These are a set of internally cached functions to support examples. They are exported as internal functions so that the examples can run correctly and cache their output to prevent excessive repetition of the code examples.
Usage
example_england_covid_by_age()
example_poisson_age_stratified()
example_poisson_locfit()
example_proportion_age_stratified()
example_ganyani_ip()
example_du_serial()
example_ip()
example_bpm()
example_serial()
example_poisson_rt()
example_poisson_growth_rate()
example_poisson_rt_smooth()
example_poisson_rt_2class()
example_delayed_observation()Functions
example_england_covid_by_age(): Example input format including age stratified COVID-19 data.example_poisson_age_stratified(): Example output ofpoisson_locfit_model()run on age stratified COVID-19 data.example_poisson_locfit(): Example output ofpoisson_locfit_model()run on un-stratified COVID-19 data.example_proportion_age_stratified(): Example output ofproportion_locfit_model()An England COVID-19 age stratified proportion model dataset. The proportion represented here is the positive tests in this age group, versus the positive tests in all age groups, so represents a relative growth of ne age group versus others.example_ganyani_ip(): Generation time estimate frommake_gamma_ip(). This estimate is truncated to make it compatible withEpiEstim. Take from Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, Hens N. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020 Apr;25(17):2000257. doi: 10.2807/1560-7917.ES.2020.25.17.2000257. PMID: 32372755; PMCID: PMC7201952.example_du_serial(): An undjusted serial interval between symptomsmake_resampled_ip()Z. Du, X. Xu, Y. Wu, L. Wang, B. J. Cowling, and L. A. Meyers, ‘Serial Interval of COVID-19 among Publicly Reported Confirmed Cases’, Emerg Infect Dis, vol. 26, no. 6, pp. 1341–1343, Jun. 2020, doi: 10.3201/eid2606.200357.example_ip(): Example output ofmake_gamma_ip()A test infectivity profile generated from a set of discretised gamma distributions with parameters mean 5 (95% CI 4-6) and sd 2 (95% CI 1.5-2.5).example_bpm(): Example output ofsim_branching_process()An example of the linelist output of the branching process model simulation. This is generated using theexample_ip()infectivity profile and also includes a delay to symptom onset which is a random gamma distributed quantity with mean of 6 and standard deviation of 2example_serial(): Example output ofmake_resampled_ip()A serial interval estimated from symptom onset in simulated data including negative intervals. This serial interval is resampled from the first 1000 patients in theexample_bpm()dataset for whom both infector and infectee has symptoms. These patients are generated with a symptom delay of mean 6 days and SD 2 from infection (discrete under-dispersed gamma) and an infectivity profile with mean 5 days and SD 2 as defined inexample_ip()dataset. This serial interval is relevant to the estimation of $R_t$ from symptomatic case counts in theexample_bpm()dataset but includes negative times, and cannot be used withEpiEstim.example_poisson_rt(): Example output ofsim_poisson_Rt_model()An example of the linelist output of the poisson model simulation with defined $R_t$. This is generated using theexample_ip()infectivity profileexample_poisson_growth_rate(): Example output ofsim_poisson_model()A simulation dataset determined by a step function of growth rates. This is useful for demonstrating growth rate estimators.example_poisson_rt_smooth(): Example output ofsim_poisson_Rt_model()Output of a poisson model simulation with a smooth function for $R_t$ defined asR(t) = e^(sin(t/80*pi)^4-0.25)). This is a relatively unchallenging test data set that should not pose a problem for smooth estimators.example_poisson_rt_2class(): Two class example usingsim_poisson_Rt_model()Two smooth $R_t$ based incidence timeseries one growing with an time varying Rtexp(sin(t / 80 * pi)^4 - 0.25)and the other offset by 10 days:exp(sin((t - 10) / 80 * pi)^4 - 0.25). This is a simple relative growth testexample_delayed_observation(): Example output ofsim_branching_process()withsim_apply_delay()This simulates what might be observed in an outbreak if there was on average a 5 day delay on the reporting of hospital admissions. The configuration of the outbreak is the same asexample_bpm(), but this is summary data that describes the whole history of admissions that were observed, when observed at any given time point. This is a triangular set of data where the counts are right censored by the observation time.
