12 2313 testland
#> 21: 2020-03-13 2651 testland
#> 22: 2020-03-14 2547 testland
#> 23: 2020-03-15 3497 testland
#> 24: 2020-03-16 2823 testland
#> 25: 2020-03-17 4000 testland
#> 26: 2020-03-18 3526 testland
#> 27: 2020-03-19 4207 testland
#> 28: 2020-03-20 5322 testland
#> 29: 2020-03-21 5986 testland
#> 30: 2020-03-22 6557 testland
#> 31: 2020-02-22 14 realland
#> 32: 2020-02-23 62 realland
#> 33: 2020-02-24 53 realland
#> 34: 2020-02-25 97 realland
#> 35: 2020-02-26 93 realland
#> 36: 2020-02-27 78 realland
#> 37: 2020-02-28 250 realland
#> 38: 2020-02-29 238 realland
#> 39: 2020-03-01 240 realland
#> 40: 2020-03-02 561 realland
#> 41: 2020-03-03 347 realland
#> 42: 2020-03-04 466 realland
#> 43: 2020-03-05 587 realland
#> 44: 2020-03-06 769 realland
#> 45: 2020-03-07 778 realland
#> 46: 2020-03-08 1247 realland
#> 47: 2020-03-09 1492 realland
#> 48: 2020-03-10 1797 realland
#> 49: 2020-03-11 977 realland
#> 50: 2020-03-12 2313 realland
#> 51: 2020-03-13 2651 realland
#> 52: 2020-03-14 2547 realland
#> 53: 2020-03-15 3497 realland
#> 54: 2020-03-16 2823 realland
#> 55: 2020-03-17 4000 realland
#> 56: 2020-03-18 3526 realland
#> 57: 2020-03-19 4207 realland
#> 58: 2020-03-20 5322 realland
#> 59: 2020-03-21 5986 realland
#> 60: 2020-03-22 6557 realland
#> date confirm region
#>
#> $high_plots
#> $high_plots$infections
#>
#> $high_plots$reports
#>
#> $high_plots$R
#>
#> $high_plots$growth_rate
#>
#>
#> $plots
#> $plots$infections
#>
#> $plots$reports
#>
#> $plots$R
#>
#> $plots$growth_rate
#>
#>
# }