library(outbreaks)
library(incidence2)
library(i2extras) We provide functions to return the peak of the incidence data (grouped or ungrouped), bootstrap from the incidence data, and estimate confidence intervals around a peak.
bootstrap()dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = gender)
#> 10 missing observations were removed.
bootstrap(x)
#> An incidence2 object: 51 x 3
#> [126 cases from days 2013-02-27 to 2013-07-10]
#> [interval: 1 day]
#> [cumulative: FALSE]
#>
#> date gender count
#> <date> <fct> <int>
#> 1 2013-02-27 m 1
#> 2 2013-03-07 m 1
#> 3 2013-03-09 f 1
#> 4 2013-03-13 f 1
#> 5 2013-03-19 f 2
#> 6 2013-03-20 f 1
#> 7 2013-03-21 m 1
#> 8 2013-03-22 f 1
#> 9 2013-03-25 m 3
#> 10 2013-03-27 f 3
#> # … with 41 more rowsfind_peak()dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = gender)
#> 10 missing observations were removed.
# peaks across each group
x %>% find_peak(regroup = FALSE)
#> # A tibble: 2 x 3
#> date gender count
#> <date> <fct> <int>
#> 1 2013-04-11 f 3
#> 2 2013-04-03 m 6
# peak without groupings
x %>% find_peak()
#> `.` is stratified by groups
#> regrouping groups before finding peaks
#> An incidence2 object: 1 x 2
#> [7 cases from days 2013-04-03 to 2013-04-03]
#> [interval: 1 day]
#> [cumulative: FALSE]
#>
#> date count
#> <date> <int>
#> 1 2013-04-03 7estimate_peak()Note that the bootstrapping approach used for estimating the peak time makes the following assumptions:
dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = province)
#> 10 missing observations were removed.
# regrouping for overall peak
x %>% regroup() %>% estimate_peak()
#> ================================================================================
#> # A tibble: 1 x 6
#> date observed_count estimated_date lower_ci upper_ci peaks
#> <date> <int> <date> <date> <date> <list>
#> 1 2013-04-03 7 2013-04-06 2013-03-29 2013-04-14 <tibble [100 ×…
# across provinces and with progress bar suppressed
x %>% estimate_peak(progress = FALSE)
#> # A tibble: 13 x 7
#> province date observed_count estimated_date lower_ci upper_ci peaks
#> <chr> <date> <int> <date> <date> <date> <lis>
#> 1 Anhui 2013-03-09 1 2013-03-26 2013-03-09 2013-04-14 <tib…
#> 2 Beijing 2013-04-11 1 2013-04-22 2013-04-11 2013-05-21 <tib…
#> 3 Fujian 2013-04-17 1 2013-04-20 2013-04-17 2013-04-29 <tib…
#> 4 Guangdo… 2013-07-27 1 2013-07-27 2013-07-27 2013-07-27 <tib…
#> 5 Hebei 2013-07-10 1 2013-07-10 2013-07-10 2013-07-10 <tib…
#> 6 Henan 2013-04-06 1 2013-04-09 2013-04-06 2013-04-17 <tib…
#> 7 Hunan 2013-04-14 1 2013-04-15 2013-04-14 2013-04-23 <tib…
#> 8 Jiangsu 2013-03-19 2 2013-03-26 2013-03-08 2013-04-17 <tib…
#> 9 Jiangxi 2013-04-15 1 2013-04-19 2013-04-15 2013-05-03 <tib…
#> 10 Shandong 2013-04-16 1 2013-04-19 2013-04-16 2013-04-27 <tib…
#> 11 Shanghai 2013-04-01 4 2013-03-30 2013-02-27 2013-04-04 <tib…
#> 12 Taiwan 2013-04-12 1 2013-04-12 2013-04-12 2013-04-12 <tib…
#> 13 Zhejiang 2013-04-06 5 2013-04-08 2013-03-31 2013-04-14 <tib…