Examples
suppressWarnings({
example_poisson_age_stratified() %>% dplyr::glimpse()
example_ip() %>% dplyr::glimpse()
example_bpm() %>% dplyr::glimpse()
example_serial() %>% dplyr::glimpse()
example_poisson_rt() %>% dplyr::glimpse()
example_poisson_growth_rate() %>% dplyr::glimpse()
example_poisson_rt_smooth() %>% dplyr::glimpse()
example_poisson_rt_2class() %>% dplyr::glimpse()
example_ganyani_ip() %>% dplyr::glimpse()
example_du_serial() %>% dplyr::glimpse()
example_delayed_observation() %>% dplyr::glimpse()
})
#> incomplete fit locfit model - try decreasing `deg` or increasing `window`.
#> Rows: 26,790
#> Columns: 20
#> Groups: class [19]
#> $ class <fct> 00_04, 00_04, 00_04, 00_04, 00_04, 00_04, 00_04, 00_0…
#> $ time <t[day]> 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44…
#> $ incidence.fit <dbl> -1.612905, -1.803708, -1.959326, -2.082483, -2.175902…
#> $ incidence.se.fit <dbl> 1.0533915, 0.9003108, 0.7844360, 0.7012956, 0.6463539…
#> $ incidence.0.025 <dbl> 0.02528574, 0.02820419, 0.03029440, 0.03152429, 0.031…
#> $ incidence.0.05 <dbl> 0.03523977, 0.03745604, 0.03878940, 0.03932042, 0.039…
#> $ incidence.0.25 <dbl> 0.09793934, 0.08972926, 0.08304107, 0.07765344, 0.073…
#> $ incidence.0.5 <dbl> 0.19930774, 0.16468709, 0.14095339, 0.12462043, 0.113…
#> $ incidence.0.75 <dbl> 0.40559365, 0.30226304, 0.23925342, 0.19999437, 0.175…
#> $ incidence.0.95 <dbl> 1.1272371, 0.7240980, 0.5121982, 0.3949666, 0.3286558…
#> $ incidence.0.975 <dbl> 1.5709871, 0.9616244, 0.6558260, 0.4926439, 0.4028982…
#> $ growth.fit <dbl> -0.2093030305, -0.1855586041, -0.1593989278, -0.13168…
#> $ growth.se.fit <dbl> 0.22418245, 0.20710820, 0.19030250, 0.17387303, 0.157…
#> $ growth.0.025 <dbl> -0.64869255, -0.59148321, -0.53238498, -0.47246827, -…
#> $ growth.0.05 <dbl> -0.57805034, -0.52622127, -0.47241869, -0.41767908, -…
#> $ growth.0.25 <dbl> -0.360511792, -0.325250959, -0.287756014, -0.24895896…
#> $ growth.0.5 <dbl> -0.2093030305, -0.1855586041, -0.1593989278, -0.13168…
#> $ growth.0.75 <dbl> -0.058094269, -0.045866249, -0.031041841, -0.01440780…
#> $ growth.0.95 <dbl> 0.15944428, 0.15510406, 0.15362083, 0.15431230, 0.156…
#> $ growth.0.975 <dbl> 0.23008649, 0.22036600, 0.21358712, 0.20910150, 0.206…
#> Rows: 1,800
#> Columns: 5
#> Groups: boot [100]
#> $ tau <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ a0 <dbl> 0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.…
#> $ a1 <dbl> 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11…
#> $ probability <dbl> 0.000000e+00, 7.677533e-03, 9.291725e-02, 2.043664e-01, 2.…
#> $ boot <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2…
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#> complete
#> Rows: 36,813
#> Columns: 8
#> $ time <t[day]> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,…
#> $ generation_interval <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ infector <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ generation <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ symptom_onset <lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE,…
#> $ symptom_onset_delay <dbl> NA, NA, 6, 6, 5, 5, NA, 6, NA, 11, 6, NA, 5, 8, NA…
#> $ symptom_onset_time <t[day]> NA, NA, 6, 6, 5, 5, NA, 6, NA, 11, 6, NA, 5, 8,…
#> Rows: 2,394
#> Columns: 5
#> Groups: boot [100]
#> $ tau <dbl> -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, …
#> $ a0 <dbl> -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, …
#> $ a1 <dbl> -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3…
#> $ probability <dbl> 9.138864e-06, 2.057870e-03, 1.033504e-03, 5.130966e-03, 7.…
#> $ boot <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> Rows: 81
#> Columns: 6
#> Groups: statistic [1]
#> $ time <t[day]> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ rt <dbl> 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, …
#> $ imports <dbl> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ rate <dbl> 30.0000000, 0.7045695, 5.7984339, 12.9399338, 17.4968560, 20…
#> $ count <int> 27, 0, 5, 12, 16, 15, 20, 30, 32, 43, 53, 62, 88, 90, 112, 1…
#> $ statistic <chr> "infections", "infections", "infections", "infections", "inf…
#> Rows: 105
#> Columns: 6
#> Groups: statistic [1]
#> $ time <t[day]> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ growth <dbl> 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, …
#> $ imports <dbl> 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ rate <dbl> 100.0000, 110.5171, 122.1403, 134.9859, 149.1825, 164.8721, …
#> $ count <int> 94, 93, 131, 136, 153, 157, 188, 196, 223, 247, 268, 320, 32…
#> $ statistic <chr> "infections", "infections", "infections", "infections", "inf…
#> Rows: 161
#> Columns: 6
#> Groups: statistic [1]
#> $ time <t[day]> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ rt <dbl> 0.7788008, 0.7788026, 0.7788303, 0.7789494, 0.7792673, 0.779…
#> $ imports <dbl> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ rate <dbl> 30.0000000, 0.2194882, 1.8028493, 3.9734539, 5.0843677, 5.01…
#> $ count <int> 29, 0, 0, 6, 3, 9, 5, 3, 4, 6, 10, 0, 1, 1, 2, 5, 2, 2, 1, 3…
#> $ statistic <chr> "infections", "infections", "infections", "infections", "inf…
#> Rows: 322
#> Columns: 8
#> Groups: class [2]
#> $ time <t[day]> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ rt <dbl> 0.7788008, 0.7788026, 0.7788303, 0.7789494, 0.7792673, 0.779…
#> $ imports <dbl> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ rate <dbl> 30.0000000, 0.2194882, 1.8028493, 3.9734539, 5.0843677, 5.01…
#> $ count <int> 33, 0, 0, 1, 4, 5, 5, 3, 3, 3, 2, 7, 5, 1, 3, 4, 3, 3, 4, 4,…
#> $ statistic <chr> "infections", "infections", "infections", "infections", "inf…
#> $ class <fct> one, one, one, one, one, one, one, one, one, one, one, one, …
#> $ denom <int> 64, 0, 2, 9, 9, 10, 15, 5, 4, 8, 3, 14, 10, 3, 9, 5, 6, 7, 1…
#> Rows: 2,400
#> Columns: 5
#> Groups: boot [100]
#> $ tau <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ a0 <dbl> 0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.…
#> $ a1 <dbl> 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11…
#> $ probability <dbl> 0.000000e+00, 3.213194e-04, 2.011007e-02, 1.168491e-01, 2.…
#> $ boot <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> Rows: 2,528
#> Columns: 5
#> Groups: boot [100]
#> $ tau <dbl> -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, …
#> $ a0 <dbl> -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.…
#> $ a1 <dbl> -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5…
#> $ probability <dbl> 0.007822933, 0.003372250, 0.014498957, 0.014498957, 0.0567…
#> $ boot <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
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#> complete
#> Rows: 3,321
#> Columns: 4
#> Groups: obs_time, statistic [81]
#> $ statistic <chr> "admitted", "admitted", "admitted", "admitted", "admitted", …
#> $ obs_time <t[day]> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ time <t[day]> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ count <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …