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Evaluates Rt over two fixed time points with Rt assumed to vary based on a random walk in-between the two time points and after a period of time.
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| library(EpiNow2) | |
| library(data.table) | |
| # Get example case counts | |
| reported_cases <- EpiNow2::example_confirmed[1:60] | |
| # Add a dummy breakpoint (used only when optionally estimating breakpoints) | |
| reported_cases <- reported_cases[, breakpoint := data.table::fifelse((date >= as.Date("2020-03-16") & date <= as.Date("2020-03-29")) | date >= as.Date("2020-04-11"), | |
| 1, 0)] | |
| # Set up example generation time | |
| generation_time <- list(mean = EpiNow2::covid_generation_times[1, ]$mean, | |
| mean_sd = EpiNow2::covid_generation_times[1, ]$mean_sd, | |
| sd = EpiNow2::covid_generation_times[1, ]$sd, | |
| sd_sd = EpiNow2::covid_generation_times[1, ]$sd_sd, | |
| max = 30) | |
| # Set delays between infection and case report | |
| # (any number of delays can be specifed here) | |
| incubation_period <- list(mean = EpiNow2::covid_incubation_period[1, ]$mean, | |
| mean_sd = EpiNow2::covid_incubation_period[1, ]$mean_sd, | |
| sd = EpiNow2::covid_incubation_period[1, ]$sd, | |
| sd_sd = EpiNow2::covid_incubation_period[1, ]$sd_sd, | |
| max = 30) | |
| reporting_delay <- list(mean = log(5), | |
| mean_sd = log(2), | |
| sd = log(2), | |
| sd_sd = log(1.5), | |
| max = 30) | |
| # Run model with default settings | |
| def <- estimate_infections(reported_cases, family = "negbin", | |
| generation_time = generation_time, | |
| delays = list(incubation_period, reporting_delay), | |
| samples = 1000, warmup = 200, cores = 4, | |
| horizon = 0, chains = 4, estimate_rt = TRUE, | |
| verbose = TRUE, return_fit = TRUE) | |
| # Plot output | |
| plots <- report_plots(summarised_estimates = def$summarised, | |
| reported = reported_cases) | |
| plots$summary | |
| # Run model with breakpoints but otherwise static Rt | |
| # This formulation may increase the apparent effect of the breakpoint but needs to be tested using | |
| # model fit criteria (i.e LFO). | |
| fbkp <- estimate_infections(reported_cases, family = "negbin", | |
| generation_time = generation_time, | |
| delays = list(incubation_period, reporting_delay), | |
| samples = 1000, warmup = 200, cores = 4, | |
| chains = 4, estimate_breakpoints = TRUE, fixed = TRUE, horizon = 0, | |
| verbose = TRUE, return_fit = TRUE) | |
| # Plot output | |
| plots <- report_plots(summarised_estimates = fbkp$summarised, | |
| reported = reported_cases) | |
| plots$summary | |
| # Pull out breakpoint summary | |
| fbkp$summarised[variable == "breakpoints"] | |
| # Pull out R summary | |
| fbkp$summarised[variable == "R"] |
Breakpoints
> fbkp$summarised[variable == "breakpoints"]
date variable strat type bottom top lower upper median mean sd
1: <NA> breakpoints 1 <NA> 0.7184323 0.9448626 0.7542409 0.8546214 0.8229805 0.8284250 0.07134650
2: <NA> breakpoints 2 <NA> 0.7299843 0.9809953 0.7874522 0.8960190 0.8611210 0.8616640 0.07796272
3: <NA> breakpoints 3 <NA> 0.7671999 1.0261384 0.8150642 0.9189434 0.8848340 0.8896843 0.07939538
4: <NA> breakpoints 4 <NA> 0.7778229 1.0612418 0.8363687 0.9461817 0.9111816 0.9162515 0.08657045
5: <NA> breakpoints 5 <NA> 0.7871807 1.0858288 0.8780703 1.0025742 0.9318813 0.9398570 0.09230524
6: <NA> breakpoints 6 <NA> 0.7961855 1.1002891 0.8795017 1.0014766 0.9501674 0.9545234 0.09397374
7: <NA> breakpoints 7 <NA> 0.8145902 1.0933885 0.9000130 1.0101090 0.9674788 0.9712042 0.08529753
8: <NA> breakpoints 8 <NA> 0.8349446 1.1198205 0.9327519 1.0489880 0.9798645 0.9805619 0.08870146
9: <NA> breakpoints 9 <NA> 0.8482785 1.1476188 0.9488036 1.0766093 1.0049255 1.0040295 0.09407015
10: <NA> breakpoints 10 <NA> 0.8521385 1.1524926 0.9266578 1.0473304 1.0060516 1.0123790 0.09315835
11: <NA> breakpoints 11 <NA> 0.8639003 1.1735322 0.9431898 1.0689608 1.0108137 1.0181225 0.09710003
12: <NA> breakpoints 12 <NA> 0.8821359 1.1731399 0.9710531 1.0849796 1.0200879 1.0242674 0.09143071
13: <NA> breakpoints 13 <NA> 0.8834980 1.1755286 0.9467024 1.0628853 1.0231593 1.0282019 0.09146208
14: <NA> breakpoints 14 <NA> 0.8813254 1.1873960 0.9776351 1.0875547 1.0321665 1.0367937 0.09238645
15: <NA> breakpoints 15 <NA> 0.8489028 1.1608301 0.9215926 1.0447059 0.9899859 0.9986267 0.09445889
Rt estimates:
> fbkp$summarised[variable == "R"]
date variable strat type bottom top lower upper median mean sd
1: 2020-02-22 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
2: 2020-02-23 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
3: 2020-02-24 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
4: 2020-02-25 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
5: 2020-02-26 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
6: 2020-02-27 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
7: 2020-02-28 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
8: 2020-02-29 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
9: 2020-03-01 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
10: 2020-03-02 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
11: 2020-03-03 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
12: 2020-03-04 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
13: 2020-03-05 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
14: 2020-03-06 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
15: 2020-03-07 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
16: 2020-03-08 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
17: 2020-03-09 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
18: 2020-03-10 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
19: 2020-03-11 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
20: 2020-03-12 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
21: 2020-03-13 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
22: 2020-03-14 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
23: 2020-03-15 R <NA> estimate 1.6229954 1.7049730 1.6582797 1.6922513 1.6703114 1.6691387 0.02523126
24: 2020-03-16 R <NA> estimate 1.1913740 1.5491442 1.2900082 1.4491832 1.3755092 1.3821003 0.11295550
25: 2020-03-17 R <NA> estimate 0.9985109 1.3554728 1.1033569 1.2562641 1.1808398 1.1873777 0.11227383
26: 2020-03-18 R <NA> estimate 0.8653293 1.2376494 0.9591665 1.1075643 1.0457497 1.0536002 0.11332290
27: 2020-03-19 R <NA> estimate 0.7851320 1.1359600 0.8797646 1.0202230 0.9550124 0.9615575 0.10753577
28: 2020-03-20 R <NA> estimate 0.7412762 1.0630038 0.8313083 0.9617075 0.8937357 0.8993709 0.10041219
29: 2020-03-21 R <NA> estimate 0.7038559 1.0126346 0.7803446 0.9039182 0.8438652 0.8544923 0.09747694
30: 2020-03-22 R <NA> estimate 0.6764468 0.9748821 0.7549331 0.8769790 0.8189115 0.8265666 0.09244133
31: 2020-03-23 R <NA> estimate 0.6645458 0.9670163 0.7484348 0.8751893 0.7997669 0.8076478 0.09425787
32: 2020-03-24 R <NA> estimate 0.6546243 0.9565019 0.7250452 0.8446799 0.7999754 0.8072158 0.09307308
33: 2020-03-25 R <NA> estimate 0.6839714 0.9612741 0.7524730 0.8625964 0.8062774 0.8132966 0.08997928
34: 2020-03-26 R <NA> estimate 0.6929421 0.9728689 0.7609478 0.8816302 0.8208108 0.8241912 0.08962846
35: 2020-03-27 R <NA> estimate 0.7090285 0.9679610 0.7881070 0.8933225 0.8378399 0.8398763 0.08198008
36: 2020-03-28 R <NA> estimate 0.7357354 0.9684348 0.8070636 0.9032873 0.8582898 0.8591889 0.07252142
37: 2020-03-29 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
38: 2020-03-30 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
39: 2020-03-31 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
40: 2020-04-01 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
41: 2020-04-02 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
42: 2020-04-03 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
43: 2020-04-04 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
44: 2020-04-05 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
45: 2020-04-06 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
46: 2020-04-07 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
47: 2020-04-08 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
48: 2020-04-09 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
49: 2020-04-10 R <NA> estimate 0.8125842 0.9499249 0.8582622 0.9095175 0.8848453 0.8851085 0.04135362
50: 2020-04-11 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
51: 2020-04-12 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
52: 2020-04-13 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
53: 2020-04-14 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
54: 2020-04-15 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
55: 2020-04-16 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
56: 2020-04-17 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
57: 2020-04-18 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
58: 2020-04-19 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
59: 2020-04-20 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
60: 2020-04-21 R <NA> estimate based on partial data 0.7534634 1.0088976 0.8149202 0.9228872 0.8799057 0.8825871 0.07944632
date variable strat type bottom top lower upper median mean sd
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Default smooth variation over time
Fixed, smooth intervention period, fixed (with a breakpoint after intervention defined as over